Chen Z. Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Analysis and Projection Based Methods

Berlin: Springer, 2017. — 124 p.Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.The Basics of Fault Detection
Evaluation and Comparison of T 2 and Q Statistics for Fault Detection
Canonical Correlation Analysis-based Fault Detection Methods
Improved CCA-based Fault Detection Methods
A Projection-based FD method for Dynamic Processes with deterministic disturbances
Benchmark Studies
Conclusions and Future Work