Abstract
The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in the reasoning of uncertainties, diagnostics of different fault severities, and understandability. However, these methods involve higher and more time-consuming effort; they require a deep understanding of the causal relationships between faults and symptoms; and there is still a lack of automatic approaches to improving the efficiency. The data-driven methods rely on similarities and patterns, and they are very sensitive to changes of patterns and have more accuracy than the knowledge-driven methods, but they require massive data for training, cannot inform about the reason behind the result, and represent black boxes with low understandability. The research problem is thus the combination of knowledge-driven and data-driven diagnosis in DCV and heating systems, to benefit from both categories. The diagnostic method presented in this paper involves less effort for experts without requiring deep understanding of the causal relationships between faults and symptoms compared to existing knowledge-driven methods, while offering high understandability and high accuracy. The fault diagnosis uses a data-driven classifier in combination with knowledge-driven inference with both fuzzy logic and a Bayesian Belief Network (BBN). In offline mode, for each fault class, a Relation-Direction Probability (RDP) table is computed and stored in a fault library. In online mode, we determine the similarities between the actual RDP and the offline precomputed RDPs. The combination of BBN and fuzzy logic in our introduced method analyzes the dependencies of the signals using Mutual Information (MI) theory. The results show the performance of the combined classifier is comparable to the data-driven method while maintaining the strengths of the knowledge-driven methods.
Highlights
Recent advances in Information and Communications Technology (ICT), especially in embedded systems, enable the development of embedded control systems that profoundly couple our physical world to the computation world
The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in reasoning of uncertainties, they diagnose different fault severities, and are more understandable. These methods involve higher and more time-consuming effort, they require a deep understanding of the causal relationships between faults and symptoms, and there is still a lack of automatic approaches to improve the efficiency
The data-driven methods rely on similarities and patterns and they are very sensitive to changes of patterns and have more accuracy than the other knowledge-driven based methods, but they require massive data for training, cannot inform about the reason behind the result, and they represent black boxes with low understandability
Summary
Recent advances in Information and Communications Technology (ICT), especially in embedded systems, enable the development of embedded control systems that profoundly couple our physical world to the computation world. The complexity of HVAC systems makes them error-prone and susceptible to faults that may lead to a waste of energy, for example, continuous heating in the case of a stuck-at damper, poor thermal comfort, and unacceptable indoor air quality. Despite the inherent complexity of DCV and heating systems, their applications require them to be fault and failure-tolerant. A fault- and failure-tolerant design of DCV and heating systems requires developments in failure detection and fault diagnosis techniques, which is a challenge. ASHRAE Project 1043-RP shows that a typical water-cooled centrifugal chiller can face more than twenty types of common faults [5]. ASHRAE Project 1312-RP indicated 68 types of common faults for a typical air handling unit [6]. It is costly to capture sufficient training data for every fault, and most of the research projects consider only a part of these faults in most data-drivenbased chiller FDD methods
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
