Fault Feature Extraction of Reciprocating Compressor Based on Adaptive Waveform Decomposition and Lempel-Ziv Complexity

Tang, Youfu; Feng, Lin

doi:10.1109/access.2019.2923657

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…A new feature extraction technique was introduced by Tang and Lin (2019) to diagnose multiple fault conditions of the compressor using vibration parameters. An adaptive waveform decomposition (AWD) method was used to convert a non-stationary waveform into a stationary wave, and the signals were de-noised using threshold information value.…”

Section: Introductionmentioning

confidence: 99%

Prediction of air compressor condition using vibration signals and machine learning algorithms

Aravinth

Sugumaran

2022

Journal of Vibration and Control

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Air compressors are widely used equipment in the modern world for their tremendous utilization in applications of both domestic and industrial sectors. The inbuilt mechanical parts are often prone to various failures due to the complexity in the construction of air compressors that affects the overall system process. Hence, it is essential to devise a methodology to identify the failures at the early stages of its operation to avoid the major causalities due to process breakdown and system seizure. In this study, a single-acting single-stage reciprocating air compressor was chosen. The fault conditions like inlet valve fluttering, outlet valve fluttering, valve plate leakage, and check valve fault were considered. The statistical, histogram and autoregressive moving average features were extracted from the raw vibration signals. The most dominating features were selected using a decision tree algorithm and those features were classified using machine learning classifiers like Lazy K Star, Decorate, and radial basis function networks. The classifier Lazy K Star on autoregressive moving average feature exhibits the highest fault classification rate of 99.67% in classifying various compressor conditions and the results were compared and presented.

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Section: Introductionmentioning

confidence: 99%

Prediction of air compressor condition using vibration signals and machine learning algorithms

Aravinth

Sugumaran

2022

Journal of Vibration and Control

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“…Given these facts, various multiscale analysis methods are proposed consecutively to describe the complexity and uncertainty of signals at multiple scales, which can achieve richer and more comprehensive bearing fault information. The most common multiscale analysis methods include multiscale Lempel–Ziv complexity (MLZC) [ 10 ], multiscale sample entropy (MSE) [ 11 ], multiscale permutation entropy (MPE) [ 12 ] and multiscale fuzzy entropy (MFE) [ 13 ]. However, these methods also have some disadvantages.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine

Yan

Liu

Jia

2020

Sensors

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The vibration signal induced by bearing local fault has strong nonstationary and nonlinear property, which indicates that the conventional methods are difficult to recognize bearing fault patterns effectively. Hence, to obtain an efficient diagnosis result, the paper proposes an intelligent fault diagnosis approach for rolling bearing integrated symplectic geometry mode decomposition (SGMD), improved multiscale symbolic dynamic entropy (IMSDE) and multiclass relevance vector machine (MRVM). Firstly, SGMD is employed to decompose the original bearing vibration signal into several symplectic geometry components (SGC), which is aimed at reconstructing the original bearing vibration signal and achieving the purpose of noise reduction. Secondly, the bat algorithm (BA)-based optimized IMSDE is presented to evaluate the complexity of reconstruction signal and extract bearing fault features, which can solve the problems of missing of partial fault information existing in the original multiscale symbolic dynamic entropy (MSDE). Finally, IMSDE-based bearing fault features are fed to MRVM for achieving the identification of bearing fault categories. The validity of the proposed method is verified by the experimental and contrastive analysis. The results show that our approach can precisely identify different fault patterns of rolling bearings. Moreover, our approach can achieve higher recognition accuracy than several existing methods involved in this paper. This study provides a new research idea for improvement of bearing fault identification.

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“…Reciprocating compressors (RCs) are widely used in the petrochemical, refrigeration, and gas transportation industries [1][2][3]. The valve is the core part of RC, which determines the stable and efficient operation, and it is one of the most easily damaged parts in RC.…”

Section: Introductionmentioning

confidence: 99%

“…As one of the most commonly used FDM, vibration-based pattern recognition method has attracted significant attention in the field of rotating equipment fault diagnosis. However, the vibration characteristics of RC have non-linear, non-stationary, and multi-component coupling factors [3,9], which pose challenges for RC fault diagnosis. Generally, the FDM of RC can be divided into three categories: model-based, data-driven, and a combination of the two [2,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…The data types of data-driven method can be divided into single signal and multi-signal fusion. Single signal, such as vibration signal [3,18,[28][29][30], acoustic signal [31][32][33][34], current signal [24], [25], temperature signal [21], pressure signal [35], and instantaneous phase signal [36], etc., have been successfully applied to RC fault diagnosis. Although good diagnosis results have been achieved, the difficulty of data processing is increased because of the singleness of signal and the few effective fault features.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

et al. 2020

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High impact and strong noise complicate the response of reciprocating compressor (RC). It requires a complex signal processing method that is a single response-based or excitation-based fault diagnosis method applied to RC valve leakage fault diagnosis. This paper proposes a quantitative diagnosis method of RC valve leakage that is based on system characteristic diagnosis method. First, the current signal of the RC induction motor and the cylinder vibration signal are introduced as the excitation and response signals, the mathematical model of the RC motor current is established, and the influence mechanism of the valve leakage on the RC vibration is analyzed. Subsequently, the ensemble empirical mode decomposition and comb filter are respectively used to extract the fault characteristic information of excitation signal and response signal to obtain the excitation condition indicators (CIs), response CIs, and system CIs. Finally, the support vector machine based on the obtained CIs classified the valve leakage failure patterns of different severity, and a fault diagnoser was constructed for the quantitative diagnosis of valve leakage fault. The results of experiment and application proved that the proposed method could realize the quantitative diagnosis of RC valve leakage fault while using simple signal processing technology.

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Fault Feature Extraction of Reciprocating Compressor Based on Adaptive Waveform Decomposition and Lempel-Ziv Complexity

Cited by 12 publications

References 28 publications

Prediction of air compressor condition using vibration signals and machine learning algorithms

Prediction of air compressor condition using vibration signals and machine learning algorithms

A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine

Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

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