ShapeRef: A Representation Method of Industrial Abnormal Time-Series Waveform Based on Shape Reference
Lin Shi, Changyou Zhang, Shuai Yang, Wenjia Wu, Bo Wen, Ji Ma · 2023
Time-series waveform data widely exist in various industrial fields, such as equipment monitoring and fault diagnosis. The current time series representation methods have limitations when dealing with industrial abnormal time-series waveforms, such as limited applicability, semantic ambiguity, and time distortion. This work proposes a novel shape reference-based representation method for industrial abnormal time-series waveform (ShapeRef), which takes the shape of the standard waveform as a reference to represent the anomaly deviation. Specifically, ShapeRef first establishes a time-series shape reference frame, then proposes the minimum shape difference-based mapping method to describe the mapping process of coordinates, and finally reduces multi-intersection points in the mapping process to achieve uniform mapping of the abnormal time-series waveform. Experimental results show that ShapeRef can effectively represent abnormal time-series waveforms and outperforms several baseline methods in the clustering task of a real industrial equipment waveform dataset. This work enhances the accuracy and reliability of industrial equipment monitoring and fault diagnosis, which could have significant practical implications.