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

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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A heterogeneous fault diagnosis method for bearings in gearbox

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406214544727 ◽

2014 ◽

Vol 229 (8) ◽

pp. 1491-1499 ◽

Cited By ~ 3

Author(s):

Xiaohui Chen ◽

Lei Xiao ◽

Xinghui Zhang ◽

Zhenxiang Liu

Keyword(s):

Fault Diagnosis ◽

Bearing Failure ◽

Optimum Frequency ◽

Bearing Faults ◽

Outer Race ◽

Bearing Fault ◽

Spectral Kurtosis ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Inner Race

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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Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

Entropy ◽

10.3390/e22010057 ◽

2019 ◽

Vol 22 (1) ◽

pp. 57 ◽

Cited By ~ 1

Author(s):

Jing Tian ◽

Lili Liu ◽

Fengling Zhang ◽

Yanting Ai ◽

Rui Wang ◽

...

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

Random Forest ◽

High Sensitivity ◽

Generalization Ability ◽

Bearing Faults ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Ae Signal ◽

Ae Signals

Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.

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Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

Sensors ◽

10.3390/s19051088 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1088 ◽

Cited By ~ 45

Author(s):

Gaowei Xu ◽

Min Liu ◽

Zhuofu Jiang ◽

Dirk Söffker ◽

Weiming Shen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Fault Diagnosis ◽

Random Forest ◽

Convolutional Neural Network ◽

Ensemble Learning ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Multi Level

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.

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Rolling bearing fault diagnosis based on wavelet energy spectrum, PCA and PNN

The 26th Chinese Control and Decision Conference (2014 CCDC) ◽

10.1109/ccdc.2014.6852274 ◽

2014 ◽

Author(s):

Keyong Shao ◽

Miaomiao Cai ◽

Guofeng Zhao

Keyword(s):

Fault Diagnosis ◽

Energy Spectrum ◽

Rolling Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Wavelet Energy

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An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm

IEEE Access ◽

10.1109/access.2021.3063929 ◽

2021 ◽

pp. 1-1

Author(s):

Lanjun Wan ◽

Kun Gong ◽

Gen Zhang ◽

Xinpan Yuan ◽

Changyun Li ◽

...

Keyword(s):

Fault Diagnosis ◽

Random Forest ◽

Rolling Bearing ◽

Random Forest Algorithm ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method

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Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults

Mathematical Problems in Engineering ◽

10.1155/2021/5503107 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Zhengyu Du ◽

Jie Ma ◽

Chao Ma ◽

Min Huang ◽

Weiwei Sun

Keyword(s):

Fault Diagnosis ◽

Fractional Fourier Transform ◽

Weighted Average ◽

Combination Method ◽

Intrinsic Mode Functions ◽

Bearing Faults ◽

Bearing Fault ◽

Mode Decomposition ◽

Reconstructed Signal ◽

Diagnosis Method

Aiming at the difficulty of extracting and classifying early bearing faults, a fault diagnosis method based on weighted average time-varying filtering empirical mode decomposition and improved eigenclass is proposed in this paper. Firstly, the bearing fault signal is decomposed into a series of intrinsic mode functions by the signal decomposition method, and the amplitude of the component is modulated by the weighted average method to enhance the fault impulse component. Then, the fractional Fourier transform is used to filter the reconstructed signal. Regarding classification issues, the eigenclass classifier is optimized by the IDE method that can be used for feature dimensionality reduction. Finally, the optimal features are selected and input into the IDE-EigenClass model. The experimental results show that the bearing fault diagnosis method proposed in this paper has higher accuracy and stability than the traditional PNN, SVM, BP, and other methods.

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