Classification of Bearing Fault Detection Using Multiclass SVM: A Comparative Study

Zgarni, Slaheddine; Braham, Ahmed

doi:10.1109/ssd.2018.8570564

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Furthermore, OC-SVM has the ability to learn given little data. e authors found that OC-SVM classification accuracy is higher than that of classical SVM and this is also affirmed by [16]. More research on the application of SVM in fault detection and diagnosis problems can be found in [17].…”

Section: Related Workmentioning

confidence: 79%

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Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed

Pule

Matsebe

Samikannu

2022

Mathematical Problems in Engineering

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Vibration analysis is widely used as an efficient condition monitoring (CM) tool for rotating machines in various industries. Fault detection and diagnosis (FDD) models play an important role in the development of any CM system. The use of artificial intelligence (AI) has since gained recognition in the development of fault detection and diagnosis systems. In this paper, a combination of principal component analysis (PCA) which is used for reducing the data dimensionality, and support vector machine (SVM) which is adopted for classification to carry out fault detection and diagnosis of faults in bearings using vibrations. The diagnostic feature design and machine learning toolbox in MATLAB are used to develop features and train the models, respectively. Real data from the Mendeley data depository is used to test and evaluate the models. Model training is carried out using data with varying speeds representing different conditions of bearing making it different from similar approaches involving SVM. The choice of data used proves that SVM can be able to classify faults with consideration of the varying operating speeds. Results have shown that the combination of PCA and SVM is effective in fault diagnosis of bearing faults under varying speeds such that a 97.4% classification accuracy was achieved. The result implies that PCA and SVM can be implemented in various industrial setups where variable speeds can occur both intentionally or nonintentionally. Furthermore, the method was able to differentiate between compounding faults and faults that occur at different times. The confusion matrix further proves the quality and accuracy of the trained model. Future work will focus on the development of models that can carry out the prognosis of faults in bearings as well as to model for other faults other than bearing faults.

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Section: Related Workmentioning

confidence: 79%

“…SVMs are also adopted for fault classification in [15,16] and other classification problems in [18]. In [1], principal component analysis (PCA) is used on FFT data to reduce the dimensionality of the data.…”

Section: Related Workmentioning

confidence: 99%

Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed

Pule

Matsebe

Samikannu

2022

Mathematical Problems in Engineering

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“…The development of a lowcost brushless DC motor sensorless controller using dsPIC30F4011 investigated by Karnavas using sensorless techniques avoiding to use Hall sensors [17]. Bearing Error Detection Classification Using SVM Multiclass was studied by Zgarni, like a drive speed that was set for bearing fault detection [18]. Sboui studied and observed the behavior of Network-Connected Induction Generators Experiencing Low-Frequency Ripple Speed with Driving Speed, and the effect of low-frequency ripple of drive speed on the quality of the injected current and the instantaneous power debited by an induction generator into the connection network [19].…”

Section: Introductionmentioning

confidence: 99%

The Design of Tachometer Contact and Non-Contact Using Microcontroller

Tunggal

Kirana

Arfianto³

et al. 2020

jrc

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Calibration is required for various kinds of tools from kitchen tools such as stirrers or mixers to medical tools such as temperature calibration, ECG, blood pressure, and other instrumentation tools to be according to the required standards. In medical devices, a digital tachometer has been used as a calibration support device to measure the speed of objects that rotate in RPM on the centrifuge. Some medical devices are calibrated in km/hour, for example, the ergo cycle and treadmill. Two different devices or a conversion from RPM to km/hour are required. The research aimed to design a measurement device that performed calibration of devices with different units, namely tachometer that can be used to measure speed in RPM and km/hr. Based on the research objective, a tool was made to measure the motor's rotational speed using the E18-D80NK sensor, microcontroller, and LCD without touching the object being measured.

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Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm

Borman

Rossi

Jusman

et al. 2021

2021 1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)

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Classification of Bearing Fault Detection Using Multiclass SVM: A Comparative Study

Cited by 5 publications

References 16 publications

Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed

Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed

The Design of Tachometer Contact and Non-Contact Using Microcontroller

Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm

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