Data-driven fault diagnosis for heterogeneous chillers using domain adaptation techniques. (July 2021)

• Record Type: Journal Article
• Authors: van de Sand, Ron; Corasaniti, Sandra; Reiff-Stephan, Jörg
• Is Part Of: Control engineering practice. Volume 112(2021)
• Publication Date: 2021-07
• Subjects: Fault diagnosis -- Chiller -- Domain adaptation -- Data-driven; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104815
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Automatic fault diagnosis is becoming increasingly important for assessing a chiller's degradation state and plays a key role in modern maintenance strategies. Data-driven approaches have already become well established for this purpose as they rely on historical data and are therefore more generally applicable compared to their model-based counterparts. Existing chiller fault diagnosis models, however, require labelled data from the target system, which are often not available. Therefore, in this paper, a data-driven fault diagnosis model is proposed that deploys domain adaptation techniques to enable the transfer of knowledge amongst heterogeneous chillers. In particular, the model utilizes transfer component analysis (TCA) and a support vector machine with adapting decision boundaries (SVM-AD) to diagnose faults by aggregating labelled source and unlabelled target domain data in the training phase. Furthermore, it is demonstrated how the model parameters can be tuned to ensure effective classification performance, which is then evaluated by use of fault data stemming from different chiller types. Experimental results show that with the proposed approach faults can be diagnosed with high accuracy for cases when labelled target domain data are not available. Highlights: Cross-domain fault diagnosis can be successfully applied for different chiller types. A set of transferable features across different chillers can be effectively learned. High fault diagnosis performance is achieved using the proposed model. The circular validation strategy allows tuning the model parameters. Adapted SVM decision boundaries show optimized results.