A Novel Method for Fault Diagnosis of Rotating Machinery

Tang, Meng; Liao, Yaxuan; Luo, Fan; Li, Xiangshun

doi:10.3390/e24050681

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Therefore, it holds great potential for application in the filed of diagnosing faults in rotating machinery. For example, Tang et al [78] utilized GA to determine the optimal embedding dimension and class number for composite dispersion entropy, as well as to optimize relevant parameters, resulting in significantly improved fault diagnosis efficiency. Tang et al [79] proposed a fault diagnosis method for double DBN bearings based on QGA and utilized quantum genetic algorithm (QGA) to optimize the parameters of Bi-DBN.…”

Section: Population-based Heuristicsmentioning

confidence: 99%

A survey on fault diagnosis of rotating machinery based on machine learning

Wang,

Huang,

Xiong

et al. 2024

Meas. Sci. Technol.

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With the booming development of modern industrial technology, rotating machinery fault diagnosis is of great significance to improve the safety, efficiency and sustainable development of industrial production. Machine learning as an effective solution for fault identification, has advantages over traditional fault diagnosis solutions in processing complex data, achieving automation and intelligence, adapting to different fault types, and continuously optimizing. It has high application value and broad development prospects in the field of fault diagnosis of rotating machinery. Therefore, this article reviews machine learning and its applications in intelligent fault diagnosis technology and covers advanced topics in emerging deep learning techniques and optimization methods. Firstly, this article briefly introduces the theories of several main machine learning methods, including Extreme Learning Machines (ELM), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Deep Belief Networks (DBN) and related emerging deep learning technologies such as Transformer, adversarial neural network (GAN) and graph neural network (GNN) in recent years. The optimization techniques for diagnosing faults in rotating machinery are subsequently investigated. Then, a brief introduction is given to the papers on the application of these machine learning methods in the field of rotating machinery fault diagnosis, and the application characteristics of various methods are summarized. Finally, this survey discusses the problems to be solved by machine learning in fault diagnosis of rotating machinery and proposes an outlook.

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Section: Population-based Heuristicsmentioning

confidence: 99%

A survey on fault diagnosis of rotating machinery based on machine learning

Wang,

Huang,

Xiong

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…It requires regular monitoring of all system components, including electrical machines. Electrical machines operating increasingly under severe conditions or at the limits of their capacity are subject to faults that can lead to failures [ 1 , 2 , 3 ]. Investigations in several industrial fields have revealed that rolling bearing elements (RBEs) are the main sources of failures for almost

of low- to high-power machines [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features

Mezni

Delpha

Braham

2022

Entropy

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Among the existing bearing faults, ball ones are known to be the most difficult to detect and classify. In this work, we propose a diagnosis methodology for these incipient faults’ classification using time series of vibration signals and their decomposition. Firstly, the vibration signals were decomposed using empirical mode decomposition (EMD). Time series of intrinsic mode functions (IMFs) were then obtained. Through analysing the energy content and the components’ sensitivity to the operating point variation, only the most relevant IMFs were retained. Secondly, a statistical analysis based on statistical moments and the Kullback–Leibler divergence (KLD) was computed allowing the extraction of the most relevant and sensitive features for the fault information. Thirdly, these features were used as inputs for the statistical clustering techniques to perform the classification. In the framework of this paper, the efficiency of several family of techniques were investigated and compared including linear, kernel-based nonlinear, systematic deterministic tree-based, and probabilistic techniques. The methodology’s performance was evaluated through the training accuracy rate (TrA), testing accuracy rate (TsA), training time (Trt) and testing time (Tst). The diagnosis methodology has been applied to the Case Western Reserve University (CWRU) dataset. Using our proposed method, the initial EMD decomposition into eighteen IMFs was reduced to four and the most relevant features identified via the IMFs’ variance and the KLD were extracted. Classification results showed that the linear classifiers were inefficient, and that kernel or data-mining classifiers achieved 100% classification rates through the feature fusion. For comparison purposes, our proposed method demonstrated a certain superiority over the multiscale permutation entropy. Finally, the results also showed that the training and testing times for all the classifiers were lower than 2 s, and 0.2 s, respectively, and thus compatible with real-time applications.

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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review

Bagri,

Tahiry,

Hraiba

et al. 2024

Vibration

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Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these components often leads to costly downtime and potential safety risks, further emphasizing the importance of monitoring their health state. Vibration signal analysis is now a common approach for this purpose, as it provides useful information related to the dynamic behavior of machines. This research aimed to conduct a comprehensive examination of the current methodologies employed in the stages of vibration signal analysis, which encompass preprocessing, processing, and post-processing phases, ultimately leading to the application of Artificial Intelligence-based diagnostics and prognostics. An extensive search was conducted in various databases, including ScienceDirect, IEEE, MDPI, Springer, and Google Scholar, from 2020 to early 2024 following the PRISMA guidelines. Articles that aligned with at least one of the targeted topics cited above and provided unique methods and explicit results qualified for retention, while those that were redundant or did not meet the established inclusion criteria were excluded. Subsequently, 270 articles were selected from an initial pool of 338. The review results highlighted several deficiencies in the preprocessing step and the experimental validation, with implementation rates of 15.41% and 10.15%, respectively, in the selected prototype studies. Examination of the processing phase revealed that time scale decomposition methods have become essential for accurate analysis of vibration signals, as they facilitate the extraction of complex information that remains obscured in the original, undecomposed signals. Combining such methods with time–frequency analysis methods was shown to be an ideal combination for information extraction. In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. Given the conclusions drawn, future researchers are urged to investigate the interpretability and integration of the diagnosis and prognosis models developed with the aim of applying them in real-time industrial contexts. Furthermore, there is a need for experimental studies to disclose the preprocessing details for datasets and the operational conditions of the machinery, thereby improving the data reproducibility. Another area that warrants further investigation is differentiation of the various types of fault information present in vibration signals obtained from bearings, as the defect information from the overall system is embedded within these signals.

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A Novel Method for Fault Diagnosis of Rotating Machinery

Cited by 3 publications

References 30 publications

A survey on fault diagnosis of rotating machinery based on machine learning

A survey on fault diagnosis of rotating machinery based on machine learning

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features

Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review

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