A multi-attribute classification fusion system for insulated gate bipolar transistor diagnostics

Effective health diagnosis provides benefits such as improved safety, improved reliability, and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a multi-attribute classification fusion system which leverages the strengths provided by multiple membership classifiers to form a robust classification model for insulated gate bipolar transistor (IGBT) health diagnostics. The developed diagnostic system employs a k-fold cross-validation model for the evaluation of membership classifiers, and develops a multi-attribute classification fusion approach based on a weighted majority voting with dominance scheme. An experimental study of IGBT degradation was first carried out for the identification of failure precursor parameters, and classification techniques (e.g., supervised learning, unsupervised learning, and statistical inference) were then employed as the member algorithms for the development of a robust IGBT classification fusion system. In this study, the developed classification fusion model based on multiple member classification algorithms outperformed each stand-alone method for IGBT health diagnostics by providing better diagnostic accuracy and robustness. The developed multi-attribute classification fusion system provides an effective tool for the continuous monitoring of IGBT health conditions and enables the development of IGBT failure prognostics systems.