Malfunctions in machines require equipment engineers to conduct fault diagnostic. The fault diagnostics is traditionally reliant on the skills and experiences of the equipment operators and maintenance engineers heavily, which creates an unnecessary technical barrier and results in extra cost in downtime cost and operation overhead. Meanwhile, there have been rich machine logs (sensory readings, performance logs, system logs, context data, process data) of the machine as well as the maintenance data and post-service reports. Such data provide an opportunity of leveraging intelligent data-driven technologies to reduce the maintenance cost through developing automated solutions on machine fault diagnostics and prognostic for critical component failures.
In this paper, a data driven framework is proposed for machine diagnostics and prognostics to relieve the maintenance cost and increase the efficiency. It has been validated with real-world big data from complex vending machines. The proposed framework addresses the data size issue effectively by deriving applicable features and subsequently sustaining the top attributable ones only in the model. An accurate data labeling methodology is developed for supervised learning via comparing the serial number of target components in the adjacent dates. Two predictive models have been developed in this work whereby the first one is in the domain of binary classification for diagnostics, and the second one is a generalized two-stage prognostics model for multi-class classification for preventive maintenance. Cross-validated simulation results have shown that our developed diagnostics model can achieve above 80% accuracy in terms of precision, recall, and F-measure. It has also been shown that the proposed two-stage prognostics framework can outperform the conventional one-stage multiclass prediction models.
The authors would like to acknowledge the tremendous support from our collaborator BP Beverages Ltd, for partially funding the research and providing valuable domain knowl edge. This work is also supported by the A*STAR Industrial Internet of Things Research Program, under the RIE2020 IAF-PP Grant A1788a0023.