Abstract
This study proposes a detection method that incorporates the extension theory with fractional-order chaotic self-synchronization of dynamic errors in order to analyze ball bearing signals. A fractional-order Chen–Lee chaotic system (FOCLCS), which is capable of detecting slight changes in signals, is used to extract the obvious characteristics of signal disturbance. A master–slave synchronization system compares normal-state signals with fault signals to generate dynamic errors, which are extracted for synchronization and comparison. Then, a matter-element model is established based on the extension theory to enable accurate identification of the ball bearing signals. According to MATLAB simulation results, the proposed detection method integrating the extension theory with fractional-order chaotic synchronization of dynamic errors achieves 100% accuracy with a smaller amount of computation and a shorter computation time than those required by the conventional detection methods and is therefore advantageous to real-time monitoring. When applied to machine tools, the proposed detection method can serve as an aid to their online real-time analysis system.
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