Multi-domain Bearing Fault Diagnosis using Support Vector Machine

Mishra, Rismaya Kumar; Choudhary, Anurag; Mohanty, Amiya Ranjan; Fatima, Shahab

doi:10.1109/gucon50781.2021.9573613

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…An overlap of 50% was also used in its subsequent window. The mathematical representation of the moving window is reported in, 46 and the details are as follows: where l is the sample length, m is the window size, a is the percentage overlapping and ⌊ v ⌋ is the floor of v.…”

Section: Proposed Methods For Bearing Fault Diagnosismentioning

confidence: 99%

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An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization

Mishra

Choudhary

Mohanty

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Bearing is regarded as one of the core elements in rotating machines and its fault diagnosis is essential for better reliability and availability of the rotating machines. This paper puts forward an intelligent vibration signal-based fault diagnosis approach for bearing faults identification at an early stage, irrespective of speed conditions. The proposed methodology comprises of a frequency shift-based hybrid signal processing technique that involves a combination of Hilbert Transform (HT) and Discrete Wavelet Transform (DWT) followed by sliding window-based feature extraction. Thereafter, a newly developed Henry Gas Solubility Optimization (HGSO) is implemented to select the relevant features. At last, the optimal attributes are used to train the Artificial Neural Network (ANN) model for the classification of the different bearing faults. To test the effectiveness of the speed independent model, experimental validation was done with constant and varying speed conditions. The results demonstrate that the proposed methodology has a tremendous potential to eliminate unplanned failures caused by bearing in rotating machinery.

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Section: Proposed Methods For Bearing Fault Diagnosismentioning

confidence: 99%

“…An overlap of 50% was also used in its subsequent window. The mathematical representation of the moving window is reported in, 46 and the details are as follows:…”

Section: Feature Extractionmentioning

confidence: 99%

An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization

Mishra

Choudhary

Mohanty

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…process of sliding window-based feature extraction is reported in [1,29] and listed in table 3. Finally, the feature vector is generated and used as an input to the metaheuristic algorithm for feature selection.…”

Section: Sliding Window-based Feature Extractionmentioning

confidence: 99%

A self-adaptive multiple-fault diagnosis system for rolling element bearings

Mishra¹,

Choudhary²,

Fatima³

et al. 2022

Meas. Sci. Technol.

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The inevitable simultaneous formation of multiple faults in bearings generates severe vibration, propelling it for premature component failure and unnecessary downtime. For accurate diagnosis of multiple faults, Machine Learning (ML) models need to be trained with the signature of different multi-faults, which increases the data acquisition time and expenses. This paper proposes a self-adaptive vibration signature-based fault diagnostic method for detecting multiple bearing faults using various single fault vibration signatures. A time-frequency based hybrid signal processing technique, which involves Discrete Wavelet Transform (DWT) and Hilbert Transform (HT) was adopted for signal decomposition, followed by the implementation of a sliding window-based feature extraction process. Seven optimised metaheuristic algorithms were used to find the best feature sets, which were further used for the training of three ML models. The result showed that the proposed methodology has tremendous potential to detect multiple bearing fault conditions in any possible combination by using single fault data. It will be helpful where accessibility to a large amount of data is limited for multiple fault diagnosis.

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“…Statistical feature information can be examined in time, frequency, and time-frequency domains through complex signal processing and analysis methods to uncover the operating conditions of bearings from various perspectives. In many circumstances, utilising features from multiple domains has been demonstrated to be more effective in distilling useful information than single-domain features for fault diagnosis problems [ 5 ]. Yan and Jia [ 6 ] employed various approaches to extract fault feature information from multi-domain aspects.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing

Dong

Lotfipoor

2023

Sensors

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Finding relevant features that can represent different types of faults under a noisy environment is the key to practical applications of intelligent fault diagnosis. However, high classification accuracy cannot be achieved with only a few simple empirical features, and advanced feature engineering and modelling necessitate extensive specialised knowledge, resulting in restricted widespread use. This paper has proposed a novel and efficient fusion method, named MD-1d-DCNN, that combines statistical features from multiple domains and adaptive features retrieved using a one-dimensional dilated convolutional neural network. Moreover, signal processing techniques are utilised to uncover statistical features and realise the general fault information. To offset the negative influence of noise in signals and achieve high accuracy of fault diagnosis in noisy settings, 1d-DCNN is adopted to extract more dispersed and intrinsic fault-associated features, while also preventing the model from overfitting. In the end, fault classification based on fusion features is accomplished by the usage of fully connected layers. Two bearing datasets containing varying amounts of noise are used to verify the effectiveness and robustness of the suggested approach. The experimental results demonstrate MD-1d-DCNN’s superior anti-noise capability. When compared to other benchmark models, the proposed method performs better at all noise levels.

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Multi-domain Bearing Fault Diagnosis using Support Vector Machine

Cited by 17 publications

References 29 publications

An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization

An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization

A self-adaptive multiple-fault diagnosis system for rolling element bearings

Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing

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