Enhancing Fault diagnosis for Chemical Processes via MSCNN with Hyperparameters Optimization

Fault diagnosis is critical for ensuring the safety and efficiency of chemical processes, as undetected faults can lead to catastrophic consequences. While deep learning-based methods have shown promise in this field, they often require manual hyperparameter tuning, which is not efficient since they heavily rely on expert knowledge and need iterative trial-and-error. This work introduces a novel approach combining a Multiscale Convolutional Neural Network (MSCNN) with Tree-Structured Parzen Estimator (TPE) for automated hyperparameter optimization to enhance the performance of fault diagnosis for chemical processes. The Multi-Scale Module is to capture complex nonlinear features from the fault data, while the TPE efficiently searches for optimal hyperparameters for MSCNN. An experimental study was carried out on the Tennessee Eastman Process (TE Process) dataset, where the proposed method was benchmarked against state-of-the-art models. The results indicate that the MSCNN-TPE method demonstrated improved performance in terms of precision and recall, achieving 5.26% and 5.63% higher values, respectively, compared to the CNN model. Comparisons of the MSCNN with default hyperparameters further confirmed the effectiveness of these techniques in improving fault diagnosis performance. Additionally, model ensemble technique was explored to further enhance the performance of the model and provide uncertainty estimations. In conclusion, this approach offers a robust and reliable solution for fault diagnosis in the chemical industry, enhancing process safety and efficiency.