Fault diagnosis based operation risk evaluation for air conditioning systems in data centers

The air conditioning systems in data centers are essential, energy-hungry infrastructures that can keep the rack of servers below the critical temperature limits. Faulty operation may result in increased energy usage, reduced thermal comfort, and increased maintenance costs. This paper presents a novel hybrid model based fault diagnosis and estimation approach to manage the operation risk of data center air conditioning systems. Four decoupling features are firstly developed for the typical faults of the system investigated. They are uniquely affected by individual faults and deviate from their fault-free values when their corresponding faults occur. Then the fault diagnosis model integrating the decoupling features and random forest classifier is used to identify the fault type. Subsequently, the fault intensity estimator established through the random forest regression method is used to estimate the fault severity that can represent the magnitude of operation risk. The performance of the presented models are validated by the experimental data. The results illustrate that the proposed fault diagnosis model has significant performance advantage over two pure data-driven models. It is more robust to various operation conditions that may occur in practical applications. For new operation conditions, the overall correct diagnosis rate is still 94.17%, and the false alarm rates are within 5%. In addition, the mean absolute errors of the four fault intensity estimators are less than 4%.