Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach

Implementing advanced big data (BD) analytic is significant for successful incorporation of artificial intelligence in manufacturing. With the widespread deployment of smart sensors and internet of things (IOT) in the job shop, there is an increasing need for handling manufacturing BD for predictive manufacturing. In this study, we conceive the jobs remaining time (JRT) prediction during manufacturing execution based on deep learning (DL) with production BD. We developed a procedure for JRT prediction that includes three parts: raw data collection, candidate dataset design and predictive modelling. First, the historical production data are collected by the widely deployed IOT in the job shop. Then, the candidate dataset is formalised to capture various contributory factors for JRT prediction. Further, a DL model named stacked sparse autoencoder (S-SAE) is constructed to learn representative features from high dimensional manufacturing BD to make robust and accurate JRT prediction. Our work represents the ...