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Research Papers

An Information Fusion Model Based on Dempster–Shafer Evidence Theory for Equipment Diagnosis

[+] Author and Article Information
Dengji Zhou, Tingting Wei, Huisheng Zhang, Shixi Ma

School of Mechanical Engineering,
Gas Turbine Research Institute,
Shanghai Jiao Tong University,
Shanghai 200240, China

Fang Wei

AECC Commercial Aircraft,
Engine Co., Ltd.,
Shanghai 200241, China

1Corressponding author.

Manuscript received February 16, 2017; final manuscript received July 8, 2017; published online October 4, 2017. Assoc. Editor: Michael Beer.

ASME J. Risk Uncertainty Part B 4(2), 021005 (Oct 04, 2017) (8 pages) Paper No: RISK-17-1026; doi: 10.1115/1.4037328 History: Received February 16, 2017; Revised July 08, 2017

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 Dempster–Shafer (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.

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References

Figures

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Fig. 1

Relationship between belief function and plausibility function of event A

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Fig. 2

Diagnostic process without information fusion

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Fig. 3

Design steps of fusion diagnostic

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Fig. 4

Configuration of target gas turbine

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Fig. 5

Fusion frame of intake icing diagnosis

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Fig. 6

Fusion frame A of sensor fault diagnosis

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Fig. 7

Fusion frame B of sensor fault diagnosis

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Fig. 8

Curve for ambient temperature

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Fig. 9

Curve for dew point temperature

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Fig. 10

m1 of intake icing fault diagnosis

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Fig. 11

m2 of intake icing fault diagnosis

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Fig. 12

m3 of intake icing fault diagnosis

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Fig. 13

Diagnostic result based on information fusion of intake icing fault diagnosis

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Fig. 14

Measurement diagnostic result without sensor faults based on method A

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Fig. 15

Measurement diagnostic result without sensor faults based on method B

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Fig. 16

Implantable measuring errors of compressor outlet temperature sensor

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Fig. 17

Diagnostic result of compressor discharge temperature with bias

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Fig. 18

Diagnostic result of compressor discharge temperature with drift

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