Temperature Sensor Faults Monitoring in a Heat Exchanger Using Evolving Fuzzy Classification

Abstract

In this paper, an advanced evolving clustering strategy is used to design a fuzzy model-based sensor faults detection mechanism for a pilot parallel-type heat exchanger. The change in the process operating mode is detected by an incremental unsupervised clustering procedure based on participatory learning. Real experimental data is used to construct signals for fuzzy residual generation. The resulting residuals are then processed by the evolving classifier to supervise the heat exchanger operation. The obtained results clearly show the ability of the evolving fuzzy classifier to early detect the considered temperature sensor faults.