Sensor Fault Identification Using Weighted Combined Contribution Plots

Alawi, Ahmed; Choi, Sang Wook; Martin, E.B.; Morris, Julian

doi:10.3182/20060829-4-cn-2909.00151

Cited by 2 publications

(1 citation statement)

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“…In order to make the equipment return to normal operation, we must find the roots of fault accurately. The traditional variable contribution plot method [26,27,43] has a certain ability of fault identification, which can compute and compare the contribution indexes of process variables. Process variable which has the highest contribution to the monitoring statistic index can be identified in general, and this variable is the most possible reason of fault.…”

Section: Fault Diagnosis Methods Based On Knn Reconstructionmentioning

confidence: 99%

Fault diagnosis using kNN reconstruction on MRI variables

Wang

Liu

2015

Journal of Chemometrics

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In industrial processes, investigating the root causes of abnormal events is a crucial task when process faults are detected; isolating the faulty variables provides additional information for investigating the root causes of the faults. The traditional contribution plot is a popular and perspicuous tool to isolate faulty variables. However, this method can only determine one faulty variable (the biggest contributor) when there are several variables out of control at the same time. In the presented work, a novel fault diagnosis method is derived using k-nearest neighbor (kNN) reconstruction on maximize reduce index (MRI) sensors; it is aimed at identifying all fault variables precisely. This method can identify the faulty variables effectively through reconstructing MRI variables one by one. A numerical example focuses on validating the performance of kNN missing data analysis method firstly, then multi-sensors fault identification results are also given. Tennessee Eastman process is provided to demonstrate that the proposed approach can identify the responsible variables for the multiple sensors fault. Figure 1. Flow chart of the proposed method. 400wileyonlinelibrary.com/journal/cem

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Section: Fault Diagnosis Methods Based On Knn Reconstructionmentioning

confidence: 99%