Data and Knowledge Driven Adaptive Evidential Soft Sensor Model for Monitoring Unmeasured Parameters

AbstractTo solve the monitoring or predicting for the unmeasured parameters, which can not be monitored online and offline by device in industrial processes, a method, called data and knowledge driven adaptive evidential soft sensor model, was proposed based on evidence theory. In the proposed method, the imprecise training samples, applied to identify the proposed soft sensor, are constructed from running data by means of data analysis and expertise knowledge. For a given input vector x, the method provides a prediction regarding the value of the output variable y, in the form of fuzzy belief assignment (FBA), defined as a collection of fuzzy sets of values with associated masses of belief. The output FBA is computed using a nonparametric, instance-based approach: imprecise training samples in the neighborhood of x are considered as sources of partial information on the response variable; the pieces of evidence are discounted as a function of their distance to x, and pooled by using Dempster's rule of combination. Taking into account the unmeasured parameter level of coal powder filling in tubular ball mill as the example, some experiments were conducted to validate the proposed method.