Inverse fuzzy fault model for fault detection and isolation with least angle regression for variable selection

Fault detection is paramount in industrial processes because expensive reparations can be avoided, and the normal flow of operations is not disrupted. However, it is difficult to use a model-based method for fault detection in complex systems. Thus, a data-driven approach is implemented in the Tennessee Eastman process for fault detection and isolation (FDI). As a contribution, this paper proposes an inverse fuzzy fault model to detect and isolate faults. To reduce the amount of data to process, the least angle regression is applied for variable selection. To compare the detection and isolation times obtained using the fuzzy fault model, a fuzzy classifier is described; where the signals are preprocessed with the wavelet transform to highlight the faulty signals. The inverse fuzzy fault model has only four fuzzy rules and shows a smaller isolation time than the required using the fuzzy classifier.

Automated summary

The paper proposes an inverse fuzzy fault model for fault detection and isolation, with the application of least angle regression for variable selection and wavelet transform preprocessing to highlight faulty signals for reducing data processing.