Online detection of analog signal anomaly based on the evaluation of local trends

Abstract

Online anomaly detection of analog signals plays an important role in equipment fault diagnosis and predictive maintenance. However, the signal often deviates slightly from those seen previously in the early stage of equipment failure, and the anomaly is invisible to the human eye. This kind of anomaly belongs to the typical contextual anomaly. Whether this anomaly can be effectively detected determines whether the failure of the equipment can be detected in the early stage, which is of great significance for safety in production. This study aimed to propose an online anomaly detection method for the analog signals of the quasi-sine waveform class. The sample similarity in the sliding window was evaluated using a sample trend rather than sample amplitude deviation to detect anomalies based on the principle that the trend of the quasi-sinusoid waveform signal in the adjacent space was similar. Compared with the traditional method, the proposed method was sensitive to contextual anomalies and did not need a complete sample data set for model training. The proposed method was finally validated by three data sets with good results.