Application of Principal Component Analysis for Fault Detection of DC Motor Parameters

Atanasov, Nasko; Zhekov, Zhivko; Григоров, Иван; Alexandrova, Mariela

doi:10.1007/978-3-319-68324-9_34

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…PCA can distinguish between various motor faults and provide an inexpensive and simple alternative [19]. e application of PCA in fault detection and diagnosis problems is also explored in [20][21][22]. Another well-researched approach is the extension of artificial neural networks including deep normalized convolutional neural networks [23] and deep neural networks with batch normalization (BN) [24] for the classification of bearing faults.…”

Section: Related Workmentioning

confidence: 99%

“…e principal components (PCs) are acquired from the uncorrelated variables to detect and confine process anomalies in a vigorous way [40]. For simplicity, any given normal data matrix X (N × m), X is transformed into a new matrix T (N × r) where r is greater than m. is is achieved by using a transformation matrix P (m × r) [21].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

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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: 99%

Section: Principal Component Analysis (Pca)mentioning

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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“…These algorithms are spread in robot navigation, resource management, and real-time decisions [10][11][12]. In diagnostics of electrical machines, the following algorithms are used: decision trees [13], support vector machines [14], principal component analysis [15], and genetic algorithm [16].…”

Section: Intelligent Diagnostic Techniquesmentioning

confidence: 99%

Methods of Condition Monitoring and Fault Detection for Electrical Machines

et al. 2021

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Nowadays, electrical machines and drive systems are playing an essential role in different applications. Eventually, various failures occur in long-term continuous operation. Due to the increased influence of such devices on industry, industrial branches, as well as ordinary human life, condition monitoring and timely fault diagnostics have gained a reasonable importance. In this review article, there are studied different diagnostic techniques that can be used for algorithms’ training and realization of predictive maintenance. Benefits and drawbacks of intelligent diagnostic techniques are highlighted. The most widespread faults of electrical machines are discussed as well as techniques for parameters’ monitoring are introduced.

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Brushless DC motor performance monitoring using feature extraction and dimensionality reduction based on artificial intelligence technique

Mezeal,

Hussain,

Abed

2024

AIP Conference Proceedings

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Application of Principal Component Analysis for Fault Detection of DC Motor Parameters

Cited by 4 publications

References 4 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

Methods of Condition Monitoring and Fault Detection for Electrical Machines

Brushless DC motor performance monitoring using feature extraction and dimensionality reduction based on artificial intelligence technique

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