Fault diagnosis of various rotating equipment using machine learning approaches – A review

Manikandan, S.; Duraivelu, K

doi:10.1177/0954408920971976

Cited by 56 publications

(24 citation statements)

References 89 publications

(53 reference statements)

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“…In the dataset of naval propulsion system described in this paper, the input dimension of the dataset is small, the data volume of this dataset is small, and the output are continuous values. Therefore, the appropriate traditional CBM algorithms are Support Vector Regression (SVR) (Smola and Sch€ olkopf, 2004) and HMM, and the appropriate CBM algorithms based on deep learning are Adaboost (Freund and Schapire, 1997), RandomForest (Breiman, 2001) and Deep Neural Networks (DNN) (Manikandan and Duraivelu, 2021). These algorithms are overall modeled by relevant mathematical principles with the exception of DNN, so it is difficult to adjust the models for different systems without retraining their mathematical principles.…”

Section: Naval Propulsion System Dataset and Maintenance Methodsmentioning

confidence: 99%

DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system

Gao

Dong

Cui

et al. 2022

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PurposeThe purpose of this paper is to propose a new deep learning-based model to carry out better maintenance for naval propulsion system.Design/methodology/approachThis model is constructed by integrating different deep learning algorithms. The basic idea is to change the connection structure of the deep neural network by introducing a residual module, to limit the prediction output to a reasonable range. Then, connect the Deep Residual Network (DRN) with a Generative Adversarial Network (GAN), which helps achieve data expansion during the training process to improve the accuracy of the assessment model.FindingsStudy results show that the proposed model achieves a better prediction effect on the dataset. The average performance and accuracy of the proposed model outperform the traditional models and the basic deep learning models tested in the paper.Originality/valueThe proposed model proved to be better performed naval propulsion system maintenance than the traditional models and the basic deep learning models. Therefore, our model may provide better maintenance advice for the naval propulsion system and will lead to a more reliable environment for offshore operations.

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Section: Naval Propulsion System Dataset and Maintenance Methodsmentioning

confidence: 99%

DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system

Gao

Dong

Cui

et al. 2022

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“…In the recent years, DL has emerged as an advent in several fields like healthcare, image processing, etc. The major benefits of DL have been seen in image recognition domain [13][14][15]. DL can accommodate large number of feature attributes which cannot be easily processed by machine learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensembled mechanical fault recognition system based on deep learning algorithm

Guo¹,

Liu²,

Ding³

et al. 2021

J. vibroeng.

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Primary detection and removal of mechanical fault is vital for the recovery of mechanical and electrical equipment. The conventional mechanical fault recognition modules are not able obtain highly sensitive feature attributes for mechanical fault classification in the absence of prior knowledge. The fault diagnosis via data-driven methods have become a point of expansion with recent development in smart manufacturing and fault recognition techniques using the concept of deep learning. In this work, a combination of feature selection with Artificial Intelligence (AI) algorithm is presented for the mechanical fault recognition to deal with smart machine tools. This article proposes a CNN based fault recognition and classification framework that uses the combination of feature extraction, feature vector decomposition using Empirical Mode Decomposition (EMD) and deep neural network (DNN) for recognising the different fault states of the rotating machinery. The experimental outcomes obtained by the combination of EMD, feature selection module and Convolutional Neural Network (CNN) provides the detailed fault information by selecting the sensitive features from large number of faulty feature attributes. The proposed fault recognition and classification method performs better in terms of all the parameters yielding 99.01 % accuracy with respective cross-entropy loss of 0.325 and time complexity of 18 mins and 31 seconds. The comparative analysis is also done with other mainstream models and other state of the art methods, which reveals that the maximum improvement of 12.29 % is attained in terms of accuracy for the proposed fault recognition method. The presented method is robust in terms of reduction of network size, improvement of mechanical fault recognition, providing classification accuracy along with high fault diagnostic solution.

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“…Recently, digital technologies have proven to be a good tool for increasing the efficiency of industrial facilities by improving the algorithms in the control systems (Runji et al, 2022;Nikolaev et al, 2022). The application of neural networks (Ewert et al, 2020;Zhou et al, 2021) and machine learning algorithms (Kudelina, 2021;Manikandan & Duraivelu, 2021) eliminates the need to solve problems of optimization of energy and mechanical parameters and provides the required quality of regulation in control systems of electric drives and typical mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the behavior of asynchronous electric drive with a closed scalar control system when changing the inductance of the magnetizing circuit

Zhukovskiy

Vasilev

Korolev

et al. 2022

Indonesian J. Sci. Technol

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The article was devoted to the study of an automated electric drive with a scalar closed-loop speed control system. Severe duty and operating modes of electric drive determine the actual service life. Wear of the induction motor, as a key link of the electric drive, was associated with deviation from nominal parameters. The deviation of parameters of the induction motor equivalent circuit determined the resultant change of characteristics. The parameters of the equivalent circuit determined the accuracy of the adjustment of regulators and optimal algorithms in the control system of the electric drive. In continuous operation modes the possibility of auto-tuning regulators, which requires stopping or no-load mode, was excluded. The paper considered the influence of the magnetization circuit mutual inductance value of the induction motor on the behavior of the electric drive control system. Evaluation of the behavior of the scalar closed-loop speed control system was performed on the basic energy (power factor, efficiency factor) and mechanical (speed, electromagnetic torque) characteristics of the electric drive.

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Fault diagnosis of various rotating equipment using machine learning approaches – A review

Cited by 56 publications

References 89 publications

DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system

DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system

Ensembled mechanical fault recognition system based on deep learning algorithm

Analysis of the behavior of asynchronous electric drive with a closed scalar control system when changing the inductance of the magnetizing circuit

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