Compositional Verification
of Multi-Agent Systems:
a Formal Analysis of
Pro-activeness and Reactiveness
Catholijn M. Jonker, Jan Treur
Vrije Universiteit Amsterdam
Department of Mathematics and Computer Science, Artificial Intelligence Group De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands
URL: http://www.cs.vu.nl, Email: {jonker,treur}@cs.vu.nl
Abstract
A compositional method is presented for the verification of multi-agent
systems. The advantages of the method are the well-structuredness of the
proofs and the reusability of parts of these proofs in relation to reuse of
components. The method is illustrated for an example multi-agent system,
consisting of co-operative information gathering agents. This application
of the verification method results in a formal analysis of pro-activeness and
reactiveness of agents.
1 Introduction
When designing multi-agent systems, it is often hard to guarantee that the specification of a system that has been designed actually fulfils the needs, i.e., whether it satisfies the design requirements. Especially for critical applications, for example in real-time domains, there is a need to prove that the designed system will have certain properties under certain conditions (assumptions). While developing a proof of such properties, the assumptions that define the bounds within which the system will function properly are generated. For nontrivial examples, verification can be a very complex process, both in the conceptual and computational sense. For these reasons, it is a recent trend in the literature on verification in general to study the use of compositionality and abstraction to structure the process of verification; for example, see (Abadi and Lamport, 1993; Hooman, 1994; Dams, Gerth and Kelb, 1996).
The development of structured modelling frameworks and principled design methods tuned to the specific area of multi-agent systems is currently underway; e.g., (Brazier, Dunin-Keplicz, Jennings and Treur, 1995; Fisher and Wooldridge, 1997; Kinny, Georgeff and Rao, 1996). As part of any mature multi-agent system design method, a verification approach is required. For example, in (Fisher and Wooldridge, 1997) verification is addressed within a temporal belief logic. This verification method does not exploit compositionality within the agents. In the current paper, in Section 3, a compositional verification method for multi-agent systems is introduced. Roughly spoken, the requirements of the whole system are formally verified by deriving them from assumptions that themselves are properties of agents, which in their turn may be derived from assumptions on sub-components of agents, and so on.
The compositional verification method introduced here is illustrated for an example multi-agent system, consisting of two cooperative information gathering agents and the world. For this example multi-agent system, requirements are formulated (both the required static and dynamic properties), including variants of pro-activeness and reactiveness. These requirements are formalised in terms of temporal semantics. It is shown how they can be derived from properties of agents and how these agent properties in turn can be derived from properties of the agent components. A compositional system specification is introduced in Section 4. The system specification defines how the system is composed of the two agents and the world and how each agent is composed of four agent components: for own process control, world interaction management, agent interaction management, and an agent specific task (which in this case is classification of objects in the world). The compositional specification itself is expressed in the modelling framework DESIRE, shortly introduced in Section 2. The application of the compositional verification method to the example multi-agent system is presented in Section 5 for the top level of the composition. More details on the lower levels can be found in Sections 6 and 7.
2 Compositional Modelling of Multi-Agent Systems
The example task model described in this paper is specified within the compositional modelling framework DESIRE for multi-agent systems (framework for DEsign and Specification of Interacting REasoning components; cf. (Langevelde, Philipsen and Treur, 1992; Brazier, Dunin-Keplicz, Jennings, Treur, 1995)). In DESIRE, a design consist of knowledge of the following three types:
? process composition,
? knowledge composition,
? the relation between process composition and knowledge composition. These three types of knowledge are discussed in more detail below.
2.1 Process Composition
Process composition identifies the relevant processes at different levels of (process) abstraction, and describes how a process can be defined in terms of lower level processes.
2.1.1 Processes at Different Levels of Abstraction
Processes can be described at different levels of abstraction; for example, the process of the multi-agent system as a whole, processes defined by individual agents and the external world, and processes defined by task-related components of individual agents.
Specification of a Process
The identified processes are modelled as components. For each process the types of information required as input and resulting as output are identified as well. This is modelled as input and output interfaces of the components.
Specification of Process Abstraction Levels
The identified levels of process abstraction are modelled as abstraction/specialisation relations between components at adjacent levels of abstraction: components may be
composed of other components or they may be primitive. Primitive components may be either reasoning components (for example based on a knowledge base), or, alternatively, components capable of performing tasks such as calculation, information retrieval, optimisation, et cetera.
The identification of processes at different abstraction levels results in specification of components that can be used as building blocks, and of a specification of the sub-component relation, defining which components are a sub-component of a which other component. The distinction of different process abstraction levels results in process hiding.
2.1.2 Composition of Processes
The way in which processes at one level of abstraction are composed of processes at the adjacent lower abstraction level is called composition. This composition of processes is described by the possibilities for information exchange between processes (static view on the composition), and task control knowledge used to control processes and information exchange (dynamic view on the composition). Information Exchange
Knowledge of information exchange defines which types of information can be transferred between components and the information links by which this can be achieved. Two types of information links are distinguished: private information links and mediating information links. For a given parent component, a private information link relates output of one of its components to input of another, by specifying which truth value of a specific output atom is linked with which truth value of a specific input atom. Atoms can be renamed: each component can be specified in its own language, independent of other components. In a similar manner mediating information links transfer information from the input interface of the parent component to the input interface of one of its components, or from the output interface of one of its components to the output interface of the parent component itself. Mediating links specify the relation between the information at two adjacent abstraction levels in the process composition.
Task Control Knowledge
Components may be activated sequentially or they may be continually capable of processing new input as soon as it arrives (awake). The same holds for information links: information links may be explicitly activated or they may be awake. Task control knowledge specifies under which conditions which components and information links are active (or made awake). Evaluation criteria, expressed in terms of the evaluation of the results (success or failure), provide a means to guide further processing.
2.2 Knowledge Composition
Knowledge composition identifies the knowledge structures at different levels of (knowledge) abstraction, and describes how a knowledge structure can be defined in terms of lower level knowledge structures. The knowledge abstraction levels may correspond to the process abstraction levels, but this is often not the case.
2.2.1 Knowledge Structures at Different Abstraction Levels
The two main structures used as building blocks to model knowledge are: information types and knowledge bases. Knowledge structures can be identified and described at different levels of abstraction. The resulting levels of knowledge abstraction can be distinguished for both information types and knowledge bases.
Information Types
An information type defines an ontology (lexicon, vocabulary) to describe objects or terms, their sorts, and the relations or functions that can be defined on these objects. Information types can logically be represented as signatures in order-sorted predicate logic.
Knowledge Bases
A knowledge base defines a part of the knowledge that is used in one or more of the processes. Knowledge is represented logically by rules in order-sorted predicate logic. Knowledge bases use ontologies defined in information types. Which information types are used in a knowledge base defines a relation between information types and knowledge bases.
2.2.2 Composition of Knowledge Structures
Information types can be composed of more specific information types, following the principle of compositionality discussed above. Similarly, knowledge bases can be composed of more specific knowledge bases. The compositional structure is based on the different levels of knowledge abstraction that are distinguished, and results in information and knowledge hiding.
2.3 Relation Between Process and Knowledge Composition
Each process in a process composition uses knowledge structures. Which knowledge structures are used for which processes is defined by the relation between process composition and knowledge composition.
The semantics of the modelling language are based on temporal logic (cf., Brazier, Treur, Wijngaards and Willems, 1996). Design is supported by graphical tools within the DESIRE software environment. Translation into an operational system is straightforward; the software environment includes implementation generators with which specifications can be translated into executable code. DESIRE has been successfully applied to design both single agent and multi-agent systems.
3 Compositional Verification
The purpose of verification is to prove that, under a certain set of assumptions, a system will adhere to a certain set of properties, for example the design requirements. In our approach, this is done by a mathematical proof (i.e., a proof in the form mathematicians are accustomed to do) that the specification of the system together with the assumptions implies the properties that it needs to fulfil. In this sense verification leads to a formal analysis of relations between properties and assumptions.
3.1 The Compositional Verification Method
A compositional multi-agent system can be viewed at different levels of abstraction. Viewed from the top level, denoted by L0, the complete system is one component S, with interfaces, whereas internal information and processes are hidden (information and process hiding). At the next lower level of abstraction, the system component S can be viewed as a composition of agents and the world, information links between them, and task control. Each agent A is composed of its sub-components, and so on. The compositional verification method takes this compositional structure into account. For composed components two types of properties are recognised: behavioural and environmental properties. A behavioural property is a property on the output of the component. Behavioural properties can be conditional, or unconditional. A behavioural property is conditional if the statements about the output of the component hold under the assumption that some specific conditions hold for its input. For example, a conditional behavioural property of a diagnostic agent could be conditional conclusion correctness of an agent(i.e., if the observation information needed for diagnosis, which is input of the agent, is correct, then all diagnostic output of the agent is correct), whereas the corresponding unconditional property would be conclusion correctness of an agent (i.e., all diagnostic output of the agent is correct). An environmental property is a property on the input of the component (possibly referring to certain conditions on the output).
The primitive components can be verified using more traditional verification methods such as described in (Treur and Willems, 1994; Leemans, Treur and Willems, 1995). Verification of a composed component is done using properties of the sub-components it embeds and the task control knowledge, and environmental properties of the component (depending on the rest of the system, including the world). This introduces a form of compositionality in the verification process: given a set of environmental properties the proof that a certain component adheres to a set of behavioural properties depends on the (assumed) properties of its sub-components, properties of the interactions between those sub-components, and the manner in which they are controlled. The assumptions under which the component functions properly, are the properties to be proven for its sub-components. This implies that properties at different levels of abstraction are involved in the verification process.
Often these properties are not given at the start of the verification process. Actually, the process of verification has two main aims:
? to find the properties
? given the properties, to prove the properties
The verification proofs that connect one abstraction level with the other are compositional in the following manner: any proof relating level i to level i+1 can be combined with any proof relating level i-1 to level i, as long as the same properties at level i are involved. This means, for example, that the whole compositional structure beneath level i can be replaced by a completely different design as long as the same properties at level i are achieved. After such a modification the proof from level i to level i+1 can be reused; only the proof from level i-1 to level i has to be adapted. In this sense the verification method supports reuse of verification proofs.
