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A Fault Diagnosability Evaluation Method for Dynamic Systems Without Distribution Knowledge.

Fangzhou Fu, Ting Xue, Zhigang Wu

IEEE Transactions on Cybernetics

|October 28, 2020

Summary

This summary is machine-generated.

This study introduces a quantitative method for evaluating fault diagnosability in dynamic systems, even without distribution knowledge. It uses Mahalanobis distance to assess fault detection and isolation capabilities, enhancing system reliability.

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Area of Science:

Control Engineering
System Dynamics
Fault Diagnosis

Background:

Dynamic systems require robust fault diagnosis for reliability.
Evaluating fault diagnosability quantitatively is crucial but challenging without distribution knowledge.

Purpose of the Study:

To develop a quantitative fault diagnosability evaluation for dynamic systems.
To propose methods applicable even when system distribution is unknown.

Main Methods:

Characterizing system dynamics using mean vectors and covariance matrices.
Defining fault detectability and isolability based on system characteristics.
Employing Mahalanobis distance (MD) for a fault diagnosability measure.
Developing model-based and data-driven algorithms for evaluation.
Incorporating ambiguity sets for result reliability.

Main Results:

A novel MD-based measure for fault diagnosability analysis.
Effective model-based and data-driven algorithms for evaluation.
Consideration of result reliability using ambiguity sets.

Conclusions:

The proposed methods provide a quantitative approach to fault diagnosability.
The algorithms are effective in evaluating dynamic systems without distribution knowledge.
The approach enhances the reliability assessment of fault diagnosis.