Fault Diagnosis Applications

byWriter •October 14, 2022

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Fault Diagnosis Applications

Introduction

Fault Diagnosis Applications

With increasing demands for efficiency and product quality and progressing integration of automatic control systems in high-cost and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role. The classical method of supervision is to check the limits of single variables and alarming of operators. However, this can be improved significantly by taking into account the information hidden in all measurements and by automatic actions to keep the systems in operation.

During the last few decades theoretical and experimental research has shown new ways to detect and diagnose faults. One distinguishes fault detection to recognize that a fault happened, and fault diagnosis to find the cause and location of the fault. Advanced methods of fault detection are based on mathematical signal and process models and on methods of system theory and process modeling 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 is a challenging field encompassing physical-oriented system theory, experiments and computations. The considered subjects are also known as condition monitoring, fault detection and isolation (FDI) or fault detection and diagnosis

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