Diagnosis and Fault-tolerant Control 1. Группа авторов

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Название Diagnosis and Fault-tolerant Control 1
Автор произведения Группа авторов
Жанр Зарубежная компьютерная литература
Серия
Издательство Зарубежная компьютерная литература
Год выпуска 0
isbn 9781119882312



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are as follows (Basseville and Nikiforov 1993; Chen and Patton 1999):

      1 1) probabilistic reasoning;

      2 2) possibilistic reasoning with fuzzy logic;

      3 3) reasoning with artificial neural networks.

      This very short consideration shows that many different methods have been developed over the last 30 years. It is also clear that many combinations of them are possible.

      On the basis of different contributions during the last 30 years, it can be stated that parameter estimation and observer-based methods are the most frequently applied techniques for fault detection, especially for the detection of sensor and process faults. Nevertheless, the importance of neural network-based and combined methods for fault detection is steadily growing. In most applications, fault detection is supported by simple threshold logic or hypothesis testing. Fault isolation is often carried out using classification methods. For this task, neural networks are being more and more widely used.

      The number of applications using nonlinear models is growing, while the trend of using linearized models is diminishing. It seems that analytical redundancy-based methods have their best application areas in mechanical systems where the models of the processes are relatively precise. Most nonlinear processes under investigation belong to the group of thermal and fluid dynamic processes. The field of applications to chemical processes has few developments, but the number of applications is growing. The favorite linear process under investigation is the DC motor. In general, the trend is changing from applications to safety-related processes with many measurements, as in nuclear reactors or aerospace systems, to applications in common technical processes with only a few sensors. For diagnosis, classification and rule-based reasoning methods are the most important, and the use of neural network classification as well as fuzzy logic-based reasoning is growing.

      I.9. FDI application report

Application Number of contributions
Simulation of real processes 105
Large-scale pilot processes 94
Small-scale laboratory processes 68
Full-scale industrial processes 98
Fault type Number of contributions
Sensor faults 129
Actuator faults 111
Process faults 123
Control loop or controller faults 48
Method type Number of contributions
Observer 123
Parity space 74
Parameter estimation 101
Frequency spectral analysis 57
Neural networks 79
Evaluation method Number of contributions
Neural networks 89
Fuzzy logic 65
Bayes classification 54
Hypothesis testing 48
Reasoning strategy Number of contributions
Rule based 40
Sign directed graph 33
Fault symptom tree 32
Fuzzy logic 66