Mechanical vibration signal compression based on speech codecs for intelligent manufacturing

Abstract

ABSTRACT How to effectively compress mechanical signals so that they can support remote and real-time health monitoring is a hot issue in the context of intelligent manufacturing. Due to the characteristics of small repeatability, high noise, and high numerical accuracy of vibration signal, traditional compression algorithms cannot well balance the compression ratio, reconstruction quality, compression time indicators. Therefore, this paper presents a novel mechanical vibration signal compression scheme based on speech codecs, which can realize signal compression in many scenarios such as tool wear detection and bearing fault diagnosis. In this work, mechanical vibration signal compression is first transformed into a multi-objective optimization problem, and the feasibility of solving this problem from the perspective of speech coding is deeply analyzed. Further, this paper presents five possible compression methods with different compression ratios. The presented methods are evaluated with real-time data for tool wear detection in stone processing and public datasets for bearing fault diagnosis. The experimental results show that the proposed methods achieve high compression ratios (4.0 ~ 24.6) while ensuring low time overhead (the average time for compressing a sample point is not more than 0.0194 ms) and high reconstruction quality (the MSE index all less than 0.008), outperforming other state-of-the-art methods.