Sensor Fault Diagnosis, Isolation, and Accommodation for Heating, Ventilating, and Air Conditioning Systems Based on Soft Sensor

Abstract

Heating, Ventilating, and Air Conditioning (HVAC) systems often suffer from unscheduled maintenance or abnormal shutdown due to the fault of their interior sensor system. Traditional fault diagnosis methods for HVAC sensor systems primarily focus on sensor fault diagnosis and isolation, lacking fault accommodation. Therefore, to realize effective sensor fault detection, identification, and accommodation (SFDIA), a method for HVAC SFDIA based on the soft sensor is proposed. First, a diagnosis soft sensor with multi-variable input is constructed to estimate the output of the physical sensor being diagnosed. The residual between the estimated value of the diagnosis soft sensor and the measurement of the physical sensor is used as an indicator of the sensor’s condition. If the residual exceeds the fault threshold, the sensor is diagnosed to be faulty. In order to maintain valid sensor output, an accommodation soft sensor is constructed using the historical normal value. The erroneous output of the faulty sensor is substituted by the estimated value from the accommodation soft sensor, thereby realizing sensor fault tolerance control. Experimental results demonstrate that the average false alarm rate for sensor fault diagnosis is 1.57% and the average fault diagnosis rate is 96.51%. The predictive mean absolute error (MAE) and root-mean-square error (RMSE) of the recovered soft sensors are 0.0525 and 0.0738, respectively. Thus, the soft sensors developed in this paper exhibit satisfying ability in HVAC SFDIA.