An Explainable Adaptive Anomaly Detection Method for Multi-Condition Intelligent Equipment Monitoring

This paper addresses the challenges of weak parameter correlation, low detection accuracy, and poor interpretability in anomaly detection for rotating machinery under multi-condition operating scenarios. An explainable adaptive anomaly detection method is proposed. First, sensitivity and correlation analyses are employed to optimize the input parameters, and a spatial memory matrix is constructed by integrating an improved K-Nearest Neighbors algorithm with K-means clustering. Second, a multi-parameter anomaly detection model based on multivariate state estimation technique and sequential probability ratio test is developed to enable adaptive diagnosis of equipment operating conditions. Finally, error statistics are used to model the contribution trajectories of anomalous parameters, combined with a cumulative anomaly contribution rate metric to enhance the interpretability of anomaly localization. Experimental results show that the proposed method attains an average accuracy of 97.47% on multi-condition datasets, underscoring its wide applicability in industrial equipment monitoring