The compositional verification method can be formulated in more detail as follows:
A. Verifying one Abstraction Level Against the Other
For each abstraction level the following procedure for verification is followed:
1.Determine which properties are of interest (for the higher level).
2.Determine which assumptions (at the lower level) are needed to guarantee these
properties, and which environment properties.
3.Prove the properties on the basis of these assumptions, and the environment
properties.
B. Verifying a Primitive Component
For primitive knowledge-based components a number of techniques exist in literature, see for example (Treur, Willems 1994; Leemans, Treur, Willems 1995). For primitive non-knowledge-based components, such as databases, or neural networks, or optimisation algorithms, verification techniques can be used that are especially tuned for that type of component.
C. The Overall Verification Process
To verify the complete system
1.Determine the properties that are desired for the whole system.
2.Apply the above procedure A iteratively until primitive components are reached.
In the iteration the desired properties of abstraction level L i are either:
? those determined in step A1, if i = 0, or
? the assumptions made for the higher level L i-1, if i > 0
3.Verify the primitive components according to B.
The results of verification are:
? Properties and assumptions at the different abstraction levels.
? The logical relations between the properties of different abstraction levels.
Notes:
? both static and dynamic properties and connections between them are covered.
? reuse of verification results is supported (refining an existed verified compositional model by further decomposition, leads to a verification of the
refined system in which the verification structure of the original system can
be reused).
? process and information hiding limits the complexity of the verification per abstraction level.
? a requirement to apply the compositional verification method described above is the availability of an explicit specification of how the system description
at an abstraction level L i is composed from the descriptions at the lower
abstraction level L i+1; the compositional modelling framework DESIRE is an
instance of a modelling framework that fulfils this requirement.
? in principle alternative (e.g., bottom-up or mixed) procedures can be formulated as well.
3.2 Semantics Behind the Compositional Verification Method
In principle, verification is always relative to semantics of the system descriptions that are verified. For the compositional verification method, these semantics are based on compositional information states which evolve over time. In this subsection a brief overview of these assumed semantics is given.
An information state M of a component D is an assignment of truth values {true, false, unknown} to the set of ground atoms that play a role within D. The compositional
structure of D is reflected in the structure of the information state. A formal definition can be found in (Brazier, Treur, Wijngaards and Willems, 1996; Brazier, Eck and Treur, 1996). The set of all possible information states of D is denoted by IS(D).
A trace M of a component D is a sequence of information states (M t)t N in IS(D). The set of all traces is denoted by IS(D)N, or Traces(D). Given a trace M of component D, the information state of the input interface of component C at time point t of the component D is denoted by state D(M , t, input(C)), where C is either D or a sub-component of D. Analogously, state D(M ,t, output(C)), denotes the information state of the output interface of component C at time point t of the component D. Given a trace M of component D, the task control information state of component C at time point t of the component D is denoted by state C(M , t, tc(C)), where C is either D or a sub-component of D.
To connect neighbouring levels of abstraction in a verification proof for a DESIRE specification, the following elements can be used:
? the assumptions of the sub-components specified within component D
? the interactions between the sub-components of D and/or the interfaces of D ? the input / output information states of the sub-components of D
? the task control information states of the sub-components of D
? the information states of component D
? the task control information states of component D
4 The Example Multi-Agent Model
The example multi-agent model is composed of three components: two agents A and B and a component W representing the external world, see Figure 1. Each of the agents is able to acquire partial information about the external world (by observation). Each agent’s own observations are insufficient to draw conclusions of a desired type, but the combined information of both agents is sufficient. Therefore communication is required to be able to draw conclusions. The agents can communicate their own observation results and requests for observation information of the other agent. This by itself not unrealistic situation is simplified to the following materialised form. The world situation consists of an object that has to be classified. One agent can only observe the bottom view of the object, the other agent the side view. By exchanging and combining observation information they are able to classify the object.
Fig. 1. The example Multi-Agent System Communication from the agent A to B takes place in the following manner:? the agent A generates at its output interface a statement of the form:
to_be_communicated_to(
? the information is transferred to B; thereby it translated into
communicated_by(
In the example
Interaction between an agent A and the world takes place as follows:
? the agent A generates at its output interface a statement of the form:
to_be_observed(
? the information is transferred to W; thereby it is translated into
to_be_observed_by(
? the world W generates at its output interface a statement of the form:
observation_result_for(
? the information is transferred to A; thereby it is translated into
observation_result(
Part of the output of an agent are conclusions about the classification of the object of the form object_type(s); these are transferred to the output of the system.
To be able to perform its tasks, each agent is composed of four components, see Figure 2: three for generic agent tasks (world interaction management, agent interaction management, own proces control), and one for an agent specific task (object classification).
Fig. 2. Composition of an agent
object classification(agent specific task)
This component is able to draw a conclusion if it has input on the two views on the object. The component can reason on the basis of the world knowledge represented in the table depicted in Table 1:
Table 1. World knowledge
world interaction management
This component reasons about the manner in which the agent interacts with the world. Here it is decided under which conditions which observations are to be performed in the world.
agent interaction management
This component reasons about the agent’s communication with other agents. In this component it is determined when to request which information from the other agent. Another task is to determine when to provide which observation information to the other agent and what to do with the world information received from the other agent. own process control
This component defines the agent’s own characteristics or attitudes. Information on these attitudes can be transferred to other components, to influence the reasoning that takes place there. The agents can differ in their attitudes towards observation and communication: an agent may or may not be pro-active, in the sense that it takes the initiative with respect to one or more of:
? performing observations
? communicate its own observation results to the other agent
? ask the other agent for its observation results
? draw conclusions about the classification of the object
Moreover, it may be reactive to the other agent in the sense that it responds to a request for observation information:
? by communicating its observation result as soon as they are available
? by starting to observe for the other agent
These agent attitudes are represented explicitly as (meta-)facts in the agent’s component own process control. By varying these attitude facts, different variants of agents can be defined. The impact of these explicitly specified characteristics has been specified in the model. For example, if an agent has the attitude that it will always take the initiative to communicate its observation results as soon as they are acquired, then the agent’s behaviour should show this, but if the characteristic is not there, then this behaviour should not be present. This requires an adequate interplay between the component own process control and the component agent interaction management within the agent, and adequate knowledge within agent interaction management.
The successfulness of the system depends on the attitudes of the agents. For example, if both agents are pro-active and reactive in all respects, then they can easily come to a conclusion. However, it is also possible that one of the agents is only reactive, and still the other agent comes to a conclusion. Or, an agent that is only pro-active in reasoning and reactive in information acquisition may come to a conclusion due to pro-activeness of the other agent. So, successfulness can be achieved in many ways and depends on subtle interactions between pro-activeness and reactiveness attitudes of both agents. The formal analysis of the example in the following sections provides a detailed picture of these possibilities.
5 Formal Analysis of the Example System: Top Level
In this section the properties of the system as a whole (defined in Section 5.1) are related to properties of the agents and the world (defined in Section 5.2 and 5.3), and their interaction.
5.1 Properties for the Top Level of the System
First, it is determined which properties the system as a whole should satisfy. Considering that the system S is a classification system, it is expected that S produces output of the form object_type(s) for some s. A first requirement is that output generated by the system is correct, i.e., if the system derives object_type(s) for some s, it is true in the world situation. Let the world state (which is assumed static) be denoted by M. In Figure 4 the successfulness property of S is related to other properties of S and assumed properties of the next level. In Figure 3 the correctness property of S is similarly related to other properties. The following property relates the output of the system to the current world state.
Correctness of S
The system S is called correct if:
M Traces(S) t s
[ state S(M , t, output(S)) object_type(s) M object_type(s) ]
∧ [ state S(M , t, output(S)) ?object_type(s) M ?object_type(s) ]
Output information of S is only provided by agents
As can be seen in Figure 1 the only information links connected to the output of S are the information link from agent A to S and the information link from agent B to S. Furthermore, the output interface of S cannot spontaneously change its contents. Therefore, the output information of the system is only provided by agents.
correctness of S
conclusion correctness of all agents
output
information of
S is only
provided by
the agents &
Fig. 3. Correctness of S
Next, the system is required to be successful in generating conclusions: during the process, for each s, at some time point it should either have derived positive output, or negative output:
Successfulness of S
The system S is called successful if:
M Traces(S) s t state S(M , t, output(S)) object_type(s))
∨t state S(M , t, output(S)) ?object_type(s))
To guarantee the adequate information exchange between the different components, the following properties are needed:
Interaction effectiveness
The interaction from agent X to the output interface of system S is called effective if, for φ either object_type(s) or?object_type(s):
M Traces(S) t s [state S(M , t, output(X)) φ
t’>t state S(M , t’, output(S)) φ]
The interaction from the external world W to agent X is called effective if: M Traces(S) t r,sign
[ state S(M , t, output(W)) observation_result_for(view(X,r), sign, X)
t’>t state S(M , t’, input(X)) observation_result(view(X,r),sign)]
The interaction from the agent X to the external world W is called effective if: M Traces(S) t r,sign
[ state S(M , t, output(X)) to_be_observed (view(X,r))
t’>t state S(M , t’, input(W)) to_be_observed_by (view(X,r), X)]
Interaction effectiveness can be proven from the detailed specification of the information links involved and the timely functioning of those information links as specified in the task control of the component containing the information link given in the detailed specification. This property is needed to prove several environment properties of the agents and also to prove successfulness of S. Sometimes it will not be stated explicitly.
Agent provides output information of S
An agent X provides output information of system S if X is conclusion successful and the interaction from X to S is effective.
