Placeholder image for staff profile

Mr Weijun Li

Postgraduate research student


My research project


Research interests


Weijun Li, Sai Gu, Xiangping Zhang, Tao Chen (2020) Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes
Deep learning has shown great promise in process fault diagnosis. However, due to the lack of sufficient labelled fault data, its application has been limited. This limitation may be overcome by using the data generated from computer simulations. In this study, we consider using simulated data to train deep neural network models. As there inevitably is model-process mismatch, we further apply transfer learning approach to reduce the discrepancies between the simulation and physical domains. This approach will allow the diagnostic knowledge contained in the computer simulation being applied to the physical process. To this end, a deep transfer learning network is designed by integrating the convolutional neural network and advanced domain adaptation techniques. Two case studies are used to illustrate the effectiveness of the proposed method for fault diagnosis: a continuously stirred tank reactor and the pulp mill plant benchmark problem.
Weijun Li, Hui Li, Sai Gu, Tao Chen (2020) Process fault diagnosis with model-and knowledge-based approaches: Advances and opportunities
Fault diagnosis plays a vital role in ensuring safe and efficient operation of modern process plants. Despite the encouraging progress in its research, developing a reliable and interpretable diagnostic system remains a challenge. There is a consensus among many researchers that an appropriate modelling, representation and use of fundamental process knowledge might be the key to addressing this problem. Over the past four decades, different techniques have been proposed for this purpose. They use process knowledge from different sources, in different forms and on different details, and are also named model-based methods in some literature. This paper first briefly introduces the problem of fault detection and diagnosis, its research status and challenges. It then gives a review of widely used model- and knowledge-based diagnostic methods, including their general ideas, properties, and important developments. Afterwards, it summarises studies that evaluate their performance in real processes in process industry, including the process types, scales, considered faults, and performance. Finally, perspectives on challenges and potential opportunities are highlighted for future work.
Weijun Li, Sai Gu, Xiangping Zhang, Tao Chen (2020) Pattern Matching and Active Simulation Method for Process Fault Diagnosis
Fault detection and diagnosis is a crucial approach to ensure safe and efficient operation of chemical processes. This paper reports a new fault diagnosis method that exploits dynamic process simulation and pattern matching techniques. The proposed method consists of a simulated fault database which, through pattern matching, helps narrow down the fault candidates in an efficient way. An optimization based fault reconstruction method is then developed to determine the fault pattern from the candidates and the corresponding magnitude and time of occurrence of the fault. A major advantage of this approach is that it is capable of diagnosing both single and multiple faults. We illustrate the effectiveness of the proposed method through case studies of the Tennessee Eastman benchmark process.