Abstract
A multi-objective optimal sensor placement method based on quantitative evaluation of fault diagnosability is proposed. Fault diagnosability evaluation is the basis of fault diagnosis, and insufficient sensor point information is the main reason for the low quantitative evaluation index of fault diagnosability. Therefore, a method to improve the fault diagnosability of a system by adding soft and hard sensors is presented. However, increasing sensors is limited by the constraints of cost, reliability and complexity. In view of this and based on ensuring the fault diagnosability, a multi-objective optimization method for the sensors is proposed to improve system reliability and promote development towards stability, efficiency and economy.
Highlights
With the development of modern engineering systems towards scale, integration and complexity, the risk of failure increases
The research shows that low fault diagnosability is one of the main reasons leading to a high failure rate for systems
Literatures [6], [7] attempted to study diagnosability evaluation without relying on fault diagnosis algorithms, but these methods are deficient in two aspects: one is that they can only give qualitative analysis results and lack clear quantitative indicators, i.e. the degree of fault diagnosability; the other is that they do not consider uncertainties such as noise of non-linear systems, which seriously affects the accuracy of evaluation results
Summary
With the development of modern engineering systems towards scale, integration and complexity, the risk of failure increases . Li: Multi-Objective Optimal Placement of Sensors Based on Quantitative Evaluation of Fault Diagnosability which makes it difficult to accurately reflect the real diagnosable performance of the system. Literatures [6], [7] attempted to study diagnosability evaluation without relying on fault diagnosis algorithms, but these methods are deficient in two aspects: one is that they can only give qualitative analysis results and lack clear quantitative indicators, i.e. the degree of fault diagnosability; the other is that they do not consider uncertainties such as noise of non-linear systems, which seriously affects the accuracy of evaluation results. This paper explores an effective method to quantitatively evaluate the diagnosability of faults in non-linear systems. It is undoubtedly feasible to quantitatively evaluate the diagnosability of faults using KLD, which can measure the diversity of a multivariate distribution when different faults occur
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