Cost-Sensitive Learning for Predictive Maintenance
This addresses the issue of minimizing maintenance costs for industries, but it is incremental as it adapts existing cost-sensitive methods to a specific domain.
The paper tackles the problem of model selection in predictive maintenance by proposing cost-sensitive learning based on economic costs, showing it can lead to significant cost reduction and fault-tolerant policies.
In predictive maintenance, model performance is usually assessed by means of precision, recall, and F1-score. However, employing the model with best performance, e.g. highest F1-score, does not necessarily result in minimum maintenance cost, but can instead lead to additional expenses. Thus, we propose to perform model selection based on the economic costs associated with the particular maintenance application. We show that cost-sensitive learning for predictive maintenance can result in significant cost reduction and fault tolerant policies, since it allows to incorporate various business constraints and requirements.