(546d) A Hybrid Modeling Approach for Fault Detection and Isolation of HVAC Systems

Hassanpour, H. - Presenter, McMaster University
Mhaskar, P., McMaster University
House, J. M., Johnson Controls Inc
Salsbury, T. I., Johnson Controls Inc
The occurrence of various faults in heating, ventilation and air conditioning (HVAC) systems in buildings may result not only in compromised comfort levels, but also lead to increased energy consumption. This has motivated research on the design of fault detection and isolation (FDI) strategies for HVAC units. One of the approaches for design of FDI strategies utilizes first principles models to generate residuals (in order to capture the difference between observed behavior and expected behavior). In [1], the problem of sensor and actuator fault isolation is addressed by designing a state observer to exploit the analytical redundancy in the system and in [2], this problem is considered for control affine nonlinear uncertain systems. In HVAC applications, first principles models have also been utilized to achieve FDI [3]. Developing and maintaining first principles models in general, and even more so with HVAC systems, remains challenging. This has led to efforts to devise purely data driven fault detection and isolation filters.

The use of data-driven approaches such as artificial neural network, principal component analysis, wavelet analysis, etc., has been increasing significantly in the area of FDI for HVAC systems. In [4], PCA and joint angle analysis (JAA) are utilized to detect and diagnose multiple faults in variable air volume (VAV) systems. In [5], the problem of flow sensor fault detection and validation of VAV systems is considered by employing PCA models. In [6], the PCA and wavelet transform are combined to detect and isolate the faults in HVAC systems. In [7], a causal FDI framework is proposed to diagnose multiple faults in VAV boxes of HVAC systems. Then the results are compared with PCA and joint angle analysis. In [8], The problem of distributed FDI of HVAC system is considered by designing local FDI framework for each unit. In [9], The combination of basic and auxiliary neural networks along with clustering analysis are used for FDI of HVAC systems. The notion of applying hybrid approaches, those that utilize both, a rudimentary first principles model (relatively easy to develop), along with data driven approaches has been gaining significant attention lately [10,11], and could bring potential value to the problem of FDI for HVAC systems.

Motivated by the above, this work addresses the problem of fault detection and isolation by utilizing a hybrid FDI framework. The proposed hybrid FDI framework uses a combination of first principles models along with data driven model to first model the process dynamics. These hybrid models are then used as part of an FDI framework to design residuals dedicated to detecting and isolating faults. The framework is implemented on an HVAC simulation testbed, and results compared with purely data driven approaches as well as first principles model-based approaches.


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