Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory
By Hepeng Zhang, Yong Deng. 2018
99 citations recorded by the source corpus.
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%.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Chau Yuen
- Yuren Zhou
- Meng Zhang
- Billy Pik Lik Lau
- U-Xuan Tan
- Marakkalage Sumudu Hasala
- Naveed Ul L. Hassan
In Information Systems
- Chau Yuen
- Yuren Zhou
- Meng Zhang
- Billy Pik Lik Lau
- U-Xuan Tan
- Marakkalage Sumudu Hasala
- Naveed Ul L. Hassan
Citations in the corpus
- A survey of data fusion in smart city applications (2019, background): “Nonetheless on the fault detection domain, machine fault detection can be found in [96], [123], where they describe the problem of fault diagnosis and apply data fusion techniques to overcome.”