Dynamic fusion of classifiers for fault diagnosis

—This paper considers the problem of temporally fusing classifier outputs to improve the overall diagnostic classification accuracy in safety-critical systems. Here, we discuss dynamic fusion of classifiers which is a special case of the dynamic multiple fault diagnosis (DMFD) problem [1]-[3]. The DMFD problem is formulated as a maximum a posteriori (MAP) configuration problem in tri-partite graphical models, which is NP-hard. A primal-dual optimization framework is applied to solve the MAP problem. Our process for dynamic fusion consists of four key steps: (1) data preprocessing such as noise suppression, data reduction and feature selection using data-driven techniques, (2) error correcting codes to transform the multiclass data into binary classification, (3) fault detection using pattern recognition techniques (support vector machines in this paper), and (4) dynamic fusion of classifiers output labels over time using the DMFD algorithm. An automobile engine data set, simulated un...

• Type: Conference
• Year: 2007
• Where: SMC
• Authors: Satnam Singh, Kihoon Choi, Anuradha Kodali, Krishna R. Pattipati, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov, Liu Qiao
• Keywords: Control Systems, Dynamic Fusion, Dynamic Multiple Fault, SMC 2007, Window Dynamic Fusion