Failure precursor detection in complex electrical systems using symbolic dynamics

Failures in a plant's electrical components are a major source of performance degradation and plant unavailability. In order to detect and monitor failure precursors and anomalies early in electrical systems, we have developed a signal processing method that can detect and map patterns to an anomaly measure. Application of this technique for failure precursor detection in electronic circuits resulted in robust detection. This technique was observed to be superior to conventional pattern recognition techniques such as neural networks and principal component analysis for anomaly detection. Moreover, this technique based on symbolic dynamics offers superior robustness due to averaging associated with experimental probability calculations. It also provided a monotonically increasing smooth anomaly plot which was experimentally repeatable to a remarkable accuracy.