Diagnosis of defects by principal component analysis of a gas turbine
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Diagnosis of defects by principal component analysis of a gas turbine Fenghour Nadir1 · Hadjadj Elias1 · Bouakkaz Messaoud1 Received: 27 September 2019 / Accepted: 18 April 2020 © Springer Nature Switzerland AG 2020
Abstract This study examines the application of the Principal Component Analysis (PCA) technique to detect the failures in complex industrial processes such as gas turbines used for electric power generation. The early detection of failures in such complex processes is indeed paramount to prevent product deterioration, performance degradation, significant property damage and human health. We identified the PCA model by determining the optimal number of principal components retained in the PCA model, then we validated the PCA model by checking the evolution of measurements and estimated the two variables X2 and X8. Thereafter, the evolution of three detection indexes is illustrated highlighting that the filtered SPE index is the best suited one for our installation, and finally, we checked the efficiency of the linear PCA method from the filtered SPE detection index using real data of defects that may occur within the gas turbine. The results obtained will aid to confirm the performance of the linear PCA method in the field of early failure detection. Thus, the PCA method appears as an efficacious tool to monitor and diagnose complex installations. Keywords Process monitoring · Fault detection · Linear principal component · Electric power production process
1 Introduction In all industrial systems, breakdowns cause considerable economic losses. It is therefore essential to implement monitoring and diagnostic systems to avoid unexpected shutdowns, increase reliability, and ensure the safety of underlying systems. In industrial diagnosis, the detection process involves merely the detection of events that affect the evolution of the systems, and the assessment consists in comparing the actual functioning systems with those under the assumption of normal operations. Originally, the diagnosis was limited to high-risk industrial applications such as nuclear or aviation domains and advanced industries such as armament and aerospace [1–5]. Over the past three decades, the diagnosis has attracted much attention both in the industrial and in the scientific research world. In the field of diagnostics, several methods based on the concept of redundancy of information have been developed. In previous works of other researchers, the
PCA, PLS, kernel PCA (kPCA) and kernel PLS (kPLS)-based generalized likelihood fault detection techniques have been developed, namely PCA, PLS, kPCA and kPLS have been implemented as a modeling framework for fault detection [6–9]. Their principle is generally based on a consistency test between an observed behavior of the process provided by sensors and an expected behavior provided by a mathematical representation of the process. Analytical redundancy methods, therefore, require a model of the system to be monitored. This model includes several parameters whose values are assumed to
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