Article

Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

Department of Instrumentation and Automation, Petroleum University of Technology, Tehran, Iran
Energy (Impact Factor: 4.16). 12/2010; 35(12):5472-5482. DOI: 10.1016/j.energy.2010.06.001

ABSTRACT The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults.

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    • "Most of the studies reported in literature on fault diagnosis using neural networks or support vector machine involve chemical systems with a separator as one of its components (Hussain et al., 2013; Rusinov et al., 2013; Salahshoor et al., 2010). This process is selected because it reveals the most common features appearing in industrial processes. "
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    • "ANNs have been applied to nuclear reactor monitoring in [15], [16]. In [22], the fusion of Support Vector Machines (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for fault detection and diagnosis in industrial steam turbines. Many other related CI-based approaches for plant monitoring can be found in literature. "
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