Digital twin model for chiller fault diagnosis based on SSAE and transfer learning

Abstract

The equipment of chiller systems is characterized by a complex mechanical structure and operating environments that vary widely, resulting in high failure rates, energy waste, and costly maintenance. Traditional fault diagnosis methods suffer from low levels of digitalization and intelligence. Therefore, timely and accurate detection and diagnosis of the operating status of chiller systems are critical. To address the above issues, this study proposes a digital twin (DT) model for chiller system fault diagnosis based on stacked sparse auto-encoder (SSAE) and transfer learning (TL). First, a chiller system digital twin mapping model is constructed utilizing digital twin technology. Then, the SSAE model is constructed to provide real-time defect diagnosis and fault result validation. Finally, to address the limited chiller system data in practical applications, the knowledge of fault diagnosis acquired in the source domain is transferred to the target domain via TL. Experimental results demonstrate that the SSAE model outperforms back propagation (BP), recurrent neural network (RNN) and long short-term memory (LSTM) model in term of accuracy. Furthermore, using TL achieves a diagnostic accuracy of over 90% for different degrees of fault severity, with a significant decrease in cross-entropy loss compared to no TL, confirming the effectiveness of the TL method.