Rolling bearing fault detection method based on acoustic vibration signal fusion

Abstract

The invention provides a rolling bearing fault detection method based on acoustic vibration signal fusion, which comprises the following steps of: firstly, extracting a vibration signal and a sound signal of a rolling bearing to be detected, and establishing a sliding rectangular window function of the sound signal; secondly, by starting from a first signal point of a to-be-detected vibration signal, establishing a fusion signal after each time of movement by moving a rectangular window, solving a root-mean-square value and a mean-flexible value of the fusion signal to obtain a mean-slip value, and finding out an optimal fusion signal; and finally diagnosing a fault of a bearing to be detected by judging a value range of an approximate fault characteristic frequency where the optimal fusion signal is located. According to the invention, the problems that the vibration sensor arrangement is limited, the amplitude at the fault characteristic frequency is low and the like are solved, meanwhile, the problem that the signal-to-noise ratio is low due to the fact that sound signals are affected by background noise is solved, weak faults of a rolling bearing can be accurately recognized, the diagnosis accuracy and diagnosis efficiency are improved, and a good effect is achieved in rolling bearing state monitoring.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a rolling bearing fault detection method based on acoustic vibration signal fusion. Background technique [0002] Rolling bearings are one of the most widely used components in rotating machinery, and are widely used in important fields such as machining, metallurgy, chemical industry and aviation. Because rolling bearings are often in high-temperature, high-speed and heavy-load working environments, rolling bearings are easily damaged. Once they fail and are not detected in time, it will cause machine failure and unexpected shutdown, resulting in huge economic losses and even threats. to the personal safety of workers. Therefore, in rotating machinery, fault diagnosis of rolling bearings plays a vital role. [0003] The vibration signals of rolling bearings contain a lot of information and are easy to obtain. They have been widely used in the fault diagnosis of...

AI Technical Summary

Problems solved by technology

However, the vibration signal is obtained through contact measurement, and the layout of the vibration sensor is limited by the environment, so it is not suitable for installation in a working environment with high temperature, high humidity and high corrosion, and the amplitude of the fault frequency is low, so it is difficult to detect weak faults , and the acoustic signal is obtained through non-contact measurement, which can improve the limitation of contact measurement of vibration signal, and has become a new research method in fault diagnosis

However, in the process of collecting acoustic signals, fault feature information is easily affected by background noise, resulting in low signal-to-noise ratio and insufficient diagnostic accuracy.

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