In modern industrial processes

timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis (FDD) methods have been proposed and implemented

the performance of which

however

could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder

a deep learning structure

in the phase of incomplete data treatment

and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process

the Tennessee Eastman process

is employed to illustrate the effectiveness and applicability of the proposed method.