Against the backdrop of the rapid development of intelligent manufacturing and the wide application of edge computing, the multi-source high-dimensional data generated during the operation of processing equipment has put forward higher requirements for the storage, communication and energy consumption control capabilities of the system. Especially when achieving preventive maintenance of key components, how to efficiently handle vibration signals with high collection frequency and large information volume has become the research focus. To solve the problem of low efficiency of traditional compression methods when dealing with complex signal structures, this paper proposes an automatic classification and hierarchical compression algorithm for edge-end operating state data based on sequence autocorrelation feature analysis and signal stability determination. This algorithm can perform dynamic identification and classification according to signal characteristics, thereby matching the optimal compression strategy. Specifically, the system first identifies the periodically prominent high-frequency vibration signals through the correlation analysis of the sampling sequence, and then further distinguishes the steady-state and slow-varying signals in the non-vibration data through stability analysis to achieve precise division of the data stream. In terms of the selection of compression methods, this paper designs a lossless compression mechanism combining difference transform and entropy coding for data sequences with relatively stable structures. For the slowly changing trend signal, an adaptive compression scheme integrating forward prediction and arithmetic coding was constructed; For vibration data with intense fluctuations, a joint modeling and quantization coding strategy in the time-frequency domain is introduced, achieving efficient lossy compression while maintaining feature retention. The experimental results show that the classification compression framework proposed in this paper effectively reduces resource overhead while improving the compression ratio, providing practical and feasible technical support for edge intelligent processing in the mechanical manufacturing process.

Jiayi Sun. Research on Intelligent Classification and Typing Compression Algorithm for Machining State Data Based on Autocorrelation Coefficient and Stability Discrimination. Kinetic Mechanical Engineering (2025), Vol. 6, Issue 1: 11-25. https://doi.org/10.38007/KME.2025.060102.

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