Cyclostationarity analysis and diagnosis method of bearing faults

2009 ◽  
Author(s):  
Bin Wu ◽  
Minjie Wang ◽  
Bin Wu ◽  
Yuegang Luo ◽  
Changjian Feng
Keyword(s):  
Bearing Faults ◽  
Diagnosis Method

A heterogeneous fault diagnosis method for bearings in gearbox

2014 ◽  
Vol 229 (8) ◽  
pp. 1491-1499 ◽  
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.

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

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.

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

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.

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

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 57 ◽  
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.

A Novel Bearing Fault Diagnosis Method Based on LMD and Wavelet Packet Energy Entropy

2017 ◽  
Vol 18 (5) ◽  
Author(s):  
Xiumei Li ◽  
Yong Liu ◽  
Huimin Zhao ◽  
Wu Deng ◽  
Yannan Sun
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Peak Frequency ◽  
Band Pass Filter ◽  
Rolling Element Bearings ◽  
Pass Filter ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Rolling Element ◽  
Diagnosis Method

AbstractRolling element bearings faults may lead to fatal breakdown of machines. Therefore, it is significant to be study bearings diagnosis, and the vibration-based methods have received intensive study because vibration signals collected from bearings carry rich information on machine health conditions, and it is possible to obtain vitalcharacteristic information from the vibration signals through using signal processing techniques. This paper proposes a novel vibration-based diagnosis method about bearing faults, first, a new pattern recognition method is proposed to diagnose bearing faults through using the interval value of the spectral peak frequency in the frequency domain; second, vibration signals of different parts faults of the bearings will be processed by different algorithm for precisely extracting the fault characteristics; and third, in order to extract transient characteristics from a noisy signal, the filter need to be developed and to further improve the signal-to-noise ratio (SNR), band pass filter is designed based on the PSD of vibration signals in this paper. The vibration signals collected from rolling element bearings are used to demonstrate the performance of the proposed method, andthe results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings.

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

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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