Depth-Based Condition Monitoring and Contributing Factor Analysis for Anomalies in Combined Cycle Power Plant

Unexpected fault or failure in the power plant have caused high maintenance costs, the loss of energy production, and even safety issues. Developments in sensor technologies and data analytics have aided proper preventive maintenance actions for the system to improve asset availability and reduce repair costs. Nevertheless, effective condition monitoring of a power plant experiences a considerable nuisance from challenging issues such as inherent data characteristics such as high correlations between process variables, irrelevant information from environmental noises, and system complexity. To resolve these problems, this paper proposes an integrated monitoring scheme for performing efficient corrective actions by identifying the variables related to anomalies in combined cycle power plants. The scheme includes a clustering-based linear discriminant analysis to extract key variables for reducing dimensionality to efficiently handle the data, followed by employing the Mahalanobis depth statistics for anomaly detection and causal analysis via contribution scores. The proposed monitoring scheme is applied to condition monitoring data of a combined cycle power plant in South Korea, which include two types of anomalous operations. The reliability and robustness of the proposed condition monitoring scheme are validated by comparing other state-of-the-art methods. The proposed method shows a potential in efficiently detecting anomalies during operation and even early detecting the precursors of anomalies. It is expected to prevent imminent faults or failures by taking proper actions to relevant key process parameters of combined cycle power plant in advance.