To leverage the available system data, factories across the globe are embracing Predictive Maintenance in the era of Industry 4.0. This has put much emphasis on the development of data-driven algorithms to enable company-wide deployment of predictive maintenance. In the current study, the objective is to predict failures by mining asset maintenance log. Towards this end, first, a text mining-based approach has been suggested to identify different failures, followed by a survival analysis based approach to predict the failure sequence and patterns. The developed algorithm has been tested on maintenance logs of centrifugal casting machines.
Combing Text Mining and Survival Analysis for Asset Health Management Using Maintenance Log
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