Abstract
Fault diagnosis is a problem processing variable information obtained from different sources in nature. Evidence theory, efficient to deal with information viewed as evidence, is widely used in fault diagnosis. However, a shortcoming of the existing fault diagnosis methods only gets probability distribution rather than the basic probability assignment. A novel method of generating basic probability assignment that takes information quality into account is proposed. The probability distribution is determined by the preliminary matrix and sampling matrix that are constructed by sensor data. And the quality of probability distribution is taken as the discount factor and the rest of belief is assigned to the universal set. Hence, the basic probability assignment is obtained. Then, basic probability assignment can be combined with Dempster and Shafer evidence theory to determine the status of the engine. An application of engine fault is shown to illustrate the practicability of the proposed method. Then by comparing the result of the method which takes information quality into account (the proposed method) and does not do it, the former is better than the latter. Finally, the reliability analysis shows that the proposed method has strong reliability because performance accuracy is 100% when the error rate is less than 10%.
Highlights
Fault diagnosis is important to system to correct timely and work smoothly
The discount factor obtained from the Shannon entropy, representing information quality, is used to construct basic probability assignments
A new method that entropy of each probability obtained from each feature is used as the discount factor to get the basic probability assignment is proposed
Summary
Fault diagnosis is important to system to correct timely and work smoothly. Up to now, fault diagnosis has been applied extensively to all kinds of profession, such as mechanics,[1,2,3,4] chemistry,[5] nucleus,[6] and electric.[7]. A novel dissolved gas analysis (DGA) method[62] for power transformer incipient fault diagnosis based on integrated adaptive neuro fuzzy inference system (ANFIS) and Dempster–Shafer theory (DST) is presented. A new transformer fault diagnosis method[66] based on a wavelet neural network optimized by adaptive genetic algorithm (AGA) and an improved D-S evidence theory fusion technique is proposed. To handle the above issues, in this article, a new method that Shannon entropy[68,69] of each probability obtained from each feature is used as discount factor[70,71,72] to acquire the basic probability assignment is proposed. In section ‘‘Proposed method,’’ we present the frame of discernment and the new evidence combination. AY where m(A) represents how strongly the evidence supports A
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