Bearing Fault Detection in Adjustable Speed Drives via a Support Vector Machine with Feature Selection using a Genetic Algorithm

Teotrakool, K.; Devaney, M.J.; Eren, Levent

doi:10.1109/imtc.2008.4547208

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…One classical work on the use of SVM towards identifying bearing faults can be found in [47], where classification results obtained by the SVM are optimal in all of the cases, with an overall improvement over the performance of ANN. Other similar SVM based papers [48]- [60] also illustrated the effectiveness and efficiency of employing SVM to serve as the fault classifier.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 86%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in deep learning (DL) algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods over conventional ML methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

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Section: Support Vector Machines (Svm)mentioning

confidence: 86%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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“…f (t), i(t) and o(t) represent the values of forget gate, input gate and output gate at time t, respectively. α(t) represents the initial feature extraction of h(t − 1) and x(t) at time t. The specific calculation process is equations ( 9)- (12),…”

Section: The Backbone Of Bilstm-scn With Cbammentioning

confidence: 99%

“…Then, the original fault data undergoes statistical methods for preprocessing. Finally, advanced ML algorithms are used to train the data samples and classify the specific fault type through inference [10][11][12]. In which, classical ML algorithms have been maturely used in industrial bearing fault diagnosis due to the high efficiency of the model, such as artificial neural networks, K-nearest neighbors, fuzzy neural networks [13], Bayesian networks [14], extreme learning machines [15], support vector machine (SVM) [16], and optimized SVM with high non-linear learning ability, etc [17].…”

Section: Introductionmentioning

confidence: 99%

An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults

You,

Qiu,

2023

Meas. Sci. Technol.

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This study proposes an efficient rolling bearing fault diagnosis model of a hybrid neural network with a lightweight attention mechanism. Firstly, to achieve the low complexity of deep learning computation, data reduction and denoising are performed by sparse convolutional network (Principal Component Analysis and Improved Complete Ensemble Empirical Modal Decomposition of Adaptive Noise), then processed data is imported to the hybrid neural network model with CBAM (Convolutional Block Attention Module ). The BiLSTM-SCN (Bi-directional Long Short-Term Memory and Sparse Convolutional Networks ) are used in the backbone of the model. A lightweight, generalized attention mechanism is introduced to the last layer of the model for enhancing feature learning, which can further improve the diagnostic accuracy and efficiency. Compared with existing deep learning fault diagnosis models, In simulating the most realistic cross-conditions and cross-platform conditions, which leads to the random nature of fault generation and makes model diagnosis more complex, the proposed method still maintains less running time and excellent diagnostic accuracy. Finally, the experimental results fully prove that the model has reliable robust and efficient, and it achieves the best balance of diagnostic accuracy and diagnostic efficiency of the hybrid deep learning model.

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“…At certain times these machines operate underneath negative conditions, which include high ambient temperature, moisture, and overload or electrical conditions like (a) Unbalance magnetic pull; (b) uncontrolled heat (c) Increased load (d) Efficiency reduction (e) Decrease in average torque (f) Enhanced torque pulsation, that may subsequently result in motor malfunctions thus leading to failure [2]. Failure of machinery at an unexpected time can cause heavy losses to the country's economy financially and technically due to the unavailability of healthy machinery to major sectors i.e., train system, wind turbine system, and industrial production machinery that result in high protection expenses, extreme economic losses, and protection [3]. Statistics show that more than 90% of machines use rolling components in industrial applications [4].…”

Section: Introductionmentioning

confidence: 99%

A Qualified review of ML and DL algorithms for Bearing Fault Diagnosis

Bibi

Naz

Talpur

et al. 2022

IJIST

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Moving machinery is the backbone of socio-economic development. The use of machines helps in increasing the production of everyday used items, and tools, that generate electricity and mechanical energy, and provides easy and fast transportation and help by saving human efforts, energy, and time. The mechanical industry is totally dependent on the bearing and it is considered bread and butter of the system. Bearing failure is about 40% of the total failures of induction motors which is why it is a crucial challenge to predict the failure and helps prevent future downtime events through maintenance schedules with the latest techniques and tools of. This paper presents a review of how DL techniques and algorithms outsmarted ML for bearing fault detection and diagnosis and summarizes the accuracy results generated by most common DL algorithms over classical ML algorithms. Additionally this paper reasons different criteria for which DL algorithms have been proved efficient for building productive model in the field of bearing fault detection. Furthermore, some of the most famous datasets by different universities have been discussed and accuracy results are provided by reviewing algorithms on the CWRU dataset by different researchers and comparison chart is listed in the results section.

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Bearing Fault Detection in Adjustable Speed Drives via a Support Vector Machine with Feature Selection using a Genetic Algorithm

Cited by 11 publications

References 15 publications

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults

A Qualified review of ML and DL algorithms for Bearing Fault Diagnosis

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