Abstract
BackgroundFault detection and diagnosis technology is of great significance for practical industrial processes. Industrial process characteristics change with time due to various reasons such as changing working conditions. This will cause false alarm or missing alarm of process monitoring. MethodsIn this paper, an adaptive slow feature analysis (SFA) - sparse autoencoder (SAE) algorithm is proposed to establish an adaptive model for time-varying process monitoring. Model update index is built based on time-varying characteristics extracted using SFA model. Process monitoring index is built based on sparse characteristics extracted using SAE model. Through online adaptive update strategy, updated monitoring model is realized to adapt to the time-varying characteristics of the process. Significant findingsThe proposed algorithm has good performance on penicillin fermentation process data set and can realize the task of adaptive process monitoring.
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