A diagnosis method for motor bearing faults based on harmonic injection

Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains two bearing fault modes, inner race fault and outer race fault. The results demonstrate that the proposed method can detect the bearing fault easier than spectral Kurtosis and envelope Kurtosis.

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A New Hybrid Diagnosis of Bearing Faults Based on Time-Frequency Images and Sparse Representation

Traitement du signal ◽

10.18280/ts.370604 ◽

2020 ◽

Vol 37 (6) ◽

pp. 907-918

Author(s):

Ilhan Aydin ◽

Seyfullah Kaner

Keyword(s):

Deep Learning ◽

Induction Motors ◽

Early Stage ◽

Training Data ◽

Bearing Faults ◽

Time Frequency ◽

Histograms Of Oriented Gradients ◽

Diagnosis Method ◽

And Performance ◽

Learning Machine

Induction motors are an essential component of many applications in industry due to their robust and simple construction. Since bearing faults are the most occurred fault type in the induction motors, it is important to implement the fault detection procedure at an early stage to prevent a sudden interruption of industrial systems. In recent years, deep learning-based techniques have become important tools for converting raw data into images and for producing high-quality images. However, deep learning-based techniques are still difficult to apply in real-time because the techniques require large training data, which slows down the learning process. In the present study, we propose a novel bearing faults diagnosis method at different operating speeds and load conditions. We obtain the time-frequency (TF) representation by applying continuous wavelet analysis to the raw vibration signals. The results of TF representation is recorded as an image. We apply co-occurrence Histograms of Oriented Gradients (coHOG) to the image to obtain features and classify the features with extreme learning machine with a sparse classifier (ELMSRC) to diagnose faults. We obtained better results in terms of time and performance compared with the proposed method of other classification and deep learning techniques.

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Intelligent Diagnosis Method for Bearing Faults of In-wheel Motor

International Journal of Comprehensive Engineering ◽

10.14270/ijce2020.a00228.10 ◽

2020 ◽

Keyword(s):

Intelligent Diagnosis ◽

Bearing Faults ◽

Diagnosis Method

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Motor Bearing Faults Detection and Classification based on Convolutional Neural Network and Support Vector Machine: A Comparative Study

10.1109/gcat52182.2021.9587774 ◽

2021 ◽

Author(s):

Sujit Kumar

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Comparative Study ◽

Convolutional Neural Network ◽

Support Vector ◽

Bearing Faults ◽

Motor Bearing

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The diagnosis method for induction motor bearing fault based on Volterra series

Fifth International Conference on Intelligent Control and Information Processing ◽

10.1109/icicip.2014.7010270 ◽

2014 ◽

Author(s):

Changqing Xu ◽

Chidong Qiu ◽

Meng Xia ◽

Guozhu Cheng ◽

Zhengyu Xue

Keyword(s):

Induction Motor ◽

Volterra Series ◽

Bearing Fault ◽

Diagnosis Method ◽

Motor Bearing

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Fault Diagnosis Method for Inter-Shaft Bearings Based on Information Exergy and Random Forest

Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy ◽

10.1115/gt2018-76101 ◽

2018 ◽

Author(s):

Jing Tian ◽

Yanting Ai ◽

Ming Zhao ◽

Chengwei Fei ◽

Fengling Zhang

Keyword(s):

Fault Diagnosis ◽

Energy Spectrum ◽

Random Forest ◽

Complex Signals ◽

Bearing Faults ◽

Outer Race ◽

Bearing Fault ◽

Wavelet Energy ◽

Diagnosis Method ◽

Ae Signals

To reasonably process the complex signals and improve the diagnosis accuracy of inter-shaft bearing incipient faults, this paper develops wavelet energy spectrum exergy (WESE) and random forest (RF) (short for WESE-RF) method with respect to acoustic emission (AE) signals. Inter-shaft bearing faults, which contain inner race fault, outer race fault, rolling element faults and normal status under different measuring points and different rotational speeds, are simulated based on the test rig of inter-shaft bearings, to collect the AE signals of these faults. Regarding the AE signals of inter-shaft bearing faults, the WESE values, one signal feature, are extracted from an information exergy perspective, and are applied to structure feature vectors. The WESE values of these AE signals are regarded as the sample set which include the training samples subset used to establish the WESE-RF model of fault diagnosis and the test samples subset applied to test the effectiveness of the developed WESE-RF model. The investigation on the fault diagnosis of inter-shaft bearing demonstrates the fault diagnosis method with the WESE-RF has good generalization ability and high diagnostic accuracy of over 0.9 for inter-shaft bearing fault. The efforts of this paper provide a useful approach-based information exergy and wavelet energy spectrum for inter-shaft bearing fault diagnosis.

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