Robust System Modeling, Process Monitoring and Fault Diagnosis in the Era of Big Data

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Multivariate statistical methods have been widely studied to extract valuable information from collected data for process monitoring and fault diagnosis, which play an important role to ensure normal operation of industrial processes. Recently, with the explosive acceleration in technologies development, the amount of data collected has grown exponentially, and the traditional statistical methods become less preferable, since they cannot process billions of data samples in real time due to their computational limitations. Moreover, new challenges arise with voluminous data, such as messy datasets (abnormal data with outliers, missing values and large noise), heterogeneous data sources, and complex dynamics and nonlinearity. To ensure energy efficiency, product quality and plant safety in modern large-scale industrial processes, it is important to address these issues and detect process anomalies and equipment malfunctions as early as possible. Therefore, there is a great incentive to develop large-scale robust system modeling, process monitoring and fault diagnosis frameworks in the era of big data. The long-term goal is to design a systematic framework to extract knowledge from big datasets for smart decision making, including research on scalability and robustness enhancement, dynamics and nonlinearity handling, model structure re-design (deep learning and reinforcement learning), and development of large-scale statistical analytics platforms. The proposed program will focus on two shorter-term objectives simultaneously: (O1) design of large-scale data preprocessing techniques and direct robust models, and (O2) design of large-scale robust time series models. Specifically, O1 will focus on developing advanced parallel data cleaning techniques and robust fusion to fuse mixed data sources, while O2 will incorporate temporal information and initiate the design of robust time series clustering for anomaly detection. Together, O1 and O2 will integrate scalability and robustness to handle the aforementioned data issues. The monitoring and diagnosis frameworks will be developed, which will be verified through both simulated and industrial processes. The program will provide a crucial theoretical foundation for research on big data analytics in process systems engineering (PSE) area, while also benefitting other areas such as drug quality detection in the pharmaceutical industry. Early detection and diagnosis of potential hazards will improve productivity and operation safety, and reduce environmental impact, which will bring significant economic benefits to Canada. For instance, furnace explosion can be effectively avoided by monitoring water leakage in electric arc furnaces. The research personnel in the program will obtain strong modeling and analytical skills, which will equip them to pursue academic and industrial positions in PSE and other areas such as computer science, contributing high quality professionals to the Canadian workforce.