Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Głowacz, Adam

doi:10.1515/amm-2016-0028

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…If mk is the element of the matrix M at the row k and the column i ( , ∈ {1,2,3,4}), and s is the score of the classification of the positive class of the classifier SVM1, then a new observation is assigned to the class (k) which minimizes the aggregation of the losses for the SVM1 according to equations (9)    …”

Section: Support Vector Machines (Svm) and Mathematical Principlementioning

confidence: 99%

“…In a study presented in [8], the author used an approach that is based on the classical spectral analysis and a method of classification, inspired by the supervised learning theory of support vector machines (SVM), enabling the detection and identification of the ball bearing defect in the induction motor. In [9], the author proposes a technique of diagnosis of rotor electrical defects in the induction motor based on the analysis of acoustic signals collected from three motors.…”

Section: Introductionmentioning

confidence: 99%

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Use of Support Vector Machines for Classification of Defects in the Induction Motor

Benzahioul

Metatla

Kerboua

et al. 2019

Acta Universitatis Sapientiae, Electrical and Mechanical Engineering

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The classification and detection of defects play an important role in different disciplines. Research is oriented towards the development of approaches for the early detection and classification of defects in electrical drive systems. This paper, proposes a new approach for the classification of induction motor defects based on image processing and pattern recognition. The proposed defect classification approach was carried out in four distinct stages. In the first step, the stator currents were represented in the 3D space and projected onto the 2D space. In the second step, the projections obtained were transformed into images. In the third step, extraction of features whereas the Histogram of Oriented Gradient (HOG) is used to construct a descriptor based on several sizes of cells. In the fourth step, a method of classifying the induction motor defects based on the Support Vector Machine (SVM) was applied. The evaluation results of the developed approach show the efficiency and the precision of classification of the proposed approach.

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Section: Support Vector Machines (Svm) and Mathematical Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Support Vector Machines for Classification of Defects in the Induction Motor

Benzahioul

Metatla

Kerboua

et al. 2019

Acta Universitatis Sapientiae, Electrical and Mechanical Engineering

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show abstract

“…In addition, there are masses of categorizers practiced in various scenarios. For instance, decision tree classifiers can diagnose motor fault tasks or detect breast cancer based on medical data [ 26 , 27 , 28 ]; naive Bayes algorithm has been applied as a fault classifier to investigate the status of a monoblock centrifugal pump or an engine [ 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

Chen

et al. 2018

Sensors

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Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.

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“…Reliability in public transportation is a very wide topic, which includes components and services. In the first case, relevant work was performed on fault diagnosis of motors by the analysis of acustic, thermal, and vibration signals . In the second case, reliability concerns the dependability of a transit service in terms of multidimensional aspects.…”

Section: Introductionmentioning

confidence: 99%

“…In the first case, relevant work was performed on fault diagnosis of motors by the analysis of acustic, thermal, and vibration signals. [1][2][3][4][5] In the second case, reliability concerns the dependability of a transit service in terms of multidimensional aspects. This paper focuses on service-time reliability by archived automatic vehicle location (or AVL systems).…”

mentioning

confidence: 99%

Time Reliability Measures in bus Transport Services from the Accurate use of Automatic Vehicle Location raw Data

Barabino¹,

Francesco

Mozzoni³

2016

Quality & Reliability Eng

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Archived automatic vehicle location (AVL) data are widely used in bus transportation, but typically contain anomalies, such as missing data points and unseen bus overtakings. These anomalies may alter the measurement of time reliability (i.e., headways and schedule time deviations) of buses at stops with respect to passenger experiences. However, in many studies, anomalies are ignored, neglected, or partially addressed. This paper investigates the effect of AVL anomalies on headways and schedule deviations, which are keys for regularity and punctuality evaluations and are well-practicized among bus operators worldwide. The analysis is carried out on six scenarios, each representing a combination of AVL data anomalies. The outcomes show that addressing anomalies result in more severe regularity measures, which point out criticalities suffered by passengers, whereas it can be skipped for the measurement of punctuality. In addition, an increase of 5% of missing data results in the increase of 5% in the coefficient of variation of the headway, but it does not lead to meaningful variations in punctuality measures. These results are useful for both practitioners interested in passenger-oriented measurement of time reliability and researchers working on the disclosure of the determinants of unreliability

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Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Cited by 10 publications

References 31 publications

Use of Support Vector Machines for Classification of Defects in the Induction Motor

Use of Support Vector Machines for Classification of Defects in the Induction Motor

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

Time Reliability Measures in bus Transport Services from the Accurate use of Automatic Vehicle Location raw Data

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