A non-intrusive three-way catalyst diagnostics monitor based on support vector machines

The three-way catalytic converter performance degrades as it ages over time due to many phenomenon such as catalyst poisoning, sintering or physical damage of the instrument. To reduce the emission impact on environment, the Environmental Protection Agency (EPA) regulations requires the on-board diagnostics (OBD) method to set a flag (fault code) once the catalyst reaches its threshold. In this work, we propose a support vector machine based non-intrusive classification method to diagnose the catalyst as it ages. To further improve the model robustness and to reduce the size of support vectors, multiple clustering algorithms were evaluated. The model was tested on multiple catalyst systems under various operating conditions and good results were observed.

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