Online Sequential Sparse Robust Neural Networks With Random Weights for Imperfect Industrial Streaming Data Modeling

Industrial streaming data exhibits the concept drift characteristic due to the time-variant operating conditions, which degrades the performance of models established by traditional offline batch learning. Moreover, the widespread outliers and the correlations between data variables in industrial data streams can have a devastating impact on modeling. Therefore, this paper presents a novel online sequential sparse robust neural networks with random weights (OSSR-NNRW) for imperfect industrial streaming data to achieve highly reliable online modeling of time-variant dynamic systems. First, sparse partial least squares regression is used to replace least squares estimation for network output weights calculation, which not only can effectively solve the multicollinearity problem caused by correlations, but also enable variable selection to improve the performance and interpretability. Second, we introduce the online sequential learning strategy with forgetting factor to realize adaptive updating of model parameters, thus enhancing the online learning ability and overcoming the time-variant dynamics of industrial systems. More importantly, in order to strengthen the robustness of the model, Schweppe generalized M-estimation is adopted to determine the modeling weights by the model residual size and the distance information of input vectors in the high-dimensional space to resolve the prevalent existence of outliers in the input and output samples. Finally, data experiments on two industrial systems have validated the effectiveness, advancement, and practicality of the proposed method. Note to Practitioners—In the process industry, the product quality relies on the timely and accurate measurement of key production indicators. However, owing to the time-varying characteristics of industrial processes and the limitations of measurement devices, conventional batch learning-based data-driven models are difficult to apply to imperfect industrial data stream scenarios. To this end, the OSSR-NNRW is proposed for online robust modeling of complex time-variant dynamic systems by combining sparse robust modeling and online learning strategy in a unified framework of neural networks with random weights. The OSSR-NNRW enables online learning based on industrial data streams while resolving correlations between data variables and mitigating the negative effects of outliers from both input and output samples on the modeling process. Experimental results using two typical process industry datasets show that the proposed OSSR-NNRW has high estimation accuracy and can be easy to implement in industrial processes.