A Diagnosis Method of Bearing and Stator Fault in Motor Using Rotating Sound Based on Deep Learning

Nakamura, Hisahide; Asano, K.; Usuda, Seiran; Mizuno, Yukio

doi:10.3390/en14051319

Cited by 20 publications

(6 citation statements)

References 25 publications

(38 reference statements)

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“…The optimized values of C in all the diagnosis in Fig. 7 were 2 9 . The optimized values of γ in the 2D and 3D analyses were 2 −2 and 2 −6 , respectively.…”

Section: Diagnosis Proceduresmentioning

confidence: 99%

“…Recently, various diagnostic methods, including vibration [4][5][6], acoustic emission [7][8][9], stray flux monitoring [10,11], and motor current signature analysis (MCSA) [12][13][14] have been used to detect bearing faults. The vibration, acoustic emission, and stray flux analysis methods are sometimes unsuitable for practical use because the vibration and acoustic sensors detect ambient noise.…”

Section: Introductionmentioning

confidence: 99%

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Bearing Scratch Fault Detection by Three‐Dimensional Features and a Support Vector Machine

Yatsugi

Pandarakone

Mizuno

et al. 2022

IEEJ Transactions Elec Engng

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Induction motors play a crucial role in various industries owing to their high robustness. The demand for early fault detection is getting attention to avoid serious damage to the machines. Bearing fault is the most common failure in induction motors and the possibility of scratches has a higher probability among the various classes of the bearing faults. Recently, the effective diagnosis method considering the progression and orientation of the scratch fault by using a machine learning algorithm have been reported. However, the diagnosis of such scratch faults with high accuracy is still required and challenging for early fault detection. In this article, the scratch faults are diagnosed with high accuracy rate and the detailed analysis are conducted. A support vector machine algorithm is used to diagnose the faults, where sideband frequency components of the load current are used as the features. In order to obtain high accuracy rate, additional features are considered, where in this study, the rotating speed of the motor and a higher order frequency component are selected as candidates and compared. The rotating speed along with sideband components of load current shows high accuracy rate. Furthermore, the robustness of the method is tested against the multiple faults. The results show that the rotating speed is an effective feature for highly sensitive diagnoses of bearing scratch faults.

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“…The optimized values of C in all the diagnosis in Fig. 7 were 2 9 . The optimized values of γ in the 2D and 3D analyses were 2 −2 and 2 −6 , respectively.…”

Section: Diagnosis Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bearing Scratch Fault Detection by Three‐Dimensional Features and a Support Vector Machine

Yatsugi

Pandarakone

Mizuno

et al. 2022

IEEJ Transactions Elec Engng

Self Cite

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show abstract

“…For the bearing faults of IMs, various diagnosis methods have recently been proposed, including vibration [20][21][22], acoustic emission [23][24][25], stray flux monitoring [26,27], and motor current signature analysis (MCSA) [28][29][30][31][32][33]. Although the vibration and acoustic emission methods are effective in detecting faults, those methods are sometimes unsuitable for practical use because vibration and acoustic sensors can detect ambient noise.…”

Section: Introductionmentioning

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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“…Acoustic emission-based techniques are used to diagnose bearing faults [6][7][8]. Furthermore, multi-physical fiber optic sensors for bearing condition monitoring [9], and a driving sound-based technique for diagnosing bearing and short-circuit faults [10] are proposed.…”

Section: Introductionmentioning

confidence: 99%

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

Nakamura¹,

Mizuno

2022

Energies

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Induction motors are widely used in industry and are essential to industrial processes. The faults in motors lead to high repair costs and cause financial losses resulting from unexpected downtime. Early detection of faults in induction motors has become necessary and critical in reducing costs. Most motor faults are caused by bearing failure. Machine learning-based diagnostic methods are proposed in this study. These methods use effective features. First, load currents of healthy and faulty motors are measured while the rotating speed is changing continuously. Second, experiments revealed the relationship between the magnitude of the amplitude of specific signals and the rotating speed, and the rotating speed is treated as a new feature. Third, machine learning-based diagnoses are conducted. Finally, the effectiveness of machine learning-based diagnostic methods is verified using experimental data.

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A Diagnosis Method of Bearing and Stator Fault in Motor Using Rotating Sound Based on Deep Learning

Cited by 20 publications

References 25 publications

Bearing Scratch Fault Detection by Three‐Dimensional Features and a Support Vector Machine

Bearing Scratch Fault Detection by Three‐Dimensional Features and a Support Vector Machine

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

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

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