An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN

Liu, Qian; Cai, Liuxi; Yu, Xiaoling; Ma, Haihui; Li, Yun; Shu, Yongqian

doi:10.3390/app12105182

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…This method also has advantages over other similar fault diagnosis methods, such as those of Li et al and Lv et al Li et al 22 carried out feature extraction on indicator diagram in a similar way to our study, but finally they only used it for fault identification of valve leakage degree. Lv et al 23 conducted digital image processing on the indicator diagram to obtain features and used it for fault identification of the valve and piston ring, with a classification accuracy of 97.9%. However, the state where multiple components fail simultaneously are not involved in this method, and the physical meaning of the extracted features was unclear.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Li et al 22 extracted four-dimensional features of pressure ratio, process angle coefficient, area coefficient and process index coefficient from indicator diagram, established a diagnostic model by principal component analysis and linear discriminant analysis, and used this method to realize the diagnosis of slight leakage caused by valve plate crack and serious leakage caused by valve plate deformation. Lv et al 23 used the feature extraction method used for image recognition to extract the comprehensive gradient of expansion curve and compression and curve vertical projection features in indicator diagram, and then input them into artificial neural network for classification. The classification accuracy reaches 97.9%.…”

Section: Introductionmentioning

confidence: 99%

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A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction

Zhu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part A:

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The damage of vulnerable components inside the cylinder of reciprocating compressor, including the valve, piston ring, packing and piston ring, will cause the unexpected shutdown of the compressor unit. The indicator diagram which reflects the thermodynamic process in the cylinder is suitable for fault diagnosis of vulnerable components. However, most of the published fault diagnosis methods based on indicator diagram are aimed at the fault diagnosis of gas valve. In addition, the extracted features lack physical meaning in most fault diagnosis methods using machine learning algorithm, which is not conducive to be widely applied in practical engineering. In this study, features with definite physical meaning, including average suction pressure, average discharge pressure, area of indicator diagram and centroid coordinates of indicator diagram, are extracted from indicator diagram, and the threshold database of features under normal states and various fault states is established according to the contrast experiment. The results of the experiment show that the thresholds of the extracted parameters are obviously different under normal states and various fault states. During fault diagnosis, several groups of indicator diagrams of the compressor to be diagnosed are collected at first. After feature extraction, the extracted features are compared with the thresholds under different compressor states to obtain the average numbers of features within the threshold range under different compressor states to determine the compressor states. The accuracy of the method for judging whether the compressor is faulty or normal could reach 98.3%. Furthermore, the accuracy of identifying individual faulty components and multiple faulty components could reach 86.86%. The reason for the low overall diagnostic accuracy is that certain faults have similar effects on the features extracted from indicator diagram. The proposed method is believed as an excellent fault diagnosis method for the vulnerable components inside the cylinder of reciprocating compressor.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction

Zhu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part A:

Self Cite

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“…The valve faults investigated in the reciprocating compressor were simulated by taking a faultless component and modifying its physical structure to create the seed of a fault in a controlled environment. This approach has been reported in the literature and used by several authors [ 5 , 52 , 53 ].…”

Section: Centrifugal Pump and Reciprocating Compressor Datasetsmentioning

confidence: 99%

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor

Medina,

Sánchez,

Cabrera

et al. 2024

Sensors

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Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

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“…Deep learning techniques such as CNN and LSTM are used for dynamic characteristic prediction [7,8], along with digital image processing and artificial neural networks for fault detection [9,10]. However, as a research focus, adaptive signal extraction has the advantages of data adaptation, mode independence, high accuracy and easy implementation and is the current research focus of signal fault feature extraction [11].…”

Section: Introductionmentioning

confidence: 99%

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

Zhao

Liu³

et al. 2023

Applied Sciences

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The accuracy and stability of the envelope estimation function are enduring issues throughout the research process of LMD. This paper presents double interpolation and mutation interval reconstruction local mean decomposition (DIMIRLMD) to improve the stability of the demodulation process and the accuracy of PF components. DIMIRLMD first proposes a mutation interval reconstruction envelope algorithm using extreme symmetry points to suppress the demodulation mutation phenomenon, which disturbs the stability of the demodulation process, and then selects the optimal PF component from a double interpolation PF component library based on the index of orthogonality (IO) for a better hierarchical property. DIMIRLMD was employed to analyze the simulation signal and vibration signal of a reciprocating compressor in an oversized bearing clearance state, and the results illustrate its performances are more excellent than those of three other LMD methods. Furthermore, the envelope frequency spectrum obtained from the proposed LMD presents a clear double rotation fault frequency and lower noise disturbance.

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An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN

Cited by 10 publications

References 15 publications

A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction

A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

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