A big data driven sustainable manufacturing framework for condition-based maintenance prediction

Abstract Smart manufacturing refers to a future-state of manufacturing and it can lead to remarkable changes in all aspects of operations through minimizing energy and material usage while simultaneously maximizing sustainability enabling a futuristic more digitalized scenario of manufacturing. This research develops a big data analytics framework that optimizes the maintenance schedule through condition-based maintenance (CBM) optimization and also improves the prediction accuracy to quantify the remaining life prediction uncertainty. Through effective utilization of condition monitoring and prediction information, CBM would enhance equipment reliability leading to reduction in maintenance cost. The proposed framework uses a CBM optimization method that utilizes a new linguistic interval-valued fuzzy reasoning method for predicting the information. The proposed big data analytics framework in our study for estimating the uncertainty based on backward feature elimination and fuzzy unordered rule induction algorithm prediction errors, is an innovative contribution to the remaining life prediction field. Our paper elaborates on the basic underlying structure of CBM system that is defined by transaction matrix and the threshold value of failure probability. We developed this framework for analysing the CBM policy cost more accurately and to find the probabilistic threshold values of covariate that corresponds to the lowest price of predictive maintenance cost. The experimental results are performed on a big dataset which is generated from a sophisticated simulator of a gas turbine propulsion plant. A comparative analysis confirms that the method used in the proposed framework outpaces the classical methods in terms of classification accuracy and other statistical performance evaluation metrics.