Sparse representation and dictionary learning-based bearing fault classification method

Abstract

The invention discloses a sparse representation and dictionary learning-based bearing fault classification method. The method is characterized by comprising the following steps of: preprocessing acquired history bearing vibration signals to obtain vibration signals under different working states, respectively constructing corresponding sub-dictionaries and combining the sub-dictionaries into a redundant dictionary; and online acquiring a bearing vibration signal by using a sensor, solving a sparse coefficient of the signal under the redundant dictionary by using a generalized orthogonal matching pursuit algorithm, and classifying the vibration signal through a reconstruction error so as to identify the bearing working state. According to the method, relatively good classification effect can be obtained, the dictionary training process can be accelerated and the ability, of being adapted to target signals, of the dictionary is improved, so that the method is capable of realizing the sparse decomposition of complex vibration signals more efficiently and is used for fault identification.

Description

technical field [0001] The invention relates to the field of rolling bearing vibration signal processing methods, in particular to a bearing fault classification method based on sparse representation and dictionary learning Background technique [0002] Bearings are one of the most important mechanical parts in rotating machinery. They are widely used in various important fields such as electric power, chemical industry, metallurgy, and aviation. At the same time, bearings are also one of the most easily damaged components. The quality of bearing performance and working conditions directly affects the performance of the entire machine equipment, and its defects will cause abnormal vibration and noise of the equipment, and even cause equipment damage. Therefore, it is particularly important to diagnose rolling bearing faults, especially for the analysis of early faults. In the process of collecting equipment vibration signals, various co-occurring factors such as noise and s...

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Problems solved by technology

[0004] Aharon et al. proposed the K-SVD algorithm, which implements dictionary learning through iterations of dictionary updating and coefficient solving. However, when the algorithm uses matching pursuit to solve sparse coefficients, each iteration selects the most relevant atom in the dictionary, making the method Time-consuming to solve

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