Fault diagnosis based on grey relational analysis and synergetic pattern recognition for aero-engine gas-path systems

Zhang, Bangcheng; Chen, Jing; Yin, Xiaojing; Gao, Zhi

doi:10.1177/0954410019827657

Cited by 4 publications

(2 citation statements)

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“…In these conditions, degradation can be observed as a deviation in a set of performance parameters called health parameters. By determining the deviation of performance parameters from a normal level and examining the relationship between these deviations, gas path faults can be detected, isolated, and identified 3 . Commonly, the efficiency and flow capacity of the gas path components are considered as health index.…”

Section: Introductionmentioning

confidence: 99%

“…By determining the deviation of performance parameters from a normal level and examining the relationship between these deviations, gas path faults can be detected, isolated, and identified. 3 Commonly, the efficiency and flow capacity of the gas path components are considered as health index. These parameters are inherently unmeasurable and therefore need to be derived indirectly from measurable parameters.…”

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confidence: 99%

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Noise‐robust gas path fault detection and isolation for a power generation gas turbine based on deep residual compensation extreme learning machine

Nekoonam,

Montazeri‐Gh

2023

Energy Science & Engineering

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One of the major challenges facing fault diagnosis tools is their exposure to noise. The presence of noise may cause false alarms or the inability to detect a progressive fault in the early stages of its occurrence. Continuing previous efforts to address such a problem, in this paper, a noise‐robust diagnosis system for an industrial gas turbine is presented. The proposed structure employs a set of deep residual compensation extreme learning machines (DRCELMs). In this model, an optimal number of compensating blocks are trained to recover some of the lost useful information in the face of noise. Training and testing data required to develop the fault diagnosis model are generated by a performance model of the studied gas turbine. The t‐distributed stochastic neighbor embedding algorithm is employed for visualizing the gas path faults. Furthermore, the performance of the DRCELM is evaluated by comparing it with six other diagnosis models. The results indicate higher robustness of the DRCELM compared to other fault diagnosis systems. The proposed model presents a classification accuracy of >97% in noisy data and an accuracy of >98% in noise‐free data and combined data, while the average of fault positive rate and fault negative rate in noisy data is less than 2.5%.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%