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research-article

AN INFORMATION FUSION MODEL BASED ON D-S EVIDENCE THEORY FOR EQUIPMENT DIAGNOSIS

[+] Author and Article Information
Dengji Zhou

Gas Turbine Research Institute, Shanghai Jiao Tong University, Shanghai, China
zhoudj@sjtu.edu.cn

Tingting Wei

Gas Turbine Research Institute, Shanghai Jiao Tong University, Shanghai, China
wei-tt@sjtu.edu.cn

Huisheng Zhang

Gas Turbine Research Institute, Shanghai Jiao Tong University, Shanghai, China
zhslm@sjtu.edu.cn

Shixi Ma

Gas Turbine Research Institute, Shanghai Jiao Tong University, Shanghai, China
mashixi@126.com

Fang Wei

AECC Commercial Aircraft, Engine Co., LTD, Shanghai, China
weifang1983@126.com

1Corresponding author.

ASME doi:10.1115/1.4037328 History: Received February 16, 2017; Revised July 08, 2017

Abstract

An abnormal operating effect can be caused by different faults, and a fault can cause different abnormal effects. An information fusion model, with hybrid-type fusion frame, is built in this paper, so as to solve this problem. This model consists of data layer, feature layer and decision layer, based on an improved D-S evidence algorithm. After the data preprocessing based on event reasoning in data layer and feature layer, the information will be fused based on the new algorithm in decision layer. Application of this information fusion model in fault diagnosis is beneficial in two aspects, diagnostic applicability and diagnostic accuracy. Additionally, this model can overcome the uncertainty of information and equipment to increase diagnostic accuracy. Two case studies are implemented by this information fusion model to evaluate it. In the first case, fault probabilities calculated by different methods are adopted as inputs to diagnose a fault, which is quite different to be detected based on the information from a single analytical system. The second case is about sensor fault diagnosis. Fault signals are planted into the measured parameters for the diagnostic system, to test the ability to consider the uncertainty of measured parameters. The case study result shows that the model can identify the fault more effectively and accurately. Meanwhile, it has good expansibility, which may be used in more fields.

Copyright (c) 2017 by ASME
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