Anomaly detection in sensor time series is a crucial aspect for raw data cleaning in gas turbine (GT) industry. In addition to efficiency, a successful methodology for industrial applications should be also characterized by ease of implementation and operation. To this purpose, a comprehensive and straightforward approach for detection, classification, and integrated diagnostics of gas turbine sensors (named DCIDS) is proposed in this paper. The tool consists of two main algorithms, i.e., the anomaly detection algorithm (ADA) and the anomaly classification algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering based on gross physics threshold application, intersensor statistical analysis (sensor voting), and single-sensor statistical analysis. Anomalies in the time series are identified by the ADA, together with their characteristics, which are analyzed by the ACA to perform their classification. Fault classes discriminate among anomalies according to their time correlation, magnitude, and number of sensors in which an anomaly is contemporarily identified. Results of anomaly identification and classification can subsequently be used for sensor diagnostic purposes. The performance of the tool is assessed in this paper by analyzing two temperature time series with redundant sensors taken on a Siemens GT in operation. The results show that the DCIDS is able to identify and classify different types of anomalies. In particular, in the first dataset, two severely incoherent sensors are identified and their anomalies are correctly classified. In the second dataset, the DCIDS tool proves to be capable of identifying and classifying clustered spikes of different magnitudes.

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