Indirect Prediction of Spindle Rotation Error Through Vibration Signal Based on Supervised Local Mean Decomposition Filter Fusion and Bi-LSTM Classification Network

Abstract

Spindle rotation error directly correlates with the quality of mechanical processing. Currently, the error was mainly converted through measuring the distance information of standard component installed at the tool position, and it can't complete the normal machining because the tool is occupied. Therefore, a novel self-adaptive supervised learning method through easy-collected vibration signal that don't affect the machining to indirect predict the error. This method includes three steps: First, the original vibration signal is decomposed by local mean decompression (LMD) method to obtain two critical components; subsequently, the two components are fused as a signal by a weighted-average approach; finally, the fused signal and corresponding error are self-adaptive supervised trained by the setting termination condition to modify fusion coefficient and network parameters. The method is used to analyze the data-set of spindle platform, which has collected the experimental data at speeds 1000, 2000, 3000, and 4000 more than 170 groups, and the indirect prediction accuracy reached 94.12%, 92.35%, 97.68%, and 90.59%, respectively. Additionally, the experimental results were compared and demonstrated by three aspects with current different algorithms.