Abstract
Feature extraction plays a crucial role in the diagnosis of rotating machinery faults. However, the vibration signals measured are inherently complex and non-stationary and the features of faulty signals are often submerged by noise. The principle and method of blind source separation are introduced, and we point out that the blind source separation algorithm is invalid in an environment of strong impulse noise. In order to solve the problem of fast separation of multi-sensor signals in an environment of strong impulse noise, first, the window width of the median filter (MF) is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Next, the filtered signals are separated by the improved second-order blind identification (SOBI) algorithm. At the same time, the method is tested on the strong pulse background noise and rub impact dataset. The results show that this method has higher efficiency and accuracy than the direct separation method. It is possible to apply the method to real-time signal analysis due to its speed and efficiency.
Highlights
Rotating machinery is a kind of widely used power equipment, so it is of great significance to implement intelligent operation and maintenance management [1,2]
Only the local vibration signals collected by a single sensor have been used to solve the problem of fault identification of rotating machinery system, which has led to obvious difficulties
In order to solve the problem of fast fault feature extraction of rotating machinery, an improved blind source separation algorithm based on median filter (MF)-second-order blind identification (SOBI) is proposed
Summary
Rotating machinery is a kind of widely used power equipment, so it is of great significance to implement intelligent operation and maintenance management [1,2]. We should make full use of a series of sensors arranged at several key sections of the rotating machinery, and implement intelligent fault decision-making technology based on as much information as possible. This view has gained consensus on the research prospect of industrial big data technology [5]. In the process of researching the intelligent operation and maintenance of rotating machinery, a major question is how to effectively extract the sensitive quantitative features of its operational state from the nonlinear, strong noise, high-dimensional vibration signal fault feature dataset, which has significance for the development of intelligent decision-making technology driven by big data
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