Statistical process control versus deep learning for power plant condition monitoring

• Published in: Computers & chemical engineering 2023-10, Vol.178, p.108391, Article 108391
• Main Authors: Hansen, Henrik Hviid, Kulahci, Murat, Nielsen, Bo Friis
• Format: Article
• Language: English
• Subjects: Anomaly detection; Condition monitoring; Deep learning; Fault detection; Machine learning; Statistical process control
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2023.108391

This study compares four models for industrial condition monitoring including a principal components analysis (PCA) approach and three deep learning models, one of which is a new, lightweight version of another. We also propose a simple attention mechanism for enchancing deep learning models with better predictions and feature importance. Two datasets are used, one simulated from the Tennessee Eastman Process, the other from two feedwater pumps at a Danish combined heat and power plant. Our final results show evidence in favour of the PCA-based approach as it has detection ability comparable to the deep learning approaches as well as faster training time, fewer hyperparameters, as well as robustness to changing operating conditions. We conclude the paper by putting into perspective the importance of building up complexity incrementally with a recommendation to start modelling with simpler and well-tested models before the adoption of more advanced, less transparent models. •A statistical model is compared with deep learning techniques for fault detection.•The principal components model’s fault detection is as good as deep learning.•Deep learning method, ARGUELite, shows promise for single regime datasets.•A unique dataset from a power plant feedwater pump was used for model evaluation.