Preventive equipment maintenance relies on the monitoring of the current equipment condition and performance level under normal operating conditions. This monitoring is called upon to prevent equipment failure by predicting possible failure occurrence on the basis of specific metrics.
Smart data solutions, sensors, and trackers are used to collect the defined metrics, process and analyze the data. On the basis of the output, the smart systems alert the energy outage, the poor functioning of the mechanisms and urge people to take right and immediate decisions.
Predictive maintenance often allows the detection of impending failures that could never be detected by human eyes. With predictive maintenance, downtime and repairs are directly tied to likely failure, minimizing cost (e.g. less labor time, less chance of unexpected failure) and maximizing asset life.
High
Regression analysis, Ranked scoring, real-time analysis and prediction
Real-time sensor data
Historical data
Operating history
Maintenance reports
Technician notes
Flyover data from drones
Predictive models (e.g., expected earthquake effects)
Public datasets (e.g., weather reports)
And more
Geo-Spatial Data, Census, Weather Reports, Demographical Data, OpenStreetMap, the Internet (e.g., twitter feeds)
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