One of the primary functions of an IoT system is to predict failures before they occur and automatically take corrective action so that unplanned downtime and the associated costs are minimized or even eliminated.
Through the monitoring of physical asset data, DataV can reduce costly maintenance deployments by proactively identifying issues that need attention prior to actual failure. By aggregating and analyzing large data sets, operators can identify patterns and anomalies and benchmark those against historical trends to inform smarter decisions that can predict issues before it is too late.
Proactive measures can be initiated from predictive analytic insights to inform preventative maintenance protocols before failing equipment reaches critical mass. Therefore predictive failure capabilities from IoT help extend the lifecycle of equipment, reduce unplanned downtime, inefficient maintenance deployments and ultimately minimize unexpected costs.