Concurrent Fault Detection and Anomaly Location in Closed-Loop Dynamic Systems With Measured Disturbances

Most data-driven process monitoring approaches consider the fault detection as a binary classification issue: normal or abnormal. All deviations from the nominal operating condition can trigger the same alarms. They fail to distinguish different fluctuation patterns and locate the positions of anomalies, such as the normal deviations in operating conditions, sensors faults, actuator faults, and process faults. A new process monitoring strategy based on orthogonal decomposition (OD) is proposed for the concurrent detection and location of different deviation patterns. OD is performed to discriminate the dynamics of data driven by measured disturbances and unmeasured disturbances in the same control system. This way, the original variable space is decomposed into the deterministic subspace and stochastic subspace. A dynamic principal component analysis-based subspace identification technique is used to construct the monitoring indices in the deterministic and stochastic subspaces, respectively. Two case studies show the validity of the OD-based process monitoring approach. Note to Practitioners—Fault diagnosis based on process data models always focused on analyzing variables’ contributions to the anomaly in the past practice. But it is frequently difficult to decide the root causes just using the variables’ contributions because different faults may induce a similar variation of the same variable. This paper provides a new scheme to locate the faulty components, including the sensor faults, actuator faults, process faults, and disturbance variations. It is more pertinent to learn about the fault locations than variables’ contributions. Moreover, by locating faults first and then figuring out variables’ contributions to a specific location, more detailed and precise diagnosis conclusions can be drawn when being compared with the results of using the variables’ contributions in a global system. This new method is purely data driven and it has no demand for complex process knowledge.