The fault detection and diagnosis in rolling element bearings using frequency band entropy

Liu, Tao; Chen, Jin; Dong, Guangming; Xiao, Wenbing; Zhou, X N

doi:10.1177/0954406212441886

Cited by 32 publications

(18 citation statements)

References 25 publications

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“…Line SE 13 is effective for discrete one-dimensional signal feature extraction. The Shannon entropy 14,15 is used to measure the information amount of DIFs with different SE lengths, as shown in Figure 2. The larger the length of structural element, faster is the decrease in the information amount of DIFs.…”

Section: Multiscale Morphologymentioning

confidence: 99%

Multiscale morphological manifold for rolling bearing fault diagnosis

Feng

Zhang

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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The vibration signals of fault rolling bearing are high-dimensional information with complex components. In order to identify different classes of bearing fault, a new multiscale morphological manifold method based on multiscale morphology and manifold learning is proposed. The multiscale morphological manifold method consists of three main steps. Firstly, multiscale difference filter based on multiscale morphological transformation is applied to obtain multiscale observation results of each signal sample. Secondly, the nonlinear feature vectors of each signal sample are constructed according to the observation approach. Finally, manifold learning is introduced to extract the low-dimensional multiscale morphological manifold features through reducing the dimension of nonlinear features. The low-dimensional multiscale morphological manifold features can reveal the differences of signal classes, which are applicable for fault diagnosis. The performance of proposed method is tested by experimental data from bearings with different types of defects. Experimental verifications confirm that the proposed method is applicable and effective for rolling bearing fault diagnosis.

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Section: Multiscale Morphologymentioning

confidence: 99%

Multiscale morphological manifold for rolling bearing fault diagnosis

Feng

Zhang

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Once a component fails, the whole system will be paralyzed. Therefore, fault diagnosis has received increasing attention [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]. In general, three fault diagnosis methods are now taken into consideration by scholars: fault diagnosis methods based on a physical model [1,2], fault diagnosis methods based on knowledge [3,4,5,6] and data-driven methods [7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, data-driven methods rely on neither expert experience nor accurate physical model. Data-driven methods can obtain useful information by data mining technologies and have become practical diagnosis technologies at present [11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Based Fault Diagnosis with Missing Data Due to Multi-Rate Sampling

Chen

Yang

Zhou

2019

Sensors

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Deep learning is an effective feature extraction method widely applied in fault diagnosis fields since it can extract fault features potentially involved in multi-sensor data. But different sensors equipped in the system may sample data at different sampling rates, which will inevitably result in a problem that a very small number of samples with a complete structure can be used for deep learning since the input of a deep neural network (DNN) is required to be a structurally complete sample. On the other hand, a large number of samples are required to ensure the efficiency of deep learning based fault diagnosis methods. To solve the problem that a structurally complete sample size is too small, this paper proposes a fault diagnosis framework of missing data based on transfer learning which makes full use of a large number of structurally incomplete samples. By designing suitable transfer learning mechanisms, extra useful fault features can be extracted to improve the accuracy of fault diagnosis based simply on structural complete samples. Thus, online fault diagnosis, as well as an offline learning scheme based on deep learning of multi-rate sampling data, can be developed. The efficiency of the proposed method is demonstrated by utilizing data collected from the QPZZ- II rotating machinery vibration experimental platform system.

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“…However, precise physical model requirements limit its application in the field of fault diagnosis for complex mechanical equipment, and the processing of a quantity of prior knowledge limits its inference validation. Nowadays, data-driven techniques are widely applied in fault diagnosis since only historical data is required for establishing a fault diagnosis model [ 14 , 15 , 16 ]. The principal component analysis (PCA), support vector machine (SVM), and artificial neural network (ANN) are the most commonly used data-driven techniques for fault diagnosis [ 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Zhou

Yang

et al. 2018

Sensors

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Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

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The fault detection and diagnosis in rolling element bearings using frequency band entropy

Cited by 32 publications

References 25 publications

Multiscale morphological manifold for rolling bearing fault diagnosis

Multiscale morphological manifold for rolling bearing fault diagnosis

Transfer Learning Based Fault Diagnosis with Missing Data Due to Multi-Rate Sampling

A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

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