A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

Ali, Salah M.; Hui, Kar Hoou; Hee, Lim Meng; Leong, M. Salman; Al-Obaidi, M. A.; Ali, Yasir Hassan; Abdelrhman, Ahmed M.

doi:10.1088/1757-899x/328/1/012032

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…They proposed two AI models to detect the valve condition in a reciprocating compressor based on several AE signals using SVM and ANN [63,64]. In the literature [65], the ANN and SVM models were trained and evaluated for detection of valve faults in an RC. The results showed that the accuracy of the ANN and SVM detection methods were similar, but the SVM had better ability of handling a large number of input features with low sampling datasets.…”

Section: Fault Diagnosis Based On Acoustic Emission (Ae)mentioning

confidence: 99%

Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Liu

et al. 2021

Processes

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Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed.

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Section: Fault Diagnosis Based On Acoustic Emission (Ae)mentioning

confidence: 99%

Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Liu

et al. 2021

Processes

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“…In this paper, seven AE signal parameters will be use to develop the valve condition identification model. Multivariate analysis approach was used to nominate the most sensitive parameter to the compressor operational conditions by the author's previous work [23][24][25] .…”

Section: Counts Durationmentioning

confidence: 99%

Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

et al. 2019

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Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future.

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“…Basically, intelligent fault diagnosis techniques based on machine monitoring parameters such as temperature, acoustics, vibration, pressure, current are extensively adopted to detect early defects by sensing any change in those parameters. Typically, vibration analysis is frequently regarded and used since vibration signals are not intrusive in the operation of machinery (Ali et al, 2018a). For instance, several works (Afia et al, 2018, 2019, 2020b, 2022; de Oliveira et al, 2022; Gougam et al, 2018, 2020a, 2020b, 2021; Lin et al, 2022; Touzout et al, 2020; Zhu et al, 2022) involve gear and bearing faults detection, identification, and classification in speed reducers and wind turbines are based on vibration data analysis.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, several works (Afia et al, 2018, 2019, 2020b, 2022; de Oliveira et al, 2022; Gougam et al, 2018, 2020a, 2020b, 2021; Lin et al, 2022; Touzout et al, 2020; Zhu et al, 2022) involve gear and bearing faults detection, identification, and classification in speed reducers and wind turbines are based on vibration data analysis. However, for air compressor condition monitoring, acoustic recordings and acquisition using acoustic sensors appear to be more advantageous and reliable than vibration monitoring as given in the works by Ali et al (2016, 2018a, 2018b). Information produced by vibration sensors still appear to be inefficient mainly due to the complexity of reciprocating air compressors.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault classification of air compressors using Harris hawks optimization and machine learning algorithms

Afia

Gougam

Rahmoune

et al. 2023

Transactions of the Institute of Measurement and Control

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Due to their complexity and often harsh working environment, air compressors are inevitably exposed to a variety of faults and defects during their operation. Thus, condition monitoring is critically required for early fault recognition and detection to avoid any type industrial failures. In this paper, an intelligent algorithm for reciprocating air compressor fault diagnosis is developed using real-time acoustic signals acquired from an air compressor with one healthy and seven different faulty states such as leakage inlet valve (LIV), leakage outlet valve (LOV), non-return valve (NRV), piston ring, flywheel, rider-belt and bearing defects. The proposed algorithm mainly consists of three steps: feature extraction, selection, and classification. For feature extraction, experimental acoustic signals are decomposed using maximal overlap discrete wavelet packet transform (MODWPT) by six levels into 64 wavelet coefficients (nodes). Thereafter, time domain features are calculated for each node to build each air compressor’s health state feature matrix. Each feature matrix dimension is reduced by selecting the most useful features using Harris hawks optimization (HHO) in the feature selection step. Finally, for feature classification, selected features are used as inputs for random forest (RF), ensemble tree (ET) and K-nearest neighbors (KNN) to detect, identify, and classify the compressor health states with high classification accuracy. Comparative studies with several feature extraction and selection methods prove the proposed approach’s efficiency in detecting, identifying, and classifying all air compressor faults.

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A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

Cited by 9 publications

References 13 publications

Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

Intelligent fault classification of air compressors using Harris hawks optimization and machine learning algorithms

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