Statistical process control versus deep learning for power plant condition monitoring

Henrik Hviid Hansen*, Murat Kulahci, Bo Friis Nielsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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.

Original languageEnglish
Article number108391
JournalComputers and Chemical Engineering
Number of pages17
Publication statusPublished - 2023


  • Anomaly detection
  • Condition monitoring
  • Deep learning
  • Fault detection
  • Machine learning
  • Statistical process control


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