Matrix concatenation feature fusion-based multivariate time series anomaly detection and diagnosis algorithm in water treatment cyber-physical systems

Key Points

• Anomaly detection significantly improves with the matrix concatenation feature fusion method, enhancing diagnostic accuracy.
• The algorithm effectively processes multivariate time series data, yielding a detection rate of 92% in real-time applications.
• Assessment using advanced feature fusion techniques allows for immediate diagnosis, addressing anomalies promptly.
• Impacts on cyber-physical systems in water treatment highlight the importance of real-time monitoring and early detection.