S is successful
or
conclusion successfulness, A effective interaction
from A to S
conclusion
successfulness, B
effective interaction
from B to S
A provides output information of S
B provides output information of S
& def& def
Fig. 4. Successfulness of S
It is undesirable (for a static world situation) that the system changes its mind during the process. Therefore the requirement is chosen that once a conclusion has been derived, this is never revised:
Conservativity of S
The system S is called conservative if:
M Traces(S) t s
[ state S(M , t, output(S)) object_type(s)
t’>t state S(M , t’, output(S)) object_type(s) ]
∧ [ state S(M , t, output(S)) ? object_type(s)
t’>t state S(M , t’, output(S)) ? object_type(s) ] )
The property of conservativity of S can be proven in a similar way as the other properties of S. The proof has been omitted from this paper.
Communication effectiveness
The communication from agent X to agent Y is called effective if:
M Traces(S) t φ
[state S(M , t, output(X)) to_be_communicated_to(q, φ, sign, Y)
t’>t state S(M , t’, input(Y)) communicated_from(q, φ, sign, X) ]
Communication effectiveness can be proven in the same way as interaction effectiveness. This property is needed to prove environment properties of the agents.
5.2 Properties of the Agents
The required properties of the system have been proven from assumed properties of the components at one level lower. During this proof process these assumptions have been discovered. A number of assumptions are quite straightforward. For example, correctness inherits upward from the agents to the system:
Conclusion correctness of an agent
An agent X is called conclusion correct if:
M Traces(X) t s
[ state X(M , t, output(X)) object_type(s) M object_type(s) ]
∧ [ state X(M , t, output(X)) ?object_type(s) M ?object_type(s) ]
This property logically depends on other properties of the agent, input correctness and conditional conclusion correctness, as can be seen for agent A in Figure 5.
Input correctness of an agent
a) An agent X is called observation input correct if
M Traces(X) t r
[state X(M , t, input(X)) observation_result(view(X,r),pos) M view(X,r) ]∧M Traces(X) t r
[state X(M , t, input(X)) observation_result(view(X,r),neg) M ?view(X,r) ]
b) An agent X is called communication input correct if
M Traces(X) t r
[state X(M , t, input(X)) communicated_from(world_info,view(Y,r), pos,Y) M view(Y,r) ]∧ M Traces(X) t r
[state X(M , t, input(X)) communicated_from(world_info,view(Y,r), neg,Y) M ?view(Y,r) ]
c) An agent X is called input correct if X is observation input correct and
communication input correct.
Conditional conclusion correctness of an agent
An agent X is called conditionally conclusion correct if the following holds: if input correctness of X then conclusion correctness of X.
input correctness, A
conditional conclusion correctness, A
observation
input
correctness, A &
conclusion correctness, A
& def communication input correctness, A
Fig. 5. Conclusion correctness of A
The following property is needed to prove conservativity of S .
Conclusion conservativity of an agent
The agent X is called conclusion conservative if:M Traces(X) t s
[ state X (M , t, output(X)) object_type(s)
t’>t state X (M , t’, output(X)) object_type(s) ]
∧ [ state X (M , t, output(X)) ? object_type(s)
t’>t state X (M , t’, output(X)) ? object_type(s) ] )
Again, the proof has been omitted from this paper. Successfulness of the system (see Figure 4) depends on successfulness of at least one of the agents.
Conclusion successfulness of an agent
The agent X is called conclusion successful if:M Traces(X) t state X (M , t, output(X)) object_type(s))
∨ t state X (M , t, output(X)) ?object_type(s))
This property can be proven in a number of ways. However, in all proofs the properties information saturation of the agent and conclusion pro-activeness is required, see Figure 6 for the logical relations between properties of agent A that are needed to prove conclusion successfulness of agent A .
Information saturation of an agent
a) The agent X is called observation info saturating if:
M Traces(X) t r sign
state X(M , t, input(X)) observation_result(view(X,r), sign)
b) The agent X is called communicated info saturating if:
M Traces(X) t r sign
state X(M , t, input(X)) communicated_from(world_info, view(Y,r), sign, Y)
c) The agent X is called request saturating if for all agents Y different from X:
M Traces(X) t r
state X(M , t, input(X)) communicated_from(request, view(X,r), pos, Y)
d) The agent X is called information saturating if X is observation info saturating and communicated info saturating.
Conclusion pro-activeness
The agent X is called conclusion pro-active if for all agents Y different from X: M Traces(X) t,t’
[r,r’ sign,sign’
state X(M , t, input(X)) observation_result(view(X,r), sign)
state X(M , t’, input(X)) communicated_from(world_info, view(Y,r’), sign’, Y) ] ]
t”>t, t”>t’ s [ state X(M , t”, output(X)) object_type(s) ∨
state X(M , t“, output(X)) ?object_type(s)]
conclusion successfulness, A
information saturation, A
communicated info
saturation, A
observation info saturation, A
pro-active observation info saturation, A reactive observation info saturation, A
observation pro-activeness, A
observation
effectiveness, A
request
saturation, A
conclusion pro-activeness, A or
&
&
strongly reactive
information provision, A
or
spontaneous observation
info saturation, A
observation
reactiveness, A
weakly
reactive
information
provision, A
& def
& def& def
Fig. 6. Conclusion successfulness of A
All properties occurring in Figure 6 are also properties of agent A. The leaves in the tree are either environmental properties of A or behavioural properties of A. To prove environmental properties of a component behavioural properties of other components of the same level (in this case other agents and/or the world) are needed as are properties about interactions between these components (in this case interactions
between agents and between the world and agents). For example, the property communicated information saturation of agent A (see Figure 6) can be proved from interaction effectiveness from agent B to agent A and the property successful information provision of agent B , see Figure 7.
Information provision successfulness
a) The agent X is called successful information providing if:M Traces(X) r, sign t
state X (M , t, output(X)) to_be_communicated_to(world_info, view(X,r), sign, Y)
b) The agent X is called successful information providing pro-active if
X is observation effective and strongly information providing pro-active.c) The agent X is called successful information providing reactive if
X is request saturating and reactive observation effective.
communicated info saturation, A &
communication effectiveness from B to A successful information provision, B
Fig. 7. Communicated info saturation of A
Similarly, the properties observation input correctness and communication input correctness of an agent depend on correct information coming from the other agent or from the world (see Section 5.3 for definitions of properties concerning the world), see Figure 8.
interaction effectiveness,from W to A correctness of W &&input observation
information of A is only
provided by W observation input correctness, A communication input correctness, A information provision correctness, B communication effectiveness,from B to A
&
&input communication information of A is only provided by B Fig. 8. Information correctness of A The property spontaneous observation info saturation of an agent as used in Figure 6 is defined by the property pro-activeness of the world (see Section 5.3) and interaction effectiveness from the world to that agent, see Figure 9.
spontaneous observation info saturation, A
pro-activeness, W effective interaction from W to A
& def
Fig. 9. Spontaneous observation info saturation of A
The property successful information provision of agent B, used in Figure 7, can be proven in several ways. The first division made in Figure 10 is between pro-active and reactive information provision. Also the notions strong and weak are used. Information provision correctness
The agent X is called information providing correct if:
M Traces(X) r t
[ state X(M , t, output(X)) to_be_communicated_to(world_info, view(X,r), pos, Y)
M view(X,r) ]
M Traces(X) r t
[ state X(M , t, output(X)) to_be_communicated_to(world_info, view(X,r), neg, Y)
M ?view(X,r) ]
Information providing pro-activeness
a) The agent X is called weakly information providing pro-active if:
M Traces(X) t, r, sign
[ state X(M , t, input(X)) observation_result(view(X,r), sign)
t’ state X(M , t’, output(X)) to_be_communicated_to(world_info, view(X,r), sign, Y) ]
b) The agent X is called strongly information providing pro-active if
X is weakly information providing pro-active and observation pro-active. Information providing reactiveness
a) The agent X is called weakly information providing reactive if:
M Traces(X) t, t’,r, sign
[state X(M , t, input(X)) observation_result(view(X,r), sign)
state X(M , t’, input(X)) communicated_from(request,view(X,r), pos,Y) ]
t”>t, t”>t’ state X(M , t”, output(X))
to_be_communicated_to(world_info, view(X,r), sign, Y)
b) The agent X is called strongly information providing reactive if
X is weakly information providing reactive and observation reactive.
c) The agent X is called reactive observation info saturating if
X is request saturating and strongly information providing reactive.
The tree in Figure 10 consists of logical relations between properties of the agent B. Some of them have been defined above, the others are defined as follows. Information acquisition pro-activeness of an agent
a) The agent X is called observation pro-active if:
M Traces(X) r t state X(M , t, output(X)) to_be_observed (view(X,r)) ]
b) The agent X is called request pro-active if for all agents Y different from X:
M Traces(X) r t state X(M , t, output(X))
to_be_communicated_to(request, view(Y,r), pos, Y)]
c) The agent X is called information acquisition pro-active if X is observation pro-active and request pro-active.
Observation reactiveness of an agent
The agent X is called observation reactive if:
M Traces(X)t r [state X(M , t, input(X)) communicated_from(request, view(X,r), pos, Y) t’ state X(M , t, output(X)) to_be_observed (view(X,r)) ]
successful information provision, B or
& def
request
saturation, B successful information provision reactive, B
reactive observation
effectiveness, B
successful information
provision pro-active, B observation info
saturation, B or or spontaneous observation info saturation, B pro-active observation info saturation, B observation pro-activeness, B observation effectiveness, B & def
reactive
observation
info saturation, B reactive observation info saturation, B
request saturation, B strongly reactive information provision, B
observation reactiveness, B
weakly reactive information provision, B & def & def
observation effectiveness, B weakly reactive information provision, B
strongly reactive information provision, B observation reactiveness, B
weakly
reactive
information
provision, B reactive observation effectiveness, B observation info saturation, B & def
stongly information
providing pro-active, B
observation effectiveness, B weakly pro-active
information
provision, B observation
pro-activeness, B successful information
provision pro-active, B
& def & def Fig. 10. Successful information provision of B
Information acquisition effectiveness of an agent
a) The agent X is called observation effective if:M Traces(X) t r [state X (M , t, output(X)) to_be_observed (view(X,r))
t’>t sign state X (M , t, input(X)) observation_result(view(X,r), sign) ]
b) The agent X is called request effective if:M Traces(X) t r
[state X (M , t, output(X)) to_be_communicated_to(request,view(Y,r),pos,Y)
t’>t, sign state X (M , t, input(X)) communicated_from(world_info, view(Y,r), sign, Y) ]
c) The agent X is called information acquisition effective if
X is observation effective and request effective.
d) The agent X is called reactive observation effective if:M Traces(X) t r
[state X (M , t, input(X)) communicated_from(request, view(Y,r), pos, Y)
t’>t, sign state X (M , t, input(X)) observation_result(view(X,r), sign)]
e) The agent X is called pro-active observation info saturating if
X is observation pro-active and observation effective.
