Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier

Rajabi, Saeed; Azari, Mehdi; Santini, Stefania; Flammini, Francesco

doi:10.1016/j.eswa.2022.117754

Cited by 81 publications

(21 citation statements)

References 40 publications

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“…In recent years, many traditional ML and DL methods have made significant progress as a result of their superior representation learning and pattern recognition capabilities, and they have also been successfully used for the predictive maintenance of industrial systems [7], [20], [21]. However, intelligent PdM systems based on traditional ML and DL have to satisfy specific conditions for achieving outstanding performance.…”

Section: Motivations For Applying Tl To Pdmmentioning

confidence: 99%

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

et al. 2023

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The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

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Section: Motivations For Applying Tl To Pdmmentioning

confidence: 99%

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

et al. 2023

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“…Permutation entropy, as a kind of information entropy, measures signal complexity through the probability of occurrence of permutation patterns. Multiscale permutation entropy analyses the permutation entropy of signals from multiple dimensions [9]. The specific algorithm is as follows: Assuming a sequentially…”

Section: Multiscale Permutation Entropy (Mpe)mentioning

confidence: 99%

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Feng

Liu

et al. 2023

J. Phys.: Conf. Ser.

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Aiming to extract efficiently the fault features of partial discharge in the process of fault diagnosis of power transformer, a method of combining Empirical Wavelet Transform (EWT) with Multiscale Permutation Entropy (MPE) is advanced to extract fault features of transformers partial discharge. Firstly, four different partial discharge pulse signals are analyzed by EWT method, and the fault signal is decomposed according to different frequency domain characteristics of the signal to obtain the intrinsic mode function (IMF) of the signal. Then, the calculated multi-scale permutation entropy of different IMFs to complete the fault feature extraction. Finally, the multi-scale entropy of the fault semaphore is used as the eigenvector of the Support Vector Machine (SVM) for glitch diagnosis, and the accurate systematization of the partial discharge semaphore of the transformer is realized. semaphore Compared with the Continuous Wavelet Transform (CWT), Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD) feature extraction way, it shows that the raised EWT-MPE is more valid and accurate in diagnosing and analyzing transformer partial discharge faults, and the accuracy of fault classification 96.43%.

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“…[ 7 , 8 , 9 ], and the application of fuzzy logic algorithms, neural networks, machine learning, and deep learning algorithms for fault diagnosis is described in refs. [ 10 , 11 , 12 , 13 , 14 ]. However, robustness and optimization of the rule table are two important challenges for fuzzy-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the performance of the fuzzy-based algorithm, neural network algorithms and neuro-fuzzy approaches are suggested in refs. [ 11 , 12 ]. In addition, the application of machine learning and deep learning algorithms for fault diagnosis is addressed in refs.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Using a Hybrid Fuzzy V-Structure Fault Estimator Scheme

Piltan

Kim

2023

Sensors

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Bearings are critical components of motors. However, they can cause several issues. Proper and timely detection of faults in the bearings can play a decisive role in reducing damage to the entire system, thereby reducing economic losses. In this study, a hybrid fuzzy V-structure fuzzy fault estimator was used for fault diagnosis and crack size identification in the bearing using vibration signals. The estimator was designed based on the combination of a fuzzy algorithm and a V-structure approach to reduce the oscillation and improve the unknown condition’s estimation and prediction in using the V-structure method. The V-structure surface is developed by the proposed fuzzy algorithm, which reduces the vibrations and improves the stability. In addition, the parallel fuzzy method is used to improve the robustness and stability of the V-structure algorithm. For data modeling, the proposed combination of an external autoregression error, a Laguerre filter, and a support vector regression algorithm was employed. Finally, the support vector machine algorithm was used for data classification and crack size detection. The effectiveness of the proposed approach was evaluated by leveraging the vibration signals provided in the Case Western Reserve University bearing dataset. The dataset consists of four conditions: normal, ball failure, inner fault, and outer fault. The results showed that the average accuracy of fault classification and crack size identification using the hybrid fuzzy V-structure fuzzy fault estimation algorithm was 98.75% and 98%, respectively.

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Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier

Cited by 81 publications

References 40 publications

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Bearing Fault Diagnosis Using a Hybrid Fuzzy V-Structure Fault Estimator Scheme

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