A new usage of reconstruction based contributions for quality-related fault diagnosis

Quality-related fault detection attracted more and more attention in quality control and process monitoring. In recent literature, reconstruction based contributions (RBC) are used for isolating faulty variables which affect product quality. If datasets of known faults are available, fault-specific RBCs are used to identify fault types. Otherwise, variable RBCs are used to isolate faulty variables. However, the existing generalized RBC are quite improvable. On one side, fault-specific RBC can not tell faulty variables, which makes it hard to locate fault root. On the other side, it is well known that the variable RBC suffers a lot from the smearing effect, which provides too many candidates of faulty variables. In the present work, a new usage of RBC is derived to select faulty variables without smearing effect on nonfaulty variables. The benchmark examples of Tennessee Eastman (TE) process is used to demonstrate the efficiency of the proposed approach for quality-related fault diagnosis.