A PLS based locally weighted project regression approach for fault diagnose of nonlinear process

In this paper, we firstly study the partial least squares (PLS) and locally weighted project regression (LWPR) methods. As a result, we find out that both methods leave some space for improvement. Then we put forward a new method, called LWPR-MPLS, to improve the traditional statistics and their thresholds. Our method reduces the computation load significantly and therefore can deal with nonlinear problems online. Besides, decomposition of the data in the space without orthogonal residuals brings additional benefits. Simulations show that our method improves the accuracy of fault diagnosis and the capacity of fault classification significantly in comparison with LWPR.

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