Abstract

Research on the intelligent fault diagnosis method of rolling bearing based on laboratory data has made some achievements. However, due to the change of working conditions and the lack of historical data of the same equipment in the actual diagnosis, some methods mostly have problems such as poor generalization. Model training and verification data are insufficient, and engineering practice still lacks effective intelligent fault diagnosis methods. In this paper, we propose a weighted k-nearest neighbor (WKNN) fault diagnosis model based on multi-dimensional sensitive features, and propose a fault diagnosis method for rolling bearings that adapts to different equipment and different operating conditions. First, we extract time domain, frequency domain, and entropy features of the original signal to form the raw signal feature set. Then, the iterative ReliefF feature screening method is used to evaluate the joint feature set, calculate the weight of each feature, remove insensitive and redundant features, and obtain a high-dimensional sensitive feature set. Finally, the WKNN classification model is used to identify bearing failure modes. The fault diagnosis model was trained using rolling bearing data from the Case Western Reserve University (CWRU), while laboratory data from the Intelligent Maintenance System (IMS), the Society of Mechanical Failure Prevention Technology (MFPT) and the engineering case data were used for testing. The results show that the model proposed in this paper has high fault diagnosis accuracy and can accurately determine the fault type after early warning. Compared with other comparison methods, the fault recognition accuracy rate is higher. And it is suitable for different working conditions and different equipment, and has good engineering application value.

Highlights

  • With the development of modern industrial Internet, big data analysis, and in particular artificial intelligence, has greatly promoted the development of intelligent fault diagnosis and made great strides towards predictive maintenance (PDM)

  • The weighted k-nearest neighbor (WKNN) classification model based on multi-dimensional sensitive features proposed in this paper shows good fault recognition accuracy for equipment under the same operating conditions, the same equipment under different operating conditions, and different equipment with different operating conditions

  • In this paper, a weighted k-nearest neighbor classification model based on multi-dimensional sensitive features is proposed as the rolling bearing fault diagnosis method

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Summary

Introduction

With the development of modern industrial Internet, big data analysis, and in particular artificial intelligence, has greatly promoted the development of intelligent fault diagnosis and made great strides towards predictive maintenance (PDM). Rotating machinery is the most common type of mechanical. According to statistics, rolling bearing faults account for 30% to 40% of the common rotating equipment faults [1]. If incipient fault diagnosis can be achieved and appropriate predictive maintenance measures can be taken in time, planned shutdowns and replacement of parts for high-end equipment can be arranged well before a failure occurs.

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