Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory

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%.

Dependent evidence combination based on decision‐making trial and evaluation laboratory method

“…The direct relation is calculated as above. According to the procedures of the DEMATEL method, the direct relation matrix M is normalized in step 2 in Equation (7). And the total relation matrix is calculated in step 3 in Equation (8).…”

Aggregation of uncertainty data based on ordered weighting aggregation and generalized information quality

“…It can be concluded that GIQ is more complicated with more information. It should be mentioned that a possible extension of GIQ is to deal with belief function, which is widely used in many real applications…”

“…With the aid of the IQ, the fusion result will be more reasonable which is applied in fault diagnosis …”

“…[26][27][28][29] Recently, Yager proposed a new IQ, 30 which is applied in fault diagnosis. 31 However, the existing IQ only measures the uncertainty of the information itself, while the association of the collected data is not considered. To solve this problem, a generalized IQ (GIQ) of probability distribution is proposed in this paper.…”

A new matrix game with payoffs of generalized Dempster‐Shafer structures

“…The belief function is the main mathematical tool for describing the subjective uncertainty phenomenon, the uncertainty . Mainly exists in three basic types, namely the ambiguity, disharmony, and nonspecific…”