Model-Based Analysis and Quantification of Bearing Faults in Induction Machines

Zhang, Shen; Wang, Bingnan; Kanemaru, Makoto; Lin, Chungwei; Liu, Dehong; Miyoshi, Masahito; Teo, Koon Hoo; Habetler, T.G.

doi:10.1109/tia.2020.2979383

Cited by 47 publications

(14 citation statements)

References 40 publications

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“…The measurement equipment was developed by the authors, which enabled the current and voltage measurement up to 20 A and 300 V with a tolerance error of ±2%, respectively. The sampling time was set to 10 µs, and the data recording length was 2 17 per channel. Data acquisition was triggered at 30 s intervals by a timer signal.…”

Section: Reproduction Of Faultsmentioning

confidence: 99%

“…Among the several methods, motor current signature analysis (MCSA) has been commonly used, showing satisfactory performance for diagnosing diverse bearing failures [11][12][13][14][15][16][17]. MCSA has several advantages, including that it is non-invasive and does not require special sensors.…”

Section: Introductionmentioning

confidence: 99%

“…However, the proposed method was evaluated by using data obtained under no load and rated load. A model-based approach is proposed to quantify the bearing fault severity while the motor is running at any speed [17]. In this study, the indicator of the bearing fault is used based on the transfer function, which is the model of the motor.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Reproduction Of Faultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Thus, the most suitable investigation could only be achieved from the integrative interpretation analysis adequately correlated with visualization of all monitored parameters [10,11]. Some other maintenance principles are based on the insulation aging detection [12] or bearing fault signaling [13]. Even laboratory maintenance tests were developed [14] or such systems dedicated to photovoltaic applications [15].…”

Section: Introductionmentioning

confidence: 99%

Smart Predictive Maintenance Device for Critical In-Service Motors

2022

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The paper proposed an innovative predictive maintenance system, designated to monitor and diagnose critical electrical equipment (generally large power electric motors) within industrial electrical installations. A smart and minimally invasive system is designed and developed. Its scope is to evaluate continuously the essential operating parameters (electrical, thermal, and mechanical) of the investigated equipment. It manages to report the deviations of inspected machine operating parameters values from the rated ones. The system also suggests the potential cause of these abnormal variations along with possible means (if the defect is identified in a database, constantly updated with each appearance of a malfunction). The developed maintenance device generates an operating report of the analyzed equipment, in which the values of power quality and energy indicators are computed and interpreted. Additionally, real-time remote transmission of analyzed data is facilitated, making them accessible from any location. The proposed maintenance system is a low-cost device that is easy to install and use in comparison with similar existing devices and equipment. The designed maintenance system was tested on dedicated to low-voltage equipment up to 100 kW.

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“…In recent years, with research on artificial intelligence (AI), the intelligent algorithms [2] were applied to fault diagnosis of rolling bearings. Features can be automatically extracted by wavelet transform (WT) [3], spectral analysis (SA) [4], etc. A relationship between features and states was established based on machine learning [5] or neural network for diagnose faults.…”

Section: Introductionmentioning

confidence: 99%

Size and Location Diagnosis of Rolling Bearing Faults: An Approach of Kernel Principal Component Analysis and Deep Belief Network

Huang¹,

Yu²,

Gu³

2021

IJCIS

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Diagnosing incipient faults of rotating machines is very important for reducing economic losses and avoiding accidents caused by faults. However, diagnoses of locations and sizes of incipient faults are very difficult in a noisy background. In this paper, we propose a fault diagnosis method that combines kernel principal component analysis (KPCA) and deep belief network (DBN) to detect sizes and locations of incipient faults on rolling bearings. Effective information of raw vibration signals processed by KPCA method is used as input signals of the DBN of which weights of the first RBM are initialized by contribution rates of principal components. A DBN with complex structures can be cut into a briefer network by KPCA-DBN model. That model reduces network structure and increases convergence rate. As a result, an average test accuracy by KPCA-DBN can reach 99.1% for identification of 12 labels including incipient faults and the training time is 28s which is half of that by DBN model. The average accuracy of rolling bearing location detection nearly gets to 100% and the average accuracy of fault size detection is above 99%. Compared with SVM, BP, CNN, Deep EMD-PCA, CNN-SVM and DBN, it is found that training time can be shortened and detection accuracy can be improved by KPCA-DBN model. The proposed method is beneficial to realize sizes and locations detection of incipient faults online.

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Model-Based Analysis and Quantification of Bearing Faults in Induction Machines

Cited by 47 publications

References 40 publications

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

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

Smart Predictive Maintenance Device for Critical In-Service Motors

Size and Location Diagnosis of Rolling Bearing Faults: An Approach of Kernel Principal Component Analysis and Deep Belief Network

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