Anomaly detection on industrial Internet time series based on SR algorithm

The Spectral Residual (SR) algorithm is originally proposed as an algorithm for image saliency detection, which can also be used for unsupervised anomaly detection of time series. The improvement of SR algorithm from frequency domain transformation, smoothing algorithm, removing the seasonal influence, and adjusting the abnormal judgment threshold were studied, and a multivariate time series anomaly detection method based on SR algorithm was proposed. Experiments showed that the improvement proposed in this paper could improve the accuracy of anomaly detection, remove the seasonal influence caused by environmental factors, and detect anomalies in a better time than the existing algorithms. In addition, the proposed algorithm could adaptively adjust the abnormal judgment threshold according to actual needs. To adapt to the multivariate time series data that often appear in industrial systems, the Independent Component Correlation Algorithm (ICA) for processing multivariate data was combined on the basis of the SR algorithm, so that the algorithm was suitable for multivariate time series. Experiments showed that the algorithm combining spectral residual algorithm and independent component analysis could be applied to automatic detection of anomalies in industrial systems, and could ensure the accuracy and real-time performance required by the algorithm. © 2024 CIMS. All rights reserved.