A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems

Abstract

Recent emphasis has been placed on improving the processes in manufacturing by employing early detection or fault prediction within production lines. Whilst companies are increasingly including sensors to record observations and measurements, this brings challenges in interpretation as standard approaches for artificial intelligence (AI) do not highlight the presence of unknown relationships. To address this, we propose a new data analytics framework for predicting faults in a large-scale manufacturing system and validate it using a publicly available Bosch manufacturing dataset with a focus on pre-processing of the data.

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“…For the one-class case, the popular learning models include neural networks and support vector machines (SVMs), whereas for the multi-class case, many established algorithms can be utilized (Sun et al 2011). Boosting is a common method of the algorithm level approach (Wagner et al 2016;Carbery et al 2018) The role of cost-sensitive learning is to minimise a cost function to learn from incorrectly classified data. Such learning approach is able to consolidate the context to compensate for imbalanced data.…”