Fault Diagnosis of Rolling Bearing Based on Feature-Level Fusion Method

Bearing failure is one of the foremost causes of breakdowns in rotating machinery and such failure can be catastrophic. Fault diagnosis is critical to maintaining the normal operation of the bearings. This paper proposes feature-level fusion method for rolling bearing fault diagnosis. Features are extracted from eight vibration signals to constitute a fusion vector. SVM is used for pattern recognition. The case study results show that the proposed method is useful for rolling bearing fault diagnosis.

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Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Symmetry ◽

10.3390/sym14010013 ◽

2021 ◽

Vol 14 (1) ◽

pp. 13

Author(s):

Jianpeng Ma ◽

Chengwei Li ◽

Guangzhu Zhang

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Information Fusion ◽

Rolling Bearing ◽

Fusion Method ◽

Rolling Bearings ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Evolution Analysis ◽

Comparison Results

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

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An Improved Scheme for Vibration-Based Rolling Bearing Fault Diagnosis Using Feature Integration and AdaBoost Tree-Based Ensemble Classifier

Applied Sciences ◽

10.3390/app10051802 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1802 ◽

Cited By ~ 1

Author(s):

Bingxi Zhao ◽

Qi Yuan ◽

Hongbin Zhang

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Ensemble Classifier ◽

Normal Operation ◽

Network Construction ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Construction Methods ◽

Visualization Technology ◽

Effective Diagnosis

Bearings are key components in modern power machines. Effective diagnosis of bearing faults is crucial for normal operation. Recently, the deep convolutional neural network (DCNN) with 2D visualization technology has shown great potential in bearing fault diagnosis. Traditional DCNN-based fault diagnosis mostly adopts a single learner with one input and is time-consuming in sample and network construction to obtain a satisfied performance. In this paper, a scheme combining diverse DCNN learners and an AdaBoost tree-based ensemble classifier is proposed to improve the diagnosis performance and reduce the requirement of sample and network construction simultaneously. In this scheme, multiple types of samples can be constructed independently and employed for diagnosis simultaneously; next, the same number of DCNN learners are built for underlying features extraction and the obtained results are integrated and finally fed into the ensemble classifier for fault diagnosis. An illustration based on the Case Western Reserve University datasets is given, which proves the superiority of the proposed scheme in both accuracy and robustness. Herein, we present a universal scheme to improve the diagnosis performance, and give an example for practical application, where the signal preprocessing and image sample construction methods can also be applied in other vibration-based analysis.

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