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With increasing demands for efficiency and product quality and progressing integration of automatic control systems in high-cost mechatronic and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role. The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, the reliability, availability, safety and systems integrity of technical processes is considered. Then fault-detection methods for single signals without models like limit and trend checking and with harmonic and stochastic models, like Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals like parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems. Especially for safety-critical processes fault-tolerant systems are required. Basic redundant structures like n-out-of-m systems, cold and hot standby are considered and ways to design fault-tolerant sensors, actuators and control systems are outlined. Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire show applications.
- Sales Rank: #2455961 in eBooks
- Published on: 2006-01-16
- Released on: 2006-01-16
- Format: Kindle eBook
From the Back Cover
Supervision, health-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles, in order to improve reliability, availability, maintenance and life-time. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity.
This book gives an introduction into the field of fault detection, fault diagnosis and fault-tolerant systems with methods which have proven their performance in practical applications. It guides the reader in a structured tutorial style:
- supervision methods, reliability, safety, system integrity and related terminology;
- fault detection with signal-based methods for periodic and stochastic signals;
- fault detection with process model-based methods like parameter estimation, state estimation, parity equations and principal component analysis;
- fault diagnosis with classification and inference methods;
- fault-tolerant systems with hardware and analytical redundancy;
- many practical simulation examples and experimental results for processes like electrical motors, pumps, actuators, sensors and automotive components;
- end-of-chapter exercises for self testing or for practice.
The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for practising engineers.
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5 of 6 people found the following review helpful.
Review of Fault Diagnosis Systems by Silvio Simani
By SIMANI SILVIO
The field of supervision, fault detection and diagnosis plays an important role due to the increasing demands for efficiency and product quality and progressing integration of automatic control systems in high-cost and safety-critical processes. The traditional strategy of supervision is to check the limits of the monitored variable for the process under investigation and alarming the operators. However, this can be improved significantly by taking into account the information hidden in all measurements and automatic actions to keep the systems in operation.
During the last thirty years, theoretical and experimental research has shown new ways to detect and diagnose faults. Fault detection consists of recognising that a fault has happened, whilst fault diagnosis regards the location of the fault and its identification. Advanced methods of fault detection are based on mathematical signal and process models and on methods of system theory and process modelling to generate fault symptoms. Fault diagnosis methods use causal fault-symptom-relationships by applying methods from statistical decision, artificial intelligence and soft computing. Therefore, efficient supervision, fault detection and diagnosis are challenging fields by encompassing physical oriented system theory, experiments and computations. A further important field is fault management. This means to avoid shutdowns by early fault detection and actions like process condition-based maintenance or repair. If sudden faults, failures or malfunctions cannot be avoided, fault-tolerant systems are required. Through methods of fault detection and reconfiguration of redundant components, breakdown and in the case of safety-critical processes accidents may be avoided.
The book treats these advanced supervision, fault detection and diagnosis methods by considering five parts, i.e. fundamentals, fault-detection methods, fault-diagnosis approaches, fault-tolerant systems, and application examples.
In more details, the first and the second chapters, which belong to the first part of the book, describe the basic tasks of supervision, monitoring, automatic protection, fault detection and fault diagnosis up to fault management. As the treated subject is distributed over many different technological areas, the used terminology is not unique. Therefore an attempt is made to give definitions to frequently used terms like faults, failures, malfunctions, reliability, availability, safety, dependability and integrity, with reference to international standards. One goal of advanced supervision is the improvement of reliability, availability and maintainability. Therefore some basics are summarized in Chapter 3. Measures regarding, for example, the reliability like failure rate, and for maintainability and availability are given together with numerical examples. For safety related systems, special analysis and synthesis methods are required, which are covered by the terms safety, system integrity and dependability, and presented in Chapter 4. In this chapter, a brief summary is given of event tree analysis, fault tree analysis, failure mode and effects analysis (FMEA), and hazard analysis and risk classification.
