智能工程系统故障诊断与预测

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"《Intelligent Fault Diagnosis and Prognosis for Engineering Systems》是由G.Vachtsevanos, F.Lewis, M.Roemer, A.Hess和B.Wu合作撰写的一本关于智能工程系统故障诊断与预测的专业书籍，由John Wiley & Sons, Inc.于2006年出版。该书探讨了在电子、机械、航空航天等领域中，如何运用智能技术进行高效且准确的故障检测和预测。" 这本书深入讨论了智能故障诊断和预测的关键概念和技术，包括但不限于以下几个方面： 1. **智能系统基础**：书中可能涵盖了人工智能（AI）和机器学习的基础知识，这些是实现故障诊断和预测的核心。AI技术如神经网络、模糊逻辑、遗传算法和贝叶斯网络等，能帮助识别复杂系统的异常模式。 2. **数据采集与预处理**：在故障诊断中，数据的质量至关重要。作者可能讲解了如何从工程系统中收集有效数据，以及如何进行数据清洗、归一化和特征提取等预处理步骤。 3. **故障特征提取**：通过分析系统运行时的参数和信号，可以提取出故障的特征。这可能涉及到时间序列分析、模式识别和信号处理技术。 4. **故障模型建立**：利用智能算法构建故障模型，可以模拟系统在不同工况下的行为，以便在实际发生故障前进行预测。这一部分可能包含了系统建模、状态空间模型和动态模型的构建方法。 5. **故障诊断算法**：书中可能介绍了多种用于故障诊断的智能算法，如支持向量机（SVM）、决策树、随机森林等，并解释了它们在工程应用中的优势和局限性。 6. **故障预测与健康管理系统**：故障预测不仅仅是识别当前的故障状态，还包括对未来故障的估计。这部分内容可能涉及预测模型的建立和维护，以及如何通过预测结果来制定预防措施，实施健康管理。 7. **案例研究**：为了使理论更具实践性，作者可能会通过实际工程案例来演示智能故障诊断和预测的方法，涵盖不同领域的应用，如航空、电力系统、自动化设备等。 8. **伦理和法规考虑**：考虑到知识产权和法律法规，书中可能会提及在实际操作中如何合法合规地使用这些技术，以及如何保护数据隐私。 9. **未来趋势与挑战**：最后，作者可能讨论了智能故障诊断和预测领域的最新发展、技术挑战以及未来可能的研究方向。通过阅读这本书，读者不仅可以掌握智能故障诊断和预测的基本理论，还能获得实际应用的指导，提升解决工程问题的能力。对于从事相关领域工作的工程师、研究人员和学生来说，是一本宝贵的参考资料。

P.1 SOME BACKGROUND, NEEDS, AND IMPACTS xix

Change of Maintenance Philosophy

• On condition

• Opportunistic

• Not ‘‘on failure’’ nor ‘‘per schedule’’

• Less interruption of mission schedule

Reduction in Test Equipment (TE)

• Less intermediate and ﬂight-line TE

• 35 percent less peculiar support equipment during system design and

development (SDD)

• Eliminated O-level TE

Beneﬁts to the Maintainer

• Unprecedented insight into vehicle/squadron/ﬂeet health

• Less time spent on inspections

• Better ability to plan maintenance

• Simpliﬁed training

• Improved fault detection

Historically, such predictions would have been more of a black art than a

science. Essentially, prognosis provides the predictive part of a comprehensive

health management system and so complements the diagnostic capabilities

that detect, isolate, and quantify the fault, and prognosis, in turn, depends on

the quality of the diagnostic system.

From an operator’s, maintainer’s, or logistician’s—the user’s—point of

view, what distinguishes prognosis from diagnosis is the provision of a lead

time or warning time to the useful life or failure and that this time window

is far enough ahead for the appropriate action to be taken. Naturally, what

constitutes an appropriate time window and action depends on the particular

user of the information and the overall system of systems design one is trying

to optimize. Thus prognosis is that part of the overall PHM capability that

provides a prediction of the lead time to a failure event in sufﬁcient time for

it to be acted on.

