Incipient Fault Detection for Complex Industrial Processes with Stationary and Nonstationary Hybrid Characteristics

Abstract

For a nonstationary process which has a time-varying mean, a time-varying variance, or both, it can be difficult to detect incipient disturbances which may be hidden by the time-varying process variations. Besides, stationary and nonstationary characteristics may coexist in complex industrial processes which, however, have not been studied for process monitoring. In the present work, a triple subspace decomposition based dissimilarity analysis algorithm is developed to detect incipient abnormal behaviors in complex industrial processes with both stationary and nonstationary hybrid characteristics. The novelty is how to comprehensively separate the stationary and nonstationary process characteristics and describe them, respectively. First, a stationarity evaluation and separation strategy is proposed to decompose the data space into three subspaces, revealing the linear stationary process characteristics, the nonlinear stationary process characteristics, and the final nonstationary process characteristics....