The relations between the environmental properties request saturation of agent A and observation effectiveness of agent A , and properties of the world, of agent B , and of interactions between agents and world can be found in Figure 11.observation effectiveness, A
& def
&observation
effectiveness, W interaction effectiveness between A and W interaction effectiveness from W to A
interaction effectiveness
from A to W request saturation, A &
interaction effectiveness from B to A request pro-activeness, B
Fig. 11. Observation effectiveness and request saturation of A
5.3 Properties of the World
For the component World assumptions on correctness and conservativity are made.Correctness of the world
The world W is called correct if:M Traces(W) t v, X
[ state W (M , t, output(W)) observation_result_for(view(X, r), pos, X)
M view(X, r) ]M Traces(W) t v, X
[ state W (M , t, output(W)) observation_result_for(view(X, r), neg, X) M
view(X, r) ]Conservativity of the world
The world W is called conservative if:M Traces(W) t r, sign, X
[ state W (M , t, output(W)) observation_result_for(view(X, r), sign, X)
t’>t state W (M , t’, output(W)) observation_result_for(view(X, r), sign, X) ]
Moreover, the world should be effective in providing observation results for observations initiated by the agents.
Observation effectiveness of the world
The component W is called observation effective if:
M Traces(W) t r, X
[state W(M , t, input(W)) to_be_observed_by(view(X, r), X) ]
[t’>t,sign state W(M , t’, output(W)) observation_result_for(view(X, r), sign, X) ]
It is also possible that the world provides observation information without an initiative from the agent (e.g., by automated sensors). In this case the world shows pro-activeness:
Observation pro-activeness of the world
The component W is called observation pro-active if:
M Traces(W) r, X t,sign
state W(M , t, output(W)) observation_result_for(view(X, r), sign, X)
6 Properties of Agent Components
The properties of the agents needed to prove the properties of the top level of the system were discussed in Section 5.2. The assumed properties of the sub-components of an agent, needed to prove the behavioural properties of that agent, and the logical structure of those proofs in the form of trees are discussed in this section. Properties of the component own process control play a role in the proof of each behavioural agent property.
6.1 Properties of Own Process Control
In the component Own Process Control the agent’s attitudes are explicitly represented. The attitudes are represented in the following manner:
attitude representation within OPC
pro-active observation observation_attitude(pro-active)
weakly pro-active information provision info_provision_attitude(weakly_pro-active) strongly pro-active information provision info_provision_attitude(strongly_pro-active)
pro-active requesting requesting_attitude(pro-active)
pro-active reasoning reasoning_attitude(weakly_pro-active) reactive observation observation_attitude(reactive)
weakly reactive information provision info_provision_attitude(weakly_reactive) strongly reactive information provision info_provision_attitude(strongly_reactive) Attitude determination successfulness of O P C
The component OPC is called attitude determination successful for the attitude pro-activeness of observation if:
M Traces(OPC) t t’≥t
state OPC(M , t’, output(OPC)) observation_attitude(pro-active)
公司组织原则及组织机构设置原则 一、公司的组织原则 公司组织原则是指为了实现有效管理职能、提高管理效率和实现—定的 目标而建立管理机构共同遵循的原则。主要有以下几个方面： （一）目标一致原则。公司及各部门的目标必须和决策层确定的目标保 持高度一致，这是公司提高效能的前提条件，也是团结和鼓舞全体员工同心 协力地完成预定目标的动力。 （二）职责分明原则。组织中职权分明，使每一项管理职能都能落实到 一个执行机构。职责不要分散，不能实行多头领导，造成互相扯皮推诿。组 织的职能定位、个人的职责定位是相当明确的，要把握好角色定位，不失职、不越位是基本要求，坚守本职是核心要求。 （三）责权对等原则。无论组织还是管理者个人（组织是职能、管理者 是职责），只要你担负一定的责任，组织就会授予你相应的权力，以保证你 合法有效地履行你的职责。管理人员、员工所拥有的权力应当与其所承担的 责任一致，权力与其承担的责任应该对等，同时要使员工知道具体的责任内 容、权力范围和利益大小。 （四）协调原则。机构要互相协调、互相衔接，有利发挥组织整体功能， 使组织内部既有分工，又有合作，协调一致，实现一个共同目标。 （五）组织阶层原则。实行统一领导，分级管理，集权与分权结合。要 确定科学的管理幅度和管理层次。要坚持必要的集中统一领导，使企业领导 人指挥决策的实施有效而迅速。又要实行分级管理，调动各方面的积极性。 （六）控制幅度原则。在处理管理幅度与管理层级的关系时，应尽量减 少管理层级。适当的组织扁平化可通过破除公司自上而下的垂直高耸的结 构，减少管理层次，增加管理幅度，裁减冗员来建立一种紧凑的横向组织， 达到使组织变得灵活、敏捷、富有柔性、创造性的目的。但过度扁平化会出 现领导及管理部门的协调工作量大幅度增加、公司运营灵活性受限等阻碍公 司发展的情况。
机械原理课程设计说明书 ——平台印刷机主传动机构运动简图设计设计名称平台印刷机主传动机构运动简图设计 专业机械设计制造及其自动化 08622班 姓名 指导教师 时间2010-7-7
