Abstract

Fault diagnosis techniques can be classified into passive and active types. Passive approaches only utilize the original input and output signals of the system. Because of the small amplitudes, the characteristics of incipient faults are not fully represented in the data of the system, so it is difficult to detect incipient faults by passive fault diagnosis techniques. In contrast, active methods can design auxiliary signals for specific faults and inject them into the system to improve fault diagnosis performance. Therefore, active fault diagnosis techniques are utilized in this article to detect and isolate incipient faults based on the fault structure. A new framework based on observer approach for active fault diagnosis is proposed and the geometric approach based fault diagnosis observer is introduced to active fault diagnosis for the first time. Based on the dynamic equations of residuals, auxiliary signals are designed to enhance the diagnosis performance for incipient faults that have specific structures. In addition, the requirements that auxiliary signals need to meet are discussed. The proposed method can realize the seamless combination of active fault diagnosis and passive fault diagnosis. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach, and it is indicated that the proposed method significantly improves the accuracy of the diagnosis for incipient faults.

Highlights

  • With the progress of science and technology, modern industrial systems have become more and more complex, and that leads to higher security risks

  • The designed auxiliary signals can enhance the performance of faults in the residuals and improve the diagnosis performance for incipient faults

  • It is worth noting that the observer only uses the original input uk and the system output data yk, and does not directly use the designed auxiliary signal ak

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Summary

Introduction

With the progress of science and technology, modern industrial systems have become more and more complex, and that leads to higher security risks. Fault diagnosis techniques can be divided into passive and active types [10]. Passive fault diagnosis (PFD) approaches only utilize the original input and output signals of the system, and, when the fault characteristics are not fully represented in the data, the diagnosis performance is not good. Due to the existence of system noise or uncertainty, system changes caused by incipient faults with small amplitudes are indistinguishable from system changes caused by system noise or uncertainty. The feedback controller in closed-loop system will cover up or compensate the system abnormality caused by fault to a certain extent, which will lead to the poor performance of passive fault diagnosis. In contrast to PFD, active fault diagnosis (AFD) will actively design auxiliary signals according to the fault modes

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