Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers

Vibration signals acquired from bearing have been found to demonstrate complicated nonlinear characteristics in literature. Fractal geometry theory has provided effective tools such as fractal dimension for characterizing the vibration signals in bearing faults detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant fractal dimension at all scales may not be true. Motivated by this fact, this work explores the application of the multi-scale fractal dimensions (MFDs) based on morphological cover (MC) technique for bearing fault diagnosis. Vibration signals from bearing with seven different states under four operations conditions are collected to validate the presented MFDs based on MC technique. Experimental results reveal that the vibration signals acquired from bearing are not critical self-similar fractals. The MFDs can provide more discriminative information about the signals than the single global fractal dimension. Furthermore, three classifiers are employed to evaluate and compare the classification performance of the MFDs with other feature extraction methods. Experimental results demonstrate the MFDs to be a desirable approach to improve the performance of bearing fault diagnosis.

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Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

Sensors ◽

10.3390/s21217319 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7319

Author(s):

Jiajun He ◽

Ping Wu ◽

Yizhi Tong ◽

Xujie Zhang ◽

Meizhen Lei ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Vibration Signals ◽

Maintenance Costs ◽

One Dimensional ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Diagnosis Method

Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.

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Bearing fault diagnosis based on DNN using multi-scale feature fusion

2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC) ◽

10.1109/yac51587.2020.9337689 ◽

2020 ◽

Author(s):

Funa Zhou ◽

Zhiqiang Zhang ◽

Danmin Chen

Keyword(s):

Fault Diagnosis ◽

Feature Fusion ◽

Scale Feature ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale

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Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM

Applied Intelligence ◽

10.1007/s10489-021-02229-1 ◽

2021 ◽

Author(s):

Fengqian Zou ◽

Haifeng Zhang ◽

Shengtian Sang ◽

Xiaoming Li ◽

Wanying He ◽

...

Keyword(s):

Fault Diagnosis ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Morphological Filtering ◽

Weighted Entropy

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A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

Applied Sciences ◽

10.3390/app8091621 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1621 ◽

Cited By ~ 11

Author(s):

Fan Jiang ◽

Zhencai Zhu ◽

Wei Li ◽

Yong Ren ◽

Gongbo Zhou ◽

...

Keyword(s):

Fault Diagnosis ◽

Correlation Coefficient ◽

Feature Space ◽

Support Vector ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Acceleration Sensors ◽

Reconstructed Signal ◽

Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

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Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation

Measurement ◽

10.1016/j.measurement.2021.109885 ◽

2021 ◽

pp. 109885

Author(s):

Ruxue Bai ◽

Quansheng Xu ◽

Zong Meng ◽

Lixiao Cao ◽

Kangshuo Xing ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Data Augmentation ◽

Rolling Bearing ◽

Convolution Neural Network ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Fusion Data

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Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

Shock and Vibration ◽

10.1155/2018/1063645 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Fan Jiang ◽

Zhencai Zhu ◽

Wei Li ◽

Bo Wu ◽

Zhe Tong ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Optimal Number ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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Efficient Bearing Fault Diagnosis by Fast-Residual Network With 2-D Representation of Vibration Signals

10.1109/iciba50161.2020.9276998 ◽

2020 ◽

Author(s):

Yunxiang Zhang ◽

Zhuyi Rao

Keyword(s):

Fault Diagnosis ◽

Residual Network ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Bearing fault diagnosis from raw vibration signals using multi-layer extreme learning machine

2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) ◽

10.1109/icemi46757.2019.9101840 ◽

2019 ◽

Author(s):

Zhao Guangquan ◽

Wu Kankan ◽

Gao Yongcheng ◽

Liu Yongmei ◽

Hu Cong

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Learning Machine

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Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Applied Sciences ◽

10.3390/app10207068 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7068

Author(s):

Minh Tuan Pham ◽

Jong-Myon Kim ◽

Cheol Hong Kim

Keyword(s):

Acoustic Emission ◽

Feature Extraction ◽

Deep Learning ◽

Fault Diagnosis ◽

Extraction Methods ◽

High Accuracy ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

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