The second part of the book treats the basic fault-detection methods. As advanced methods of fault detection are using mathematical process and signal models, Chapters 5 and 6 describe some basic continuous-time and discrete-time models. An important issue in this connection is the mathematical modelling of faults. Different kinds of frequently used fault models, different time behaviour and their influence on process models is discussed. Examples are given how faults can be modelled for actuators, processes and sensors. Static and dynamic process models are considered and it is shown how additive (offset) and multiplicative (parametric) faults influence the measurable signals. Then some models for periodic and stochastic signals are given, which are suitable for fault detection with signal-analysis methods. Chapters 7 and 8 treat fault-detection methods based on the measurement of single signals. In particular, Chapter 7 gives a survey on the most frequently used way for fault detection, the limit checking. This is usually applied for measurable absolute values and their trends. Then, more sophisticated change-detection methods are considered. A basic method consists of the real-time estimation of the mean and variance of observed stochastic variables. Also statistical tests are discussed. Moreover, fuzzy thresholds, adaptive thresholds and plausibility checks are also described. The fault detection with signal models in Chapter 8 considers first periodic signals. Classical methods like Fourier and correlation analysis including FFT and spectral estimation are summarized, followed by the identification of non-stationary periodic signals with short-time Fourier transform and wavelet transform and the identification of stochastic signals. The goal is to detect changes of the signal behaviour caused by process faults. The following chapters treat the fault detection with process models. As faults may change the behaviour of processes between input and output signals, changes in the behaviour of the processes can be used to indicate inherent faults, which are not directly measurable. Therefore attempts are made to extract changes in the process behaviour by using several measurements. This also means that "analytical redundancy" between measured signals is used, expressed by process models. Chapter 9 considers fault detection with process-identification methods. Here the process models adapt to the individual process behaviour by using cross-correlation or parameter estimation. Especially the recursive least squares parameter estimation method including their modifications is described for linear time-invariant and time-variant processes with discrete-time and continuous-time signals. A great advantage is that powerful methods exist for the identification of nonlinear processes because most real processes are nonlinear. Parameter estimation for static and dynamic non-linear processes is considered and an extract for applicable neural networks for static and dynamic systems and implementation as look-up tables is given. Fault symptoms then reflect as parameters or output signal deviations. The methods of parity equations are using fixed process models, as described in Chapter 10. They can be designed with transfer functions leading to output or equation errors, which are called primary residuals, or with state-space models. In order to make the residuals more sensitive and robust to certain faults, enhanced residuals can be generated, giving the residuals special structures or directions. Depending of the process model structure and the kind of faults, strongly isolating or weakly isolating residuals can be distinguished. Further alternatives for model-based fault detection are state observers and state estimation, presented in Chapter 11. Changes in the input/output behaviour of a process lead to changes of the output error and state variables. Therefore they can be used as residuals. Enhanced residuals are obtained with fault-detection filters or bank of observers. Similar approaches for noisy processes are possible by state estimation with Kalman filters. Output or unknown input observers result from a transformation to new state variables and outputs such that unknown inputs have no influence on the residuals. A comparison of the computational form of the residual equations and simulations show similarities between the parity equation and observer-based methods. Chapter 12 is devoted to the fault detection of control loops. This chapter is special, since detuned controller parameters, actuator or sensor faults and some disturbances have a similar effect on the control performance, and it is rather difficult to detect and diagnose faults in closed loop. A comparison of model-based fault-detection is shown in Chapter 14 by considering the assumptions made and simulations. The suitability of the individual methods for special types of faults is discussed and certain combinations are proposed. Chapter 13 gives an introduction to the principal component analysis which under the assumption of linearity, analyses the fluctuations of input and output variables of large scale processes and reduces the number of variables to those being uncorrelated while preserving most of the information. Changes of the new variables are then used to form residuals.
The third part of the book provides an overview of most important fault-diagnosis methods. Based on the symptoms of the fault-detection methods, with binary or fuzzy thresholds, and different kind of residuals or features, the task is to find the cause of the faults. The symptoms may be analytical or heuristic, which are observed by humans and expressed as linguistic terms, and exist in form of numbers, which are calculated. After an introduction into the basic problems in Chapter 15, fault diagnosis with classification methods is described, from classical pattern recognition, geometric classifiers to neural networks, as shown in Chapter 16. With more information, fault trees can be established, allowing inference methods for approximate reasoning, forward and backward chaining, as described in Chapter 17. Hybrid neuro-fuzzy systems then are used to identify fault trees with if-then rules.
Fault-tolerant systems, which tolerate appearing faults, are treated in the fourth part of the book. Unfortunately, this section of the book is quite short and not very well developed, compared with the previous parts of the monograph. For example, Chapter 18 presents the fault-tolerant design with basic static and dynamic redundant structures, e.g. with voters, hot and cold standby. The various degradation steps with states of fail safe, fail operational and fail silent are considered. Then it is shown how very simple and basic fault-tolerant sensors with hardware or analytical redundancy and fault-tolerant actuators can be built. Finally, fault-tolerant components and fault-tolerant control systems are briefly discussed in Chapter 19. The last two chapters consider parts of a general fault management.
The last fifth part of the book shows some application examples for model-based fault detection in detail. Experimental results are shown for a DC motor, in Chapter 20, an AC motor driven centrifugal pump, in Chapter 21, and an automotive suspension, in Chapter 22.
Summing up, after a summary of basics in the area of reliability and safety the chapters describe in the form of an introduction basic methods for fault detection, fault diagnosis and a quite brief treatment of general fault management. These methods can be applied to many different types of actuators, processes and sensors in open or closed loop.
The book is intended to give an overview of advanced supervision, fault detection and diagnosis and to provide a brief and basic introduction to fault-tolerant systems. The main feature of the book is to present an application-oriented approach with methods which have proven their performance in practical applications, deriving from many research projects during the last 25 years.
The book is dedicated as an introduction in teaching the field of fault detection and diagnosis, and fault-tolerant systems for graduate students or students of higher semesters of electrical and electronic engineering, mechanical and chemical engineering and computer science. Since the treated field is in a phase of increasing importance for technical and also non-technical systems, it has been tried to present the material in an easy to understand and transparent way and with realistic perspectives for the application of the treated and discussed methods. Finally, the book is also oriented towards practising engineers in research and development, design and manufacturing.
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