Fault detection and diagnosis in air handling using data-driven methods

Actuator and sensor faults are inevitable in air handling units. It may cause loss of energy and reduction in fresh air quality and may endanger human life in some cases like, e.g., the operation room. This paper presents a fault detection and diagnosis method in air handling units. At first, a model similar to the RP-1312 model was considered for air handling units. Some fault signals were then applied to the system, and finally, data were obtained to be used in fault detection block. Due to the high dimensions and volume of the data, the problem dimensions should be reduced while maintaining the quality of detection. One of the contributions of this paper is to examine the effect of various variables on fault diagnosis performance and, while retaining excellent variables, eliminates inappropriate and recessive ones. An optimal selection of the proper variables for fault detection makes the computational time of the algorithm significantly reduced. Considering abundant data and their nonlinear nature, support vector machines technique and radial basis function neural network methods were used for fault detection and diagnosis, respectively. In addition to using the radial basis function neural network method, the principal component analysis technique and kernel principal components analysis, which is the nonlinear generalization of the principal components, were used for fault diagnosis. The other contribution of this paper is to detect the sensor and actuator faults simultaneously. The simulation results show that the proposed method accurately detects and diagnoses faults and has better results than previous works.