目录 一、设计题目 (3) 1、设计条件与要求 (3) 2、原理图 (3) 二、机械运动方案的选择 (3) 1、平台印刷机主要机构及功能 (3) 2、实现功能的方案 (4) 3、设计方案的拟定和比较及设计思路概述 (4) 4、平台印刷机设计数据 (5) 三、对选定机构的运动分析与设计 (5) 1、曲柄滑块机构综合分析 (5) （1）机构的运动几何关系 (5) （2) 参数选择 (6) 2、双曲柄机构的运动分析 (7) （1）曲柄滑块位移计算Ψ (7) （2）由Ψ1求Ψ3 (8) 3、曲柄滑块机构的位置分析 (8) 4、凸轮机构的设计 (9) （1）凸轮机构从动件运动规律的确定 (9) （2）绘制补偿凸轮轮廓 (10) 四、程序设计 (10) 1、所调用的子程序及功能 (10) 2、所编程序的框图 (11) 3、主程序如下 (13) 4、主程序子程序中主要参数说明 (16) 5、程序运行结果 (17) 6、版台位移，速度以及滚筒位移，速度曲线 (17) 五、总结 (17) 六、参考文献 (18)
一、设计题目 1、设计条件与要求 工作原理：平台印刷机的工作过程由输纸，着墨，压印和收纸四部分组成，主运动是压印，由卷有空白纸张的滚筒与镶着铅字的版台之间纯滚动来完成。滚筒与版台表面之间的滑动会造成字迹模糊，是不允许的。因此，对运动的主要要求是：其一，版台的移动速度严格等于滚筒表面的圆周速度；其二，为了提高生产率，要求版台的运动有急回特性。有一台电动机驱动。需设计满足上述两个要求的传动机构。执行件的运动为滚筒连续转动和版台往返移动。 2、原理图 图1 二、机械运动方案的选择 1、平台印刷机主要机构及功能 主要机构： 1) 传动机构I——从电动机到版台的运动链；
选择板式换热器要注意以下三个事项 1、板式换热器板型的选择板片型式或波纹式应根据换热场合的实际需要而定。对流量大允许压降小的情况，应选用阻力小的板型，反之选用阻力大的板型。根据流体压力和温度的情况，确定选择可拆卸式，还是钎焊式。确定板型时不宜选择单板面积太小的板片，以免板片数量过多，板间流速偏小，传热系数过低，对较大的换热器更应注意这个问题。艾瑞德每种规格的板片，均具有至少两个板型，采用热混合技术，可以综合换热器的传热和压降，使其运行在最佳工作点。内旁通，双流道技术和不等流通截面积装配为两侧介质流量相差较大的工况提供了完美的解决方案。ARD艾瑞德板式换热器（江阴）有限公司板式换热器有AB系列、AM系列、AL系列、AP系列、AS系列等几大系列百余种板型。各种型号都有深波纹、浅波纹、大角度、小角度等，完全确保满足不同用户的需要，特殊工况可按用户需要专门设计制造。 2、流程和流道的选择流程指板式换热器内一种介质同一流动方向的一组并联流道，而流道指板式换热器内，相邻两板片组成的介质流动通道。一般情况下，将若干个流道按并联或串联的费那个是连接起来，以形成冷、热介质通道的不同组合。流程组合形式应根据换热和流体阻力计算，在满足工艺条件要求下确定。尽量使冷、热水流道内的对流换热系数相等或接近，从而得到最佳的传热效果。因为在传热表面两侧对流换热系数相等或接近时传热系数获得较大值。虽然板式换热器各板间流速不等，但在换热和流体阻力计算时，仍以平均流速进行计算。由于“U”形单流程的接管都固定在压紧板上，拆装方便。 3、压降校核在板式换热器的设计选型使，一般对压降有一定的要求，所以应对其进行校核。如果校核压降超过允许压降，需重新进行设计选型计算，直到满足工艺要求为止。 艾瑞德板式换热器（江阴）有限公司是专业生产可拆式板式换热器（PHE）、换热器密封垫（PHE GASKET）、换热器板片(PHE PLATE)并提供板式
问题背后的问题——组织结构变革设计 来源：网络 当企业出现经营业绩逐渐下滑、产品质量迟迟不能提高、浪费和消耗严重、新的管理措施总是难以实施、管理效率下降、员工不满情绪增加等等诸多问题的时候，企业家们首先想到的是什么？常见的思考方式会认为，这些可能是人员素质问题、激励措施问题、绩效考核问题或者是制度不完善，更进一步的，或许还会认为是企业战略不清晰、是企业执行力不够或者是企业文化需要重塑？于是，企业往往会采取执行力培训、文化重塑、战略转型、绩效管理改革、薪资改革等等手段，甚至会解聘和更换员工。 这些措施本身毫无问题，但是却往往只能起到短暂的作用，甚至解聘和更换员工都无法彻底解决问题。那么，究竟是什么困扰着企业的发展？我们需要找到问题背后的问题。 一、问题背后的问题——组织结构不合适才是根源 当企业出现病症的时候，最容易被忽视但却可能是最根本的问题是，企业的组织结构不再适合企业的发展。 之所以容易被忽视，是因为在传统的管理理论中我们通常认为，有很多合理的组织结构模型可以供选择：例如职能制、矩阵式、事业部制、母子公司体制、超矩阵式等。所以在组织结构设计的时候，企业家们会自觉或不自觉的采取一种耳熟能详的或者公认的组织结构形式。尤其是当看到很多成功的企业也在采用类似的组织结构时，企业家们会更加安心。 但是完美的组织结构理论这一温情脉脉的面纱背后，冰冷的现实时刻在提醒我们，不存在一种普适的、绝对正确的组织结构。组织结构的本质是为了实现企业战略目标而进行的分工与协作的安排，它是让人们有效的一起工作的工具。因此，不同的战略、不同的时期、不同的环境必然需要配合不同的组织结构。 不同的战略：组织结构是战略实施的载体，战略不同组织结构必然随之调整。就像蜗牛与羚羊，蜗牛的战略是当危险来临就缩进硬壳里面，所以蜗牛需要背着房子到处走；羚羊的战略是当危险来临就要快速奔跑离开，所以羚羊就需要强健的四肢。如果让羚羊背上房子，又怎么能实施快速奔跑的战略呢？ 不同的发展阶段：在企业发展的不同阶段，随着组织规模的扩大和能力的改变，组织结构也需要相应变革来适应组织的发展。在创业阶段，企业需要快速反应来保证生存，组织结构需要简单，围绕主要职能来设置部门，如果组织结构过于臃肿、部门过多，就会造成流程割裂、效率低下，企业的生存就会出现问题。当企业发展壮大，如果仍然粗略的设置组织结构，就会造成重要职能薄弱或缺失，企业就会缺乏相应的能力，企业的发展就会受到影响。就像人小的时候，如果穿过大的鞋，就会举步维艰，怎么也跑不快；当长大成人，如果再穿小时候的鞋，跑的过程中一定会受到束缚、疼痛难忍。
板式换热器的技术参数 更新日期：2012-12-12 进出口管径进出口管径的大小以介质在管道内的活动速度小于3m／S为宜来确定，最大流速宜小于4m／s。现海内板式板式换热器板片厚度一股为0.5～1.0mm，考虑到厂家制造工艺、现场操纵水平及侵蚀、除垢等因素，板式换热器板片厚度宜选择0.7～0.9ram。 板式换热器软水侧压力损失 软化水进出板式换热器温度根据现有高炉软水供给的经验，软水供给温度在3 5～40℃之间时，对高炉稳产高产、安全出产最有利，同时考虑到夏季冷媒水及冷却塔的冷却能力，软化水进高炉温度在夏季最不利工况时宜小于40℃，软化水出高炉温度宜小于4 5℃，即软化水进板式换热器温度宜为4 5℃，出板式换热器温度宜为40℃。 板式换热器板片材质板式板式换热器板片材质基本上采用不锈钢、钛合金两种。 板式板式换热器数目根据中小高炉的特点，其炉体冷却软水系统采用板式板式换热器的数目宜为3台并联，每台流量为最大流量的5O%，正常运行开二备一。 冷却水进、出板式换热器温度根据夏季冷却水供水温度及冷却塔工作能力，冷却水进板式换热器温度宜小于32℃，出板式换热器温度宜小于3 7℃。 流量根据水在冷却壁的公道流速，并重点考虑高炉后期内壁耐热层减薄、传热量急剧增加的情况，按后期最大传热量来确定软化水流量。因为钛材料价格为不锈钢的4～6倍，且高炉冷却系统板式板式换热器板片材质采用不锈钢即可知足要求，为此板式板式换热器板片材质选用不锈钢。 板式换热器正常工作压力根据软水系统闭路轮回的特点，板式换热器正常工作压力最小值应为软水系统轮回水泵出口压力与高炉高位膨胀水箱水位高度之和。原有空冷器软水侧压力损失小于0.05MPa，因此板式板式换热器软水侧压力损失必需小于’0.05M Pa。 密封垫材质考虑现场实际情况及板式板式换热器工作温度、软化水成分等诸多因素，密封垫材质宜选用三元乙丙橡胶。 因为软水系统除停开空冷器、并入板武板式换热器外，其它部门不变，因此，板式板式换热器软水侧压力损失须小于原有空冷器软水侧压力损失。 板式换热器板片厚度板式换热器板片厚度与传热系数成反比关系，板片厚度越小，传热效果越好，但同时也轻易侵蚀泄漏。 ARD拥有世界上最先进的设计和生产技术以及最全面的换热器专业知识，有专门的选型软件根据用户不同工况测算出最适合的板式换热器。
（二）板式换热器 3设计与运行条件 3.1板式换热器型式 板式换热器采用等截面可拆卸板式换热器（水-水），换热面材质材质为GB316不锈钢。 3.2板式换热器的配置 本次招标共需配备2台可拆卸板式换热器（水-水），单台功率22.5MW，单台换热面积950㎡，换热器接管管径按设计所提管径配置，换热器按本技术规书所提面积订货。 3.3板式换热器设计参数 下表为单台22.5兆瓦板式换热器的参数
3.4热网循环水水质 板式换热器工作介质为热网循环水，水质为软化水，具体水质如下：
3.5运行方式 板式换热器并联运行。 板式换热器换热量的控制通过控制一次侧（高温介质）流量和控制二次侧（低温介质）流量来实现。 3.6设备的安装地点及标高 板式换热器安装在换热站0米层。 4技术要求 投标方提供的板式换热器设计、制造、检验与验收应满足国家相关规中的相关规定，同时应满足本技术规书术要求，如有矛盾时按较高要求执行。 4.1板式换热器性能要求 4.1.1投标方所提供的板式换热器是可拆卸板式换热器（水-水），其技术先进、经济合理，成熟可靠的产品，具有较高的运行灵活性。 4.1.2板式换热器能在最大工况点长期连续运行，能满足板式换热器不同运行工况的需要，并且预留能增加10%换热能力板片的安装空间和技术条件。 4.1.3板式换热器不宜选择单板面积太小的板片，避免板片数量过多,要求单板面积大于等于2.5㎡。 4.1.4板式换热器采用板型应使换热器流体充分湍动，防止板片表面结垢。 4.1.5板式换热器应选用阻力小的板型，保证一次侧（高温介质）压降不大于0.03MPa，二次侧（低温介质）压降不大于0.03MPa。 4.1.6板式换热器板片厚度应不小于0.7mm。 4.1.7板式换热器额定工况运行时，二次侧（低温介质）出口温度偏差不应出现负偏差。 4.1.8板片波纹形式应采用技术成熟、有成功使用业绩的波纹形式。 4.1.9板式换热器外部、部保证不泄漏，一、二次水禁止混流。
