Gas path component fault diagnosis of an industrial gas turbine under different load condition using online sequential extreme learning machine

Montazeri-Gh, Morteza; Nekoonam, Ali

doi:10.1016/j.engfailanal.2022.106115

Cited by 21 publications

(10 citation statements)

References 36 publications

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“…The time-domain characteristic values of the vibration signal were calculated and analyzed, and six dimensioned parameter indexes were selected, including absolute mean, rms value, variance, peak-peak, skewness value and steepness value, a total of 5 dimensionless parameter indexes of peak index, margin index, pulse index, waveform index and steepness index, and four frequency domain characteristic parameter indicators including center of gravity frequency, mean square frequency and frequency standard deviation were selected to form the time frequency domain fault characteristics [11][12][13][14] .…”

Section: Fault Feature Extractionmentioning

confidence: 99%

Fault diagnosis of reciprocating compressor based on multi-scale permutation entropy

Jiang¹,

Zhang²,

Qiao³

2023

International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022)

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Reciprocating compressor is a kind of power machinery widely used in the industrial field, in order to achieve the condition monitoring of reciprocating compressor and fault diagnosis of key components, the reciprocating compressor vibration signal acquisition experimental platform is designed, and the mechanical performance of the compressor is monitored by means of vibration detection and signal analysis. In addition, a fault feature extraction method integrating time domain, frequency domain and entropy value is proposed, the fault feature extraction of the processed vibration signal is carried out, the extracted fault feature is used as input, and the compressor fault diagnosis is carried out by using the limit learning machine algorithm, and the results show that the method can better diagnose and identify the faults of different parts of the reciprocating compressor.

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Section: Fault Feature Extractionmentioning

confidence: 99%

Fault diagnosis of reciprocating compressor based on multi-scale permutation entropy

Jiang¹,

Zhang²,

Qiao³

2023

International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022)

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“…Since CF is a composite function involving both the SR and the number of sensors, the genetic algorithm aims to discover an optimal solution that maximizes the SR while minimizing the number of sensors. The mechanism of the VLGA‐ELM algorithm is further detailed in previous research 23 . The optimal parameters selected by VLGA‐ELM include GGT speed (NGG), compressor outlet pressure ( P 2 ), inlet pressure to the PT ( P 4 ), and PT output temperature ( T 5 ).…”

Section: Proposed Gas Path Fault Diagnosis Systemmentioning

confidence: 99%

“…Also, Montazeri‐Gh and Nekoonam applied a bank of online sequential ELMs (OSELM) to develop a gas path fault diagnosis system for a power generation gas turbine. This system could adaptively improve its performance when a change in loading conditions occurs 23 …”

Section: Introductionmentioning

confidence: 99%

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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“…13 Fentaye presented a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. 14 Montazeri-Gh and Nekoonam 15 applied a component fault diagnostic system based on a bank of online sequential extreme learning machines (OSELMs) that can be used for gas path fault diagnosis. Zhou et al solved the issue that the restricted window length may be incapable of capturing temporal patterns on a larger time scale by proposing the echo state KRLS algorithm.…”

Section: Introductionmentioning

confidence: 99%

Method of aeroengine fault diagnosis based on a Fingerprint map

Gou

Pan³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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The electrification and intelligentization trends of aeroengine systems present new challenges for the diagnostics of traditionally centralized control architectures. Scientific and practical fault diagnosis is of great significance for reducing aeroengine failures, improving operation efficiency, and ensuring safety. To realize accurate fault diagnosis, an aeroengine fault diagnosis method based on an improved fingerprint map is proposed in this paper. Based on aeroengine data analysis, ISTOA-SVM diagnostic model that embeds improved fingerprint map data as a priori knowledge is adopted to identify the fault sets. Data based on the statistics of the fingerprint map are processed with a ratio coefficient, and the feature parameters are appended through Floyd-principal component analysis (Floyd-PCA) to increase the identification data dimension. On the other hand, to improve the diagnostic performance of the classifier, the sooty terns optimization algorithm algorithm combined with chaotic maps, a variable helix mechanism and a separating operator is designed, and the ISTOA-SVM is constructed. The fault diagnosis method was validated by using a real-world dataset of aeroengines. Comparison experiments show that the proposed method is superior in performance over the state-of-the-art approaches in diagnostic precision under the constraint of limited data. The ISTOA-SVM addresses the issue that similar features may be confused in traditional fingerprint maps. The results are presented through 5 practical cases. The study demonstrates that the proposed method achieves superior performance and can be applied to aeroengine fault diagnosis.

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Gas path component fault diagnosis of an industrial gas turbine under different load condition using online sequential extreme learning machine

Cited by 21 publications

References 36 publications

Fault diagnosis of reciprocating compressor based on multi-scale permutation entropy

Fault diagnosis of reciprocating compressor based on multi-scale permutation entropy

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

Method of aeroengine fault diagnosis based on a Fingerprint map

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