The role of novel data in maintenance planning: Breakdown predictions for material handling equipment

We build a predictive maintenance model for material handling equipment that incorporates novel data sources to forecast breakdowns. To this end, we develop a framework to structure and extract relevant predictor variables. Subsequently, we conduct a comprehensive study of statistical learning methods for failure detection. We show that the standard sensors in material handling equipment provide sufficient data to predict the majority of breakdowns (> 85%). The findings are confirmed in two independent datasets and are thus transferable. Further, we provide a cost-based evaluation of those statistical learning methods and find that K-Nearest-Neighbors and Random Forest Classifier are cost-optimal. While most extant literature focuses on either time or condition-based maintenance, we suggest a more robust approach. We demonstrate that both time and condition are almost equally important. As a result, we present a prediction model that incorporates both variable types. From a managerial perspective we provide recommendations on data collection and highlight the importance to understand the cost ratio between breakdowns and preventive maintenance services.

Automated summary

A predictive maintenance model for material handling equipment is built using novel data sources, which can forecast breakdowns. The study explores statistical learning methods for failure detection and shows that the standard sensors in the equipment provide sufficient data for predicting the majority of breakdowns. The research also evaluates the cost-effectiveness of different statistical learning methods and finds that K-Nearest-Neighbors and Random Forest Classifier are the most optimal choices. Additionally, the study emphasizes the importance of considering both time and condition in maintenance and presents a prediction model that incorporates both variable types. Recommendations on data collection and understanding the cost ratio between breakdowns and preventive maintenance services are provided from a managerial perspective.