Diagnosis for Slight Bearing Fault in Induction Motor Based on Combination of Selective Features and Machine Learning

Nakamura, Hisahide; Mizuno, Yukio

doi:10.3390/en15020453

Cited by 18 publications

(5 citation statements)

References 25 publications

(30 reference statements)

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“…However, these higher-order components did not improve the overlapping according to our initial investigation. Meanwhile, the rotation speed could be an effective additional feature to improve the overlapping [42]. Thus, threedimensional mapping using the rotation speed as the third feature to improve the feature overlapping was performed in this study.…”

Section: Three-dimensional Analysismentioning

confidence: 99%

Common Diagnosis Approach to Three-Class Induction Motor Faults Using Stator Current Feature and Support Vector Machine

et al. 2023

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Induction motors are becoming crucial components in numerous industries. The daily usage of induction motors creates the demand for proper maintenance and slight fault detection to avoid serious damage to the induction motor and the shutdown of industries. Among the various kinds of faults in induction motors, bearing failures, broken rotor bar failures, and short-circuit insulation failures are the most common. Thus, detection and classification of slight these faults are attracting great attention. There are conventional methods for detecting such faults, such as the vibration method for bearing failure, the self-organizing map in the case of broken rotor bar failure, and motor current signature analysis for short-circuit insulation failure. From an industrial point of view, diagnosis methods that can classify all these major faults are required. However, reports on the detection and classification of slight these faults using common diagnosis methods are scarce. In this paper, all three kinds of notable faults in an induction motor were artificially induced, and diagnoses using motor stator current spectral features and the rotation speed of the motor were performed. The diagnosis was accomplished using an auto-tunable and arbitrary featured support vector machine algorithm. Although the faults were minor, a high accuracy rate was obtained. The capability to classify the faults and the high diagnosis accuracy prove the robustness and high sensitivity of the method, enabling its practical applications in industries.

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Section: Three-dimensional Analysismentioning

confidence: 99%

Common Diagnosis Approach to Three-Class Induction Motor Faults Using Stator Current Feature and Support Vector Machine

et al. 2023

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“…equipment. According to the type of signals used, commonly used bearing fault diagnosis methods are classified as vibration analysis [4,5], current analysis [6][7][8], sound analysis [9], and other methods [10,11]. Bearing troubleshooting based on vibration signals is a widely used method because vibration signals can reflect the dynamic behavior and health of the equipment [12].…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

Zhang,

Xu,

Chen

et al. 2024

Meas. Sci. Technol.

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Bearings are key components in mechanical equipment, which are widely used in various fields such as automobiles and airplanes. Aiming at the analysis of vibration signal processing under the variable speed condition of bearings, this paper proposes a new bearing fault diagnosis method, which firstly resamples the vibration signals in the angular domain, and then converts the resampled signals into images by the relative position matrix method, and finally uses the transfer learning to automatically extract the features and classify them. To verify the effectiveness of the method, it is tested on the Case Western Reserve University bearing fault dataset and University of Ottawa bearing fault dataset respectively. Compared with other time series to image methods (Recurrence Plot, etc) and other pre-trained models (GoogLeNet, etc), the proposed method has some advantages in terms of accuracy, image generation time, training time, and testing time. The accuracy of the proposed method in this paper reaches more than 90%, which suggests its potential effectiveness in the classification of bearing faults under variable speed working conditions.

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“…It does not need to add additional sensors, which is a non-invasive fault diagnosis method. In the electrical method, it is common to use the motor stator current as the basis for fault diagnosis (Xiang et al 2022, Hisahide andYukio 2022). Bearing defects will lead to a specific air gap eccentricity and cause load torque oscillation, resulting in fluctuations in the stator current.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Motor Bearing Based on Current Bi-Spectrum and Convolutional Neural Network

Jiang

et al. 2023

Lat. Am. j. solids struct.

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Motor bearings are prone to different degrees of performance degradation, fatigue damage and failure undergoing complex and harsh environments. Vibration signal analysis is a mature method for diagnosing motor bearing faults, while it is not applicable for installing additional vibration sensors on many occasions. Practically, the fault of motor bearings changes the air gap flux between the rotor and stator, which leads to harmonic fluctuations in the stator current. The current signals can be used to diagnose the motor bearing faults without additional sensors. Inevitably the harmonics caused by the motor bearing faults will be coupled with the original signals. This paper combines bi-spectrum and Convolution Neural Network (CNN) to analyze the current signals of motor bearing faults. The CNN diagnosis model is trained based on the local bi-spectrum of current, and the CNN parameters are optimized. Diagnose and analyze motor bearing faults with different fault implantation methods, working conditions, fault degrees and fault locations. The diagnostic accuracy reaches more than 80%.

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Diagnosis for Slight Bearing Fault in Induction Motor Based on Combination of Selective Features and Machine Learning

Cited by 18 publications

References 25 publications

Common Diagnosis Approach to Three-Class Induction Motor Faults Using Stator Current Feature and Support Vector Machine

Common Diagnosis Approach to Three-Class Induction Motor Faults Using Stator Current Feature and Support Vector Machine

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

Fault Diagnosis of Motor Bearing Based on Current Bi-Spectrum and Convolutional Neural Network

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