Machinery health prognostic with uncertainty for mineral processing using TSC-TimeGAN

Abstract

Deploying health state (HS) assessment and remaining useful life (RUL) prognostic on the industrial equipment can effectively avoid shutdown and reduce productivity losses. This paper proposes a temporal self-clustering (TSC) and time-series generative adversarial network (TimeGAN) based uncertainty-aware health prognostic algorithm for the hydrocyclone, a critical machine in the mineral processing. To label the HS of degradation data, an autoencoder based TSC algorithm is proposed. It integrates the extraction and clustering of health indicators into a unified framework, so that the correlation information between feature extraction and clustering can be captured for cluster analysis. A Benders decomposition based optimization approach is designed to synthetically optimize network weights and clustering centers, thereby extracting and labeling data features more reasonably. For quantifying prediction uncertainty, an uncertainty-aware prognostic algorithm is proposed to predict the RUL distribution, and the uncertainty can be quantified as the standard deviation (Std) of the distribution. Furthermore, a mechanism and TimeGAN fusion temporal sequence generation algorithm is proposed to augment the degradation process sequences of the equipment. Verification experiments using the actual data show that the proposed algorithm can effectively evaluate the HS of the equipment and provide a sensible and convincing RUL prediction.