Characteristic vector extraction method for rolling bearing fault mode identification and state monitoring

Abstract

The invention discloses a characteristic vector extraction method for rolling bearing fault mode identification and state monitoring. The time wavelet energy spectrum fuzzy entropy of rolling bearing vibration signals is used as a characteristic vector so that rolling bearing fault mode identification can be realized, the operation state of a rolling bearing can also be monitored in real time and the early fault in the operation process of rolling bearing can be timely diagnosed. According to the time wavelet energy spectrum fuzzy entropy characteristic vector extraction method, the method can be simultaneously used for mode identification and operation state monitoring of different fault types of the rolling bearing so that the defect of the conventional method of respectively processing the two problems can be overcome, and the range of the similar research method for fault diagnosis of the rolling bearing can be greatly extended. Besides, the time wavelet energy spectrum fuzzy entropy acts as a single characteristic vector so that the method has higher fault mode identification efficiency in comparison with the multi-characteristic vector analysis method. Compared with the conventional rolling bearing operation state monitoring indicators, the method is more timely and accurate in monitoring the operation state of the rolling bearing.

Description

technical field [0001] The invention relates to a feature vector extraction method for rolling bearing fault pattern recognition and state monitoring, belonging to the technical field of mechanical fault diagnosis and signal processing. Background technique [0002] Rolling bearing is the core component of transmission machinery, and plays a pivotal role in maintaining the position and rotation accuracy of the rotating shaft. According to various statistical results, rolling bearing is one of the components with the highest failure and damage rate in rotating machinery. The failure of rolling bearings can cause the entire mechanical system to shut down, causing serious economic losses and even catastrophic accidents. With the rapid development of computer technology, it has become more and more extensive to use signal processing technology combined with intelligent diagnosis methods to identify different types of failure modes and monitor operating conditions of rolling bear...

AI Technical Summary

Problems solved by technology

The eigenvectors traditionally used for monitoring the operating state of rolling bearings are mainly kurtosis index and root mean square value index. Although these two eigenvectors can draw the operating state curve of rolling bearings, the real-time performance for monitoring the operating state of rolling bearings is poor. It is difficult to monitor the occurrence of early failures of rolling bearings in time, and it is difficult to meet the needs of real-time monitoring of rolling bearings in the modern machinery industry

However, in the existing technology, there is no related eigenvector extraction method that can well solve the two problems of rolling bearing fault pattern recognition and operation status monitoring

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