A Wavelet-Based Evolving Fuzzy Framework for Fault Diagnosis in the Tennessee Eastman Process

Evolving fuzzy systems (EFS) offer an incremental learning, making them promising for fault diagnosis (FD) in industrial processes, where unknown faults and changing operation conditions are common. The evolving fuzzy structure enables incremental rule adaptation while maintaining interpretability and reduced computational complexity compared with deep learning approaches. However, the performance of EFS depends heavily on the preprocessing of input data. This study evaluates eight preprocessing strategies for EFS applied to the Tennessee Eastman benchmark process. A one-vs-rest EFS architecture was implemented for ten representative faults (IDV1, IDV2, IDV4, IDV5, IDV6, IDV7, IDV8, IDV10, IDV13 and IDV14) in order to make a comparison with other FD techniques. This approach uses seven variables selected by using the least angle regression. Preprocessing methods were applied to highlight fault signatures. Using the Daubechies-4 in the preprocessing achieved the best overall F1-score (73.68%) with a sensitivity of 97.37%, outperforming the no-preprocessing baseline (F1 = 70.67%). Per-fault analysis showed high performance for faults IDV6, IDV7, and IDV14, while IDV1, IDV2, IDV5, and IDV8 exhibited high sensitivity but lower specificity. These findings indicate that wavelet preprocessing significantly enhances EFS for FD, and that the choice of wavelet should be guided by application priorities: Daubechies-4 is recommended for maximum detection and fewer false alarms. The obtained results demonstrate that wavelet preprocessing substantially improves classification robustness and fault discrimination compared with the non-preprocessed baseline.