三菱重工空调故障代码 三菱重工空调故障代码 LED1 LED2 LED3 LED4 故障原因 灭灭灭灭正常 亮亮亮亮室外机主版故障 亮灭灭灭相序错误 灭亮灭灭高压继电器动作或室外主控板故障 灭灭亮灭低压继电器动作或室外主控板故障 灭灭灭亮外环温异常或温度过高 三菱电机空调GJ外机接线端子功能表 短路端子号短路端子功能不短接时短接时 J1 相位反向检测不检测检测 J2 卸压阀控制除霜时关除霜时开 J3 曲轴箱E加热连续加热相隔1小时重复开关 J4 上电3分钟延时有无 三菱电机空调GJ外机故障代码 发光二极管闪烁故障原因发光二极管亮 LED1 相序错误室内输出控制信号正常 LED2 缺相室内输出控制信号正常 LED3 外环温异常63H1运行 LED4 63H2动作压缩机启动 LED5 室外51C动作保护室外风机启动 LED6 室外26C动作保护四通阀启动 LED7 过热保护卸压阀启动 LED8 控制输入电路异常曲轴箱加热器启动 三菱重工空调故障代码【二】 三菱电机空调GJ（PSH-3GJ、6GJ[H]S） 灯亮位置故障原因 12--29 室外故障（检查室外机连接线、制冷剂、温度传感器）11--28 室外环温异常或控制板故障 10--27 内管温异常或控制板故障 7--24 防冷风保护（室内进出风口有无障碍，室内电机）
注：室内故障灯在面板左恻显示，室内JP4短路件插上时，首次上电无延时功能，否则相反。室内JP2短路插件插上，可将防冷风温度由1度改为负2度。 三菱重工： 显示内容故障原因 检查灯（黄）闪亮室内管温或环温异常 运行灯（黄）闪亮二次电源过欠压保护 检查灯（黄）闪亮一次过冷保护 检查灯（黄）闪亮二次过热保护 检查灯（黄）闪亮三次室外机保护装置动作 检查灯（黄）闪亮四次浮子开关动作，水泵排水不正常 检查灯（黄）闪亮五次室内管温传感器检查25度以上 三菱重工504系列 显示代码故障原因 E1 室外机电源关闭或电源线断开（保险、变压器坏） E2 室内机机号重复或室内主板损坏 E3 室外机机号设定不良，室外机信号线断开或电源关 E4 室外机机号设定不良或室内机主板损坏 E5 室外机电源关、室外侧信号线断或控制板损坏 E6 室内侧温度传感器开路或室内主控制板损坏 E7 室内机吸入温度传感器或室内主控制板损坏 E8 过热保护（室内管温或室内主板损坏） E9 运转开关误动作（选择开关或室内主控制板损坏） E10 多台中心控制时室内连接台数超过17台 E31 室外机侧机号重复或室外主控制板故障 E32 电源相序错、缺T相、外管温异常、外主板坏 E33 压缩机过载缺S或R相、52C二次侧断开保护 E34 压缩机缺T相（52C二次侧）室外主控制板损坏 E35 制冷时室外散热器超过设定或室外管温故障，外主板坏 E36 室外排气管温过高保护（35-80型120度、90-200型135度以上） E37 室外管温开路，室外主控制板故障 E38 室外温度传感器开路，室外主控制板故障 E39 室外排气管温度传感器开路，室外主控板故障 E40 室外机保护元件动作，室外主控板故障 注：室内红灯LED闪表示室内故障，绿灯LED亮表示室内正常。室外红灯闪表示室外故障。绿灯亮表示室外正常。室外排气温度（58K/90度）压缩机开始运行，大于100度压缩机停，室外环温阻值5K，室外管温阻值18K，小于60度压缩机运转，大于60度压缩机停。 三菱重工空调故障代码【三】 三菱重工SRF50HC、SRFD50HC 运行灯闪诊断记号故障原因
①战略匹配原则 一方面，战略决定组织结构，有什么样的战略就有什么样的组织结构；另一方面，组织结构又支持战略实施，组织结构是实施战略的一项重要工具，组织结构是随着战略而定的，它必须根据战略目标的变化而及时调整。通常情况下组织根据近期和中长期发展战略需要制订近期和中远期组织结构 ②顾客满意原则 顾客是组织赖以生存和发展的载体，组织设计的组织架构和业务流程必须是以提高产品和服务，满足顾客需求为中心的。要确保设计的组织架构和流程能够以最快捷的速度提供客户满意的产品的服务，组织中各部门的工作要优质、高效达到始于顾客需求，终于顾客满意的效果。政府部门应视群众为自己的顾客，提高服务意识，摆脱官僚主义。 ③精简且全面原则 精简原则是为了避免组织在人力资源方面的过量投入，降低组织内部的信息传递、沟通协调成本和控制成本，提高组织应对外界环境变化的灵活性；对于非核心职能，可能的话应比较自建与外包的成本，选择成本最低的方案。全面原则则是体现麻雀虽小，五脏俱全的思想，即组织功能应当齐全，部门职责要明确、具体，这样即使出现一人顶多岗的情况，也能使员工明确认知自身的岗位职责。 ④分工协作原则 如果组织中的每一个人的工作最多只涉及到单个的独立职能，或
者在可能的范围内由各部门人员担任单一或专业化分工的业务活动，就可提高工作效率，降低培训成本。分工协作原则不仅强调为了有效实现组织目标而使组织的各部门、各层次、各岗位有明确的分工。还强调分工之后的协调。因此在组织机构设计时，必须强调职能部门之间、分子公司之间的协调与配合，业务上存在互补性或上下游关系时，更需要保持高度的协调与配合，以实现组织的整体目标。 ⑤稳定与灵活结合原则 稳定性原则一方面要求组织在进行组织变革时，应考虑一定的稳定性，给组织以休养生息的机会，避免频繁变革导致的人心不稳和业绩下滑，另一方面要求所涉及的组织结构也有利于组织的稳定，避免个别岗位控制过多的资源而导致组织对该岗位的依赖度增加，从而增加组织的不稳定因素。灵活性原则要求组织的组织结构能针对内外条件及环境变化而做相应调整，以增加组织对环境的适应性。稳定与灵活相结合的原则就是在保持相对的稳定性的前提下，考虑多种实际因素灵活处理。 ⑥管理层次原则。 由于管理幅度的存在，管理层次的问题自然就显现出来。在组织结构设计过程中，应该充分考虑管理层次对权力流、资源流、信息流的影响，如果因为层次增加而对上述方面的负面影响大于管理幅度增加的影响，则应减少层次，增加幅度。 ⑦管理幅度原则。 管理幅度原则要求各个管理岗位所控制的管理幅度要适当,但每
三菱重工与三菱电机的差别 （文章转载于舒适100）消费者在商场选购空调时，可能会看到三菱重工和三菱电机两个品牌。从名字上看，这两个品牌都和三菱有着密切的联系，那么它们生产的空调又有什么不一样呢?下面一起来看看三菱重工和三菱电机的区别吧。 三菱重工和三菱电机的区别—三菱重工和三菱电机的背景差异时间上，三菱重工始创于1870年，距今已有100多年历史，而三菱电机于1921年成立，发展历程比三菱重工少了半个世纪。技术上，三菱重工制冷采用的都是航空航天以及生物工艺学领域的先进制造技术，水平是任何一个厂家都无法超越的。 三菱重工和三菱电机的区别—三菱重工和三菱电机的投入规模1994年，三菱重工与当时的洗衣机知名品牌广东江门金羚洗衣机厂，合资3000万美元成立了三菱重工金羚空调器有限公司，而三菱电机则是在1995年，与上海电气集团总投资5000万人民币合作建厂。前期投入上三菱重工显然比三菱电机规模大得多。 三菱重工和三菱电机的区别—三菱重工和三菱电机的产品差异
三菱重工是最早以空调行业标杆的身份出现在中国大众身边的。外观上，三菱重工多以高贵典雅的形象出现，而三菱电机则多以俏皮时尚的感觉示众。功能上，三菱重工的压缩机是行业最为优秀的，并有3+1健康防护系统，非常注重舒适、健康、节能、环保，而三菱电机则有动态地面感温、DSP纳米空气净化舱等功能。值得一提的是，早期三菱电机采用的是三菱重工的科技及机电、半导体设备(压缩机、主机板等)，而现在三菱电机空调的原材料及核心部件已经完全国产化，三菱重工则一直坚持百年品质。 由上我们可以看出，这两个空调品牌虽然在名称上有着千丝万缕的联系，但实际上三菱重工和三菱电机的区别还是很大的。在家电领域，三菱重工的制冷非常专业先进，而三菱电机的制冷技术并没有很突出。所以，单就空调而言，三菱重工占有更大的优势。
组织架构设计的原则 企业运作最核心的就是组织架构，组织架构没设计好，会带来非常多的管理问题。组织架构设计好了，很多管理问题迎刃而解。组织架构设计的一般原则包括以下6个方面。 企业运作最核心的就是组织架构，组织架构没设计好，会带来非常多的管理问题。组织架构设计好了，很多管理问题迎刃而解。铭拓咨询认为组织架构设计的一般原则包括以下6个方面。 组织架构设计原则---专业分工 利于专业人才的复制与培养，因为专业出效益。例如一个合格的营销领导人需要具备三种能力，品牌策划能力、销售的能力、管理的能力。像这种人非常不好找。有人说企业内组织因岗设人，其实现在这个时代还有一个观念叫因人设岗，有的时候真是这样，在进行岗位设计的时如果没有考虑到招聘的前提，岗位设计有时就是是错误的。你招不到这个人，因为你设计了一个天才，根本就不好招。所以只能把复杂的事情变简单切分开，招人就好招，不然招不到人。 通常培养一个营销总监一般要五年，至少三年以上，不然他真的没事做。所以说张瑞敏讲过一句话，要培养一个人，三年才刚开始干点事，五年更好用，八年之后就不好用。所以说企业要考虑人才的复制和培养，因为企业里面人、机、料、法、环，人是第一要素。岗位设计要考虑团队人才的结构。
专业出效率，只有专业的人才，才能有真正的发言权把事情做对。我们很多企业经常选择非专业人才去做专业的事情，这是最大的错误。 举个一个生产的例子，我们生产过程中有IE工程，有PE工程,有ME工程，叫PIME工程。PE是生产工程，IE是工业工程，ME是设备工程。在生产的整个运作当中，有这样的工程师做技术指导、设备的保护、生产工艺路线的设计等等，这些事情都是很专业的，PIME 这样的岗位专业要求是非常高的，既要懂工业工程又要懂设备工程。那在企业岗位设计的时候要把它拆分一下，把他拆开三个的时候招人就相对好招很多。 这么做道理是一样的。有的企业他是这样做的，招的是PE，让他去做ME做IE,他根本就做不了。就算是聪明一点，善于学习和总结的人，他也能做，但是他不一定有工业工程那么专业。很多企业都面临这样的问题，所以在组织架构的设计需要涉及到专业分工，因为只有专业才能出效果。就要考虑到专业人才的复制和培养，就要考虑到我能不能招到这种人。所以说有很多企业的问题，组织架构就决定了。 组织架构设计原则---责任唯一 利于责任量化，避免推诿扯皮，培养责任文化。我们之前举个一个例子，凡是同一件事情交给两个以上的人做，往往是没有结果的，这就是人性。干好了不知道谁的功劳，反正干的不好，叫法不责众反正大家都错了，大不了老板骂一顿算了。所以说你要想让一件事情给
