Multi-Sensor Fault Diagnosis of Induction Motors Using Random Forests and Support Vector Machine

Saberi, Alireza; Sandirasegaram, Sarvavignoban; Belahcen, Anouar; Vaimann, Toomas; Sobra, Jan

doi:10.1109/icem49940.2020.9270689

Cited by 8 publications

(16 citation statements)

References 23 publications

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“…The main disadvantage of SVM is, that in the case of lineary inseparable tasks requires the determination of parameters or the choice of certain core type. This method is not effective for larger datasets and tends to be sensitive to noise [46,67,82,96]. • Fuzzy logic is another frequently used way of classifying IM failures.…”

Section: Classification Techniquesmentioning

confidence: 99%

Advanced Signal Processing Methods for Condition Monitoring

Jaroš

Byrtus

Dohnal

et al. 2022

Arch Computat Methods Eng

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Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park’s Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.

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Section: Classification Techniquesmentioning

confidence: 99%

Advanced Signal Processing Methods for Condition Monitoring

Jaroš

Byrtus

Dohnal

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…The general algorithm of supervised learning is shown in Figure 5. Among supervised algorithms, the most widely used are the following algorithms: linear and logistic regression [47,48], Naive Bayes [49,50], nearest neighbor [51,52], and random forest [53][54][55][56]. In condition monitoring and diagnostics of electrical machines, the most suitable supervised algorithms are decision trees [57][58][59] and support vector machines [60][61][62].…”

Section: Supervised Machine Learningmentioning

confidence: 99%

“…Unsupervised learning aims to discover features from labeled examples so it is possible to analyze unlabeled examples with possibly high accuracy. Basically, the program creates a rule according to what the data are to be processed and classified.Among supervised algorithms, the most widely used are the following algorithms: linear and logistic regression[47,48], Naive Bayes[49,50], nearest neighbor[51,52], and random forest[53][54][55][56]. In condition monitoring and diagnostics of electrical machines, the most suitable supervised algorithms are decision trees[57][58][59] and support vector machines[60][61][62].…”

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confidence: 99%

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

et al. 2021

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A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.

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“…These approaches are based on statistical measurements that allow the description of a given signal. In the detection of faults in IM, these models have been used in the classification of faults such as broken rotor bars, rotor misalignment, bearing faults, unbalance, and many others [17][18][19][20]. Within the unbalance fault, signal analysis can be performed in both time or frequency domains.…”

Section: Introductionmentioning

confidence: 99%

Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features

2023

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The detection of faults in induction motors has been one of the main challenges to the industry in recent years. An effective fault detection method is fundamental to ensure operational security and productivity. Different models for intelligent fault diagnosis have been recently proposed. However, not all of them are accessible for some manufacturing processes because of the black-box approach, the complexity of hyperparameter tuning, high-dimensionality feature vectors, and the need for sophisticated computational resources. In this paper, a method for the detection of an unbalance fault in induction motors based on a low-dimensional feature vector and a low-complexity classification approach is proposed. The feature vector presented in this manuscript is based on texture features, which are a basic tool for image processing and image understanding. Nevertheless, texture features have not been explored as a powerful instrument for induction motor fault analysis. In this approach, texture features are used to analyze a set of vibration signals belonging to two different classes: an unbalanced motor and a healthy motor. Training-validation and testing stages are developed to build and evaluate the performance of the classifier, respectively. The results show higher accuracy and lower training time in comparison with different state-of-the-art approaches.

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Multi-Sensor Fault Diagnosis of Induction Motors Using Random Forests and Support Vector Machine

Cited by 8 publications

References 23 publications

Advanced Signal Processing Methods for Condition Monitoring

Advanced Signal Processing Methods for Condition Monitoring

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features

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