LAUSR

Abstract Bias of data location and increase of data variations are two typical disturbances, which in general simultaneously exist in the fault process. Targeting their different characteristics, a nested-loop fisher discriminant analysis (NeLFDA) algorithm and relative changes (RC) algorithm are effectively combined for analyzing the fault characteristics. For the fault data containing those two faults simultaneously, a combined NeLFDA-RC algorithm is proposed for fault deviations modeling, which is termed as CNR-FD. Fault directions concerning bias of data location are extracted by NeLFDA algorithm and then the fault deviations associated with these directions are removed from the fault data. Then directions concerning increase of data variations are extracted by RC algorithm. These fault directions are used as reconstruction models to characterize each fault class. Online fault diagnosis is then performed by finding the specific reconstruction models that can well remove alarm signals for current sample. Its performance is illustrated using a numerical simulation example and pre-programmed faults from Tennessee Eastman (TE) benchmark process.