Primary crushing is a critical bottleneck in mineral processing operations, where unplanned downtime can directly constrain throughput and disrupt downstream processes. Reactive maintenance, limited early warning of failures and inconsistent visibility into asset health can lead to lost production, higher costs and increased safety exposure.

In this case study, learn how a mining operation deployed Honeywell Asset Performance Management (APM) to shift from reactive to predictive, condition‑based maintenance across its primary crushing assets. By combining logical, rules‑based alerts with advanced multivariate predictive models, the operation gained early warning of developing issues – without adding complexity to operator workflows.

Discover how predictive analytics and early fault detection helped improve reliability, utilization and operational performance in a critical mining operation.