An Improved Correlation-Based Anomaly Detection Approach for Condition Monitoring Data of Industrial Equipment

An improved latent correlation anomaly detection (LCAD) method is proposed to detect anomalies from condition monitoring datasets of industrial equipment. Above all, original data were segmented to various work cycles. Then, latent correlation vector (LCV) was used to denote the latent correlation among different parameters. Based on a latent correlation probabilistic model (LCPM), an anomaly detection function (ADF) is formulated to determine the state of equipment. In order to compare this method with previously reported anomaly detection methods, simulated datasets were constructed to evaluate the effectiveness of this method. Another experiment was also conducted to test the applicability of this method based on real flight datasets. Both experiments demonstrated superior accuracy and much lower missing alarm rates of this improved LCAD method.