Fault detection, diagnosis, and data-driven modeling in HVAC chillers

Abstract

Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and fault diagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and most of the schemes are systemspecific. A generic real-time FDD scheme, applicable to all possible operating conditions, can significantly reduce HVAC equipment downtime, thus improving the efficiency of building energy management systems. This paper presents a FDD methodology for faults in centrifugal chillers. The FDD scheme compares the diagnostic performance of three data-driven techniques, namely support vector machines (SVM), principal component analysis (PCA), and partial least squares (PLS). In addition, a nominal model of a chiller that can predict system response under new operating conditions is developed using PLS. We used the benchmark data on a 90-ton real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables under nominal and eight fault conditions of different severities at twenty seven operating modes.