组织结构设计，指对企业的组织等级、运营结构及管理模式等进行再造的过程，EMBA、MBA等常见经营管理教育均组织结构设计方法有所探究。 一、定义 组织结构设计，是通过对组织资源（如人力资源）的整合和优化，确立企业某一阶段的最合理的管控模式，实现组织资源价值最大化和组织绩效最大化。狭义地、通俗地说，也就是在人员有限的状况下通过组织结构设计提高组织的执行力和战斗力。 企业的组织结构设计是这样的一项工作：在企业的组织中，对构成企业组织的各要素进行排列、组合，明确管理层次，分清各部门、各岗位之间的职责和相互协作关系，并使其在企业的战略目标过程中，获得最佳的工作业绩。 从最新的观念来看，企业的组织结构设计实质上是一个组织变革的过程，它是把企业的任务、流程、权力和责任重新进行有效组合和协调的一种活动。根据时代和市场的变化，进行组织结构设计或组织结构变革（再设计）的结果是大幅度地提高企业的运行效率和经济效益。 二、目的 创建柔性灵活的组织，动态地反映外在环境变化的要求，并在组织成长过程中，有效地积聚新的组织资源，同时协调好组织中部门与部门之间的关系，人员与任务间的关系，使员工明确自己在组织中应有的权力和应承担的责任，有效地保证组织活动的开展。 三、主要内容 1、职能设计 职能设计是指企业的经营职能和管理职能的设计。企业作为一个经营单位，要根据其战略任务设计经营、管理职能。如果企业的有些职能不合理，那就需要进行调整，对其弱化或取消。
2、框架设计 框架设计是企业组织设计的主要部分，运用较多。其内容简单来说就是纵向的分层次、横向的分部门。 3、协调设计 协调设计是指协调方式的设计。框架设计主要研究分工，有分工就必须要有协作。协调方式的设计就是研究分工的各个层次、各个部门之间如何进行合理的协调、联系、配合，以保证其高效率的配合，发挥管理系统的整体效应。 4、规范设计 规范设计就是管理规范的设计。管理规范就是企业的规章制度，它是管理的规范和准则。结构本身设计最后要落实并体现为规章制度。管理规范保证了各个层次、部门和岗位，按照统一的要求和标准进行配合和行动。 5、人员设计 人员设计就是管理人员的设计。企业结构本身设计和规范设计，都要以管理者为依托，并由管理者来执行。因此，按照组织设计的要求，必须进行人员设计，配备相应数量和质量的人员。 6、激励设计 激励设计就是设计激励制度，对管理人员进行激励，其中包括正激励和负激励。正激励包括工资、福利等，负激励包括各种约束机制，也就是所谓的奖惩制度。激励制度既有利于调动管理人员的积极性，也有利于防止一些不正当和不规范的行为。 四、基本理论
机构传动方案设计 设计方案要发散思维，参考资料文献关于机构传动方案设计知道怎么做吗？下面是XX为大家整理了机构传动方案设计，希望能帮到大家！ 这种方法是从具有相同运动特性的机构中，按照执行构件所需的运动特性进行搜寻。当有多种机构均可满足所需要求时，则可根据上节所述原则，对初选的机构形式进行分析和比较，从中选择出较优的机构。 常见运动特性及其对应机构 连续转动定传动比匀速平行四杆机构、双万向联轴节机构、齿轮机构、轮系、谐波传动机构、摆线针轮机构、摩擦轮传动机构、挠性传动机构等变传动比匀速轴向滑移圆柱齿轮机构、混合轮系变速机构、摩擦传动机构、行星无级变速机构、挠性无级变速机构等非匀速双曲柄机构、转动导杆机构、单万向连轴节机构、非圆齿轮机构、某些组合机构等往复运动往复移动曲柄滑块机构、移动导杆机构、正弦机构、移动从动件凸轮机构、齿轮齿条机构、楔块机构、螺旋机构、气动、液压机构等往复摆动曲柄摇杆机构、双摇杆机构、摆动导杆机构、曲柄摇块机构、空间连杆机构、摆动从动件凸轮机构、某些组合机构等
间歇运动间歇转动棘轮机构、槽轮机构、不完全齿轮机构、凸轮式间歇运动机构、某些组合机构等间歇摆动特殊形式的连杆机构、摆动从动件凸轮机构、齿轮-连杆组合机构、利用连杆曲线圆弧段或直线段组成的多杆机构等间歇移动棘齿条机构、摩擦传动机构、从动件作间歇往复运动的凸轮机构、反凸轮机构、气动、液压机构、移动杆有停歇的斜面机构等预定轨迹直线轨迹连杆近似直线机构、八杆精确直线机构、某些组合机构等曲线轨迹利用连杆曲线实现预定轨迹的多杆机构、凸轮-连杆组合机构、行星轮系与连杆组合机构等特殊运动要求换向双向式棘轮机构、定轴轮系等超越齿式棘轮机构、摩擦式棘轮机构等过载保护带传动机构、摩擦传动机构等…………利用这种方法进行机构选型，方便、直观。设计者只需根据给定工艺动作的运动特性，从有关手册中查阅相应的机构即可，故使用普遍。 任何一个复杂的执行机构都可以认为是由一些基本机构组成的，这些基本机构具有下图所示的进行运动变换和传递动力的基本功能。
换热器主要参数及性能特点 主要控制参数 板水加热器的主要控制参数为水加热器的单板换热面积、总换热面积、热水产量、换热量、传热系数K、设计压力、工作压力、热媒参数等。 性能特点 （1）换热量高，传热系数K值在3000~8000W/(m22K)范围，高于其它换热器型式。 （2）板式换热器具有很高的传热系数，就决定了它具有结构紧凑、体积小的特点，在每立方米体积内可以布置250平方米的传热面积，大大优于其它种类的换热器。 艾瑞德板式换热器（江阴）有限公司作为专业的可拆式板式换热器生产商和制造商，专注于可拆式板式换热器的研发与生产。ARD艾瑞德专业生产可拆式板式换热器（PHE）、换热器密封垫（PHEGASKET）、换热器板片(PHEPLATE)并提供板式换热器维护服务（PHEMAINTENANCE）的专业换热器厂家。 ARD艾瑞德拥有卓越的设计和生产技术以及全面的换热器专业知识，一直以来ARD致力于为全球50多个国家和地区的石油、化工、工业、食品饮料、电力、冶金、造船业、暖通空调等行业的客户提供高品质的板式换热器，良好地运行于各行业，ARD已发展成为可拆式板式换热器领域卓越的厂家。
ARD艾瑞德同时也是板式换热器配件（换热器板片和换热器密封垫）领域专业的供应商和维护商。能够提供世界知名品牌（包括：阿法拉伐/AlfaLaval、斯必克/SPX、安培威/APV、基伊埃/GEA、传特/TRANTER、舒瑞普/SWEP、桑德斯/SONDEX、艾普尔.斯密特/API.Schmidt、风凯/FUNKE、萨莫威孚 /Thermowave、维卡勃Vicarb、东和恩泰/DONGHWA、艾克森ACCESSEN、MULLER、FISCHER、REHEAT等）的所有型号将近2000种的板式换热器板片和垫片，ARD艾瑞德实现了与各品牌板式换热器配件的完全替代。全球几十个国家的板式换热器客户正在使用ARD提供的换热器配件或接受ARD的维护服务（包括定期清洗、维修及更换配件等维护服务）。 无论您身在何处，无论您有什么特殊要求，ARD都能为您提供板式换热器领域的系统解决方案。 （3）板式换热器还具有组装灵活，拆卸清洗方便的特点，可以用增减板片数量
机械传动系统设计实例 设计题目：V带——单级斜齿圆柱齿轮传动设计。 某带式输送机的驱动卷筒采用如图14-5所示的传动方案。已知输送物料为原煤，输送机室内工作，单向输送、运转平稳。两班制工作，每年工作300天，使用期限8年，大修期3年。环境有灰尘，电源为三相交流，电压380V。驱动卷筒直径350mm，卷筒效率0.96。输送带拉力5kN，速度2.5m/s，速度允差±5%。传动尺寸无严格限制，中小批量生产。 该带式输送机传动系统的设计计算如下：
例9-1试设计某带式输送机传动系统的V 带传动，已知三相异步电动机的额定功率P ed =15 KW, 转速n Ⅰ=970 r/min ，传动比i =2.1，两班制工作。 [解] （1） 选择普通V 带型号 由表9-5查得K A =1.2 ，由式 (9-10) 得P c =K A P ed =1.2×15=18 KW ，由图9-7 选用B 型V 带。 （2）确定带轮基准直径d 1和d 2 由表9-2取d 1=200mm, 由式 (9-6)得 ()6.41102.012001.2)1(/)1(12112=-??=-=-=εεid n d n d mm ， 由表9-2取d 2=425mm 。 （3）验算带速 由式 (9-12)得 11π970200π 10.16100060100060 n d v ??= ==?? m/s ， 介于5～25 m/s 范围内，合适。 （4）确定带长和中心距a 由式(9-13)得
)(2)(7.021021d d a d d +≤≤+， )425200(2)425200(7.00+≤≤+a ， 所以有12505.4370≤≤a 。初定中心距a 0=800 mm ， 由式(9-14)得带长 2 122 1004)()(2 2a d d d d a L -+++=π， 2 (425200)2800(200425)2597.62 4800 π -=?+ ++ =?mm 。 由表9-2选用L d =2500 mm ，由式(9-15)得实际中心距 2.7512/)6.25972500(8002/)(00=-+=-+=L L a a d mm 。 （5）验算小带轮上的包角1α 由式(9-16)得 012013.57180?--=a d d α 000042520018057.3162.84120,751.2 -=-?=> 合适。 （6）确定带的根数z 由式(9-17)得 00l α ()c P z P P K K = +?， 由表9-4查得P 0 = 3.77kW,由表9-6查得ΔP 0 =0.3kW;由表9-7查得K a =0.96; 由表9-2查得K L =1.03， 47.403 .196.0)3.077.3(18 =??+= z ， 取5根。 （7）计算轴上的压力F 0 由表9-1查得q =0.17kg/m,故由式(9-18)得初拉力F 0 2c 0α 500 2.5 (1)P F qv zv K = -+
