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

Rotating machine faults are unavoidable; thus, early diagnosis is essential to avoid further damage to the machine or other machine attached to it. Various signal analysis based conventional techniques have been developed and used in the industries to identify various defects in the rotating machines. In last two decades, researchers have shifted their focus to automated or intelligent fault diagnosis based on Artificial Intelligence (AI) techniques due to a variety of issues in conventional fault analysis techniques, such as a dependence on machine operating circumstances, human interference, and expert abilities. In AI based techniques, various machine learning (ML) and deep learning (DL) techniques have been successfully applied for fault diagnosis of various rotating machines. From last half decade DL have been gaining popularity due to its attractive characteristic of automated feature learning and solving big data, unbalanced data, big computational burden and over-fitting problems of conventional ML techniques. Advances in DL methodologies have prompted interest in DL based intelligent fault diagnosis in the industry in the last five to 6 years. This review paper summarizes recent research and developments on DL based fault diagnosis in the last five to 6 years for various critical rotating machineries in industry such as electric motors, rotor-bearing systems, gear and gearbox, wind turbines, pumps, and compressors.

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“…Compressors are vital machinery in a variety of industry applicaton, therefore diagnosing and managing them is essential. 31…”

Section: Rotaing Machinery Faults and Conventional Monitoring Methodsmentioning

confidence: 99%

“…Compressors are vital machinery in a variety of industry applicaton, therefore diagnosing and managing them is essential. 31 Types of compressor faults. The compressor faults are classified as:…”

Section: Air Compressormentioning

confidence: 99%

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Gangsar¹,

Bajpei²,

Porwal³

2022

Noise & Vibration Worldwide

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“…Regarding rotating machines-with rotor-type mechanisms, there are various industrial components in which condition monitoring research focuses on, such as rolling [3,4] and journal bearings [5], gearboxes [6], shafts [7], blades [8], entire devices [9], wind turbines [10,11], reciprocating machines [12], electric motors [13], pumps [14], helicopters [15][16][17], fans [15], cam mechanisms [18,19], generators [20], and compressors [20].…”

Section: Introductionmentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

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“…The MD-based one-class classification methods construct the Mahalanobis space (MS), represented by the MD using only the normal signal data, and then determine whether a new signal sample belongs to the MS or not. On the other hand, typical binary classification methods such as support vector machines (SVM) and random forest (RF) need both normal data and abnormal data to train the models for detecting abnormal condition of the system [2][3][4][5]. Unfortunately, in practical industrial systems, the amount of the fault data that can be collected is extremely small.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System

2021

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Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed by analyzing signal data such as vibration and noise. In this study, to detect failures based on vibration data, preprocessing was performed using signal processing techniques such as the Hamming window and the cepstrum transform. After that, 10 statistical condition indicators were extracted to train the machine learning models. Specifically, two types of Mahalanobis distance (MD)-based one-class classification methods, the MD classifier and the Mahalanobis–Taguchi system, were evaluated in detecting the faults of rotating machinery. Their performance for fault detection on rotating machinery was evaluated with different imbalanced ratios of data by comparing with binary classification models, which included classical versions and imbalanced classification versions of support vector machine and random forest algorithms. The experimental results showed the MD-based classifiers became more effective than binary classifiers in cases in which there were much fewer defect data than normal data, which is often common in the real-world industrial field.

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

Cited by 24 publications

References 53 publications

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System

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