Abstract
The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.
Highlights
The rolling bearing is one of the most commonly used components in rotating machinery and equipment [1,2]
It is of great significance to study the incipient fault detection of rolling bearings, find the incipient signal characteristics of faults, and eliminate the safety hazards in time when the fault has not developed toward a serious degree [8,9]
Aiming at the problems brought by the weak incipient fault characteristics, this paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings
Summary
The rolling bearing is one of the most commonly used components in rotating machinery and equipment [1,2]. The running state of the rolling bearings are affected by shock and vibrations, and incomplete statistics show that about 30% of rotating machinery failures are caused by bearing failures [3,4]. The failures of rolling bearings often develop from the normal stage to the incipient stage, enter the stage of repeated failure, and reach the stage of complete breakdown. Some scholars have established rolling bearings degradation models by analyzing the prior physical knowledge of rolling bearings [10,11], or completed the incipient fault detection of rolling bearings by analyzing the characteristic frequency of failure of rolling bearings [12,13]. The data-driven method is used to finding incipient failures by analyzing the collected data, and is more suitable
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call