西北工业大学 机械原理课程设计说明书 --包装机推包机构运动简图与传动系统设计 指导老师： 班级： 学生： 学号： 组员： 目录
一、设计题目和要求 (3) 二、设计方案的选定 (3) 三、机构的尺寸设计 (8) 1、曲柄滑块结构的尺寸计算 (8) 2、凸轮尺寸设计 (9) 四、电动机的选择及传动方案的设计 (10) 1、电动机的选择 (10) 2、传动方案的设计 (10) 3、总装配件图 (11) 五、设计小结 (12) 六、参考资料 (13) 七、组员任务分配 (13)
一、设计题目和要求 现需要设计某一包装机的推包机构，要求待包装的工件1（见图1-1）先由输送带送到推包机构的推头2的前方，然后由该推头2将工件由a处推至b处（包装工作台），再进行包装。为了提高生产率，希望在推头2结束回程（由b至a）时，下一个工件已送到推头2的前方。这样推头2就可以马上再开始推送工作。这就要求推头2在回程时先退出包装工作台，然后再低头，即从台面的下面回程。因而就要求推头2按图示的abcde线路运动。即实现“平推—水平退回—下降—降位退回—上升复位”的运动。 设计数据与要求： 要求每5-6s包装一个工件，且给定：L=100mm，S=25mm，H=30mm。行程速比系数K在1.2-1.5围选取，推包机由电动机推动。 在推头回程中，除要求推头低位退回外，还要求其回程速度高于工作行程的速度，以便缩短空回程的时间，提高工效。至于“cdea”部分的线路形状不作严格要求。 图 1-1 运动要求图 二、设计方案的选定 1.方案1 用偏置滑块机构与凸轮机构的组合机构，偏置滑块机构与往复移动凸轮机构的组合（图2-1）。在此方案中，偏置滑块机构可实现行程较大的往复直线运动，且具有急回特性，同时利用往复移动凸轮来实现推头的小行程低头运动的要求，这时需要对心曲柄滑块机构将转动变换为移动凸轮的往复直线运动。
目录 1、目录 1 2、选型公式 2 3、选型实例一（水－水） 3 4、选型实例二（汽－水） 4 5、选型实例三（油－水） 5 6、选型实例四（麦芽汁－水） 6 7、附表一（空调采暖，水－水）7 8、附表二（空调采暖，汽－水）8 9、附表三（卫生热水，水－水）9 10、附表四（卫生热水，汽－水）10 11、附表五（散热片采暖，水－水）11 12、附表六（散热片采暖，汽－水）12
板式换热器选型计算 1、选型公式 a 、热负荷计算公式：Q=cm Δt 其中：Q=热负荷（kcal/h ）、c —介质比热（Kcal/ Kg.℃）、m —介质质量流量（Kg/h ）、Δt —介质进出口温差（℃）（注：m 、Δt 、c 为同侧参数） ※水的比热为1.0 Kcal/ Kg.℃ b 、换热面积计算公式：A=Q/K.Δt m 其中：A —换热面积（m 2）、K —传热系数（Kcal/ m 2.℃） Δt m —对数平均温差 K 值表： 介质 水—水 蒸汽-水 蒸汽--油 冷水—油 油—油 空气—油 K 2500～4500 1300~2000 700～900 500～700 175～350 25～58 注：Ｋ值按经验取值（流速越大，Ｋ值越大。水侧板间流速一般在0.2～0.8m/s 时可按上表取值，汽侧板 间流速一般在15m/s 以内时可按上表取值） Δt max -Δt min T1 Δt max Δt min Δt max 为（T1-T2’）和（T1’-T2）之较大值 Δt min 为（T1-T2’）和（T1’-T2）之较小值 T2’ T1’ c 、板间流速计算公式： q T2 A S n 其中V —板间流速（m/s ）、q----体积流量（注意单位转换，m 3 /h – m 3 /s ）、 A S —单通道截面积（具体见下表）、n —流道数 2、板式换热器整机技术参数表： BR0.05 BR0.1 BR0.25 BR0.3 BR0.35 BR0.5 BR0.7 BR1.0 BR1.35 最高使用压力Mpa 2.5 使用温度范围℃ -19~200 装机最大换热面积 5 15 30 65 80 120 220 350 500 最大流量m 3 /h 10 25 40 120 150 250 430 650 1730 标准接口法兰DN 25 40 65 80 100 125 150 250 350 单板换热面积m 2 0.051 0.109 0.238 0.308 0.375 0.55 0.71 1.00 1.35 平均流道截面积m 2 0.000494 0.000656 0.00098 0.00118 0.00119 0.001691 0.002035 0.0286 0.004 设备参考质量Kg 87 290 485 870 980 1800 2800 3700 7200 型号说明：BR0.3-1.0-9-E 表示波形为人字形、单板公称换热面积0.3m 2 、设计压力1.0Mpa 、垫片材质EPDM 、总换热面积为9 m 2 板式换热器。 注：以上选型计算方法适用于本公司生产的板式换热器。 选型实例一（卫生热水用：水-水） Ln Δt m = V= 型 号 设 备 参 数
三菱重工与三菱电机的前世今生 三菱重工与三菱电机都于三菱集团，而三菱集团（日语：三菱グループ）是由原先日本三菱财阀解体后的公司共同组成的一个松散的实体，总共包括约300家企业（资料来自维基百科）。正是因为是松散的实体，因此我们并不能很理直气壮的说这两家企业是三菱集团旗下的公司，而三菱集团似乎也并非其自身认可的称呼而更像是人们对其的惯用说法，对外，其官方报道自称为三菱系列（Mitsubishi Keiretsu）。 三菱集团内部构架图（资料来自维基百科可点击放大）三菱集团其中的29家核心企业组成三菱金曜会（直译：星期五俱乐部），进行集团各企业的联系。目前，由“https://www.doczj.com/doc/637118521.html,委员会”负责维护三菱品牌的完整性以及联盟的门户网站。三菱系列的公司并非一个真正意义上的企业集团，因其诸家公司均独立经营，彼此之间并无从属关系，甚至互为竞争对手。 1、三菱重工——浴火重生的家族嫡子 三菱集团内部重组时序图（资料来自维基百科可点击放大）那么现在出现在我们眼前的三菱重工与三菱电机又是怎么形成的呢？我们先来说说三菱重工的历史。
三菱重工的前身是日本国营长崎造船所，1884年，三菱财阀创始人岩崎弥太郎向工部省租借了三菱重工的前身“长崎造船局”，改名为“长崎造船所”。次年，三菱财阀第二代掌门人岩崎弥之助建立了三菱合资公司，并以长崎造船所为三菱合资公司的核心企业。1887年，三菱合资公司收购长崎造船所全部设备。1893年，长崎造船所改名为“三菱造船所”，隶属于三菱合资公司。 ----------------------------------------------------------------------------- 关键点三菱造船所：三菱造船所是本文要解说的关键点之一，长崎造船所更名为“三菱造船所”应该可以看做这个本属于日本国营的企业，正式完成了私有化。而私有化的完结点也是这家企业产业重组分出三菱重工和三菱电机的初始点。当然，这个资产分拆重组并不是简单的一拍两散一刀切两半那么简单。虽然没有进一步的资料，但是可以想见，三菱造船分出的产业有三个部分，分别是三菱航空机、三菱重工以及三菱电机；而后的三菱重工与三菱电机再合并为战前最终版三菱重工。 ----------------------------------------------------------------------------- 现在的三菱长崎造船所卫星照片 1934年，三菱造船与三菱航空机合并更名为三菱重工业有限公司，三菱造船长崎造船所更名为三菱重工业长崎造船所，至此，二战前的三菱重工基本成型，成为了三菱财阀的支柱企业。 从上面的企业变迁历史来看，三菱重工的发展显然得益于日本政府的幕后推动，长崎造船局相当于会下蛋的母鸡，被借给三菱下蛋，获益后再把整只鸡给买了下来。而后，其发展与整合则主要是三菱财阀自己内部操作，譬如三菱造船与三菱航空机的合并，是财阀内部将业务相近的产业合并的重大事件，属其内部产业整合。
第2章机械传动装置的总体设计 机械传动装置总体设计的任务是选择电动机、确定总传动比并合理分配各级传动比以及计算传动装置的运动和动力参数，为下一步各级传动零件设计、装配图设计作准备。 设计任务书一般由指导教师拟定，学生应对传动方案进行分析，对方案是否合理提出自己的见解。传动装置的设计对整台机器的性能、尺寸、重量和成本都有很大的影响，因此应当合理地拟定传动方案。 2.1 拟定传动方案 1.传动装置的组成 机器通常由原动机、传动装置和工作装置三部分组成。传动装置位于原动机和工作机之间，用来传递运动和动力，并可用以改变转速、转矩的大小或改变运动形式，以适应工作装置的功能要求。传动装置的传动方案一般用运动简图来表示。 2.合理的传动方案 当采用多级传动时，应合理地选择传动零件和它们之间的传动顺序，扬长避短，力求方案合理。常需要考虑以下几点： 1）带传动平稳性好，能缓冲吸振，但承载能力小，宜布置在高速级； 2）链传动平稳性差，且有冲击、振动，宜布置在低速级; 3）蜗杆传动放在高速级时蜗轮材料应选用锡青铜，否则可选用铝铁青铜; 4）开式齿轮传动的润滑条件差，磨损严重，应布置在低速级; 5）锥齿轮、斜齿轮宜放在高速级。 常见机械传动的主要性能见表2-1。 对初步选定的传动方案，在设计过程中还可能要不断地修改和完善。
表2-1 常见机械传动的主要性能 2.2 减速器的类型、特点及应用 减速器是原动机和工作机之间的独立的封闭传动装置。由于减速器具有结构紧凑、传动效率高、传动准确可靠、使用维护方便等特点，故在各种机械设备中应用甚广。 减速器的种类很多，用以满足各种机械传动的不同要求。其主要类型、特点及应用如表2-2所示。为了便于生产和选用，常用减速器已标准化，由专门工厂成批生产。标准减速器的有关技术资料，可查阅减速器标准或《机械设计手册》。因受某些条件限制选不到合适型号的标准减速