Deep Learning Application in Mechatronics Systems’ Fault Diagnosis, a Case Study of the Demand-Controlled Ventilation and Heating System

Mechatronics systems include a vast range of interdisciplinary area of electrical and mechanical systems e.g. heating, ventilation, and air-conditioning systems in building automation systems that are responsible to provide occupants a comfortable and productive environment. The demand-controlled ventilation system as an advanced control approach in smart buildings is used for the main goal of energy saving. But, these kinds of systems because of their numerous components such as sensors and actuators are very prone to the faults. Arise of the faults, if they are not detected and diagnosed early, can lead to the system's performance degradation or extra maintenance cost and effort. Nowadays, introducing a suitable generic technique for fault detection and diagnosis is an utmost challenge. The contribution of this paper is to present a novel fault detection and diagnosis framework based on deep learning method for a case study of mechatronics systems, the demand-controlled ventilation and heating system. This paper presents all the steps including data acquisition, data preprocessing, network model design, model optimization, and network model evaluation. Ten types of faults in different classes as well as the healthy data are used to train and evaluate the performance of the designed network model. The results describe a high accuracy (97.4% in confusion matrix) via the designed deep neural network. Also, this study describes the methodology of selecting the optimum parameters of the training process by analyzing the effect of each parameter on the training accuracy.