Diagnostic Classification Based on Nonlinear Representation and Filtering of Process Measurement Data

Reliable monitoring and diagnosis of industrial processes is quite important for in terms of quality and safety. The goal of fault diagnosis is to find process variables responsible for causing specific abnormalities of the process. This work presents a classification-based diagnostic scheme based on nonlinear representation of process data. The use of a nonlinear kernel technique is able to reduce the size of the data considered and provides efficient and reliable representation of the measurement data. As a filtering stage a preprocessing is performed to eliminate unwanted parts of the data with enhanced performance. The case study of an industrial batch process has shown that the performance of the scheme outperformed other methods. In addition, the use of a nonlinear representation technique and filtering improved the diagnosis performance in the case study.