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One of the challenges of utilizing soft sensors is that their prediction accuracy deteriorates with time due to multiple factors, including changes in operating conditions. Once soft sensors are designed, a mechanism to maintain or update these models is highly desirable in industry. This paper proposes an index that can monitor the prediction performance of soft sensor models and provide guidance about when to update these models. In the proposed approach, a Kalman filter based model mismatch index is developed to monitor the prediction performance of soft sensors with the support of traditional process monitoring indexes, T 2  and SPE. Then, the soft sensor model can be updated through partial least squares (PLS) regression by using samples from the off-line training set and new process conditions. The proposed online update method is applied to an industrial process case study and the effectiveness of the proposed approach is demonstrated by comparing with traditional recursive partial least squares (RPLS).

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Soft Sensor Model Maintenance: A Case Study in Industrial Processes∗∗The authors would like to acknowledge the support from the DOW chemical company and the natural sciences and engineering research council of Canada (NSERC).