What, then, constitutes a prognostic method—essentially any method that

provides a sufﬁcient answer (time window) to meet the user’s needs. This is

an inclusive approach and one that is seen as necessary to get the system of

systems-wide coverage and beneﬁts of prognosis. Some would argue that the

only true prognostic methods are those based on the physics of failure and

one’s ability to model the progression of failure. While this proposition has

scientiﬁc merit, such an unnecessarily exclusive approach is inappropriate

xx PROLOGUE

TABLE P.1 Some PHM System Functional Capabilities

Fault detection

Fault isolation

Advanced diagnostic technologies

Predictive prognostic technologies

Useful life remaining, time-to-failure predictions

Component life usage tracking

Performance degradation trending

False-alarm mitigation

Warrant guarantee tracking, tools, and information for enabling new business

practices

Health reporting

Only tells operator what needs to be known immediately

Informs maintenance of the rest

Aids in decision making and resource management

Fault accommodation

Information fusion and reasoners

Information management

Right information to right people at right time

given the current state of the art of prognostic technologies and the most

likely outcome of insufﬁcient coverage to provide system-wide prognosis.

Thus prognostic methods can range from the very simple to the very com-

plex. Examples of the many approaches are simple performance degradation

trending, more traditional life usage counting (cycle counting with damage

assessment models and useful life remaining prediction models), the physics

of failure-based, sometime sensor driven and probabilistic enhanced, incipient

fault (crack, etc) propagation models, and others.

Modern and comprehensive PHM systems include many types of func-

tional capabilities. Table P.1 lists most of these constituent functions, capa-

bilities, and associated processes. Many of these functional capabilities,

although much advanced and more accurate, are an evolution of capabilities

developed by legacy diagnostic systems. Through the philosophy and concept

of health management, the modern PHM system uses these functional capa-

bilities to complement each other and provide a much larger impact and a

broader set of maintenance-oriented beneﬁts than any single function by itself.

Although most of these functional capabilities will be developed together

and can complement each other during PHM system development for a par-

ticular platform, real predictive prognosis represents the new, hard, and often

‘‘risky’’ technology capability.

To understand the role of predictive prognosis, one has to understand the

relationship between diagnosis and prognosis capabilities. Envisioning an in-

itial fault to failure progression timeline is one way of exploring this rela-

tionship. Figure P.3 represents such a failure progression timeline. This

timeline starts with a new component in proper working order, indicates a

P.1 SOME BACKGROUND, NEEDS, AND IMPACTS xxi

Very early incipient

fault

Proper

Working

Order - New

Desire: Advanced Sensors

and Detection Techniques

to “see” incipient fault

Prognostics Diagnostics

Need: Understanding of fault to

failure progression rate

characteristics

Predicted useful life remaining

System, Component, or Sub-

Component Failure

Develop: Useful life

remaining prediction

models – physics and

statistical based

Secondary Damage,

Catastrophic Failure

Need: Better models to

determine failure effects

across subsystems

Determine effects on

rest of aircraft

The Goal is To Detect “State Changes” as Far to the Left As Poss ible

State Awareness Detection

Need: To Manage

Interaction between

Diagnostics and

Prognostics

Figure P.3 Failure progression timeline.

time when an early incipient fault develops, and depicts how, under continuing

usage, the component reaches a component or system failure state and even-

tually, if under further operation, reaching states of secondary system damage

and complete catastrophic failure.

Diagnostic capabilities traditionally have been applied at or between the

initial detection of a system, component, or subcomponent failure and com-

plete system catastrophic failure. More recent diagnostic technologies are en-

abling detections to be made at much earlier incipient fault stages. In order

to maximize the beneﬁts of continued operational life of a system or subsys-

tem component, maintenance often will be delayed until the early incipient

fault progresses to a more severe state but before an actual failure event. This

area between very early detection of incipient faults and progression to actual

system or component failure states is the realm of prognostic technologies.

If an operator has the will to continue to operate a system and/or com-

ponent with a known, detected incipient fault present, he or she will want to

ensure that this can be done safely and will want to know how much useful

life remains at any point along this particular failure progression timeline.

This is the speciﬁc domain of real predictive prognosis, being able to accu-

rately predict useful life remaining along a speciﬁc failure progression time-

line for a particular system or component.

To actually accomplish these accurate useful life remaining prediction ca-

pabilities requires many tools in your prognostic tool kit. Sometimes available

sensors currently used for diagnosis provide adequate prognostic state aware-

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