Predicting Maintenance and Downtime Events
Mining and Resources Operators are able to replace manual checklists, closed-loop technologies and human senses with telemetry in the environment to collect millions of data points about temperature, sounds, humidity, vibrations, pressure, fluid levels, speed, location, weights, air quality, barcodes, weather and more to actively forecast potential downtime events as well as pinpoint optimal service intervals.
Real-time alerts and warnings will advise operators of the upcoming need to replace parts and automated intervention can even be used to prevent expensive breakdowns and/or damage to equipment thus enabling the process of predicting maintenance and downtime.
Data can be processed in real-time on IoT Edge devices in zero-trust or low connectivity environments, with insights sent into the cloud for aggregation and modelling improvement as needed.
Sensors in the Environment
Historian Data, Log Data and External Data
Collect and Centralise the Data
Transform the Data into Useful Insights and Predictors
Predictive, rather than Preventative Maintenance results in replacing parts and performing services less often. Parts are changed once worn or performing poorly rather than arbitrarily replacing them at predetermined intervals. This leads to lower long-term costs and greater production efficiency. Overall Equipment Effectiveness (OEE) key performance indicators can be used to measure productivity and identify potential process losses.
Similarly, predicting downtime leads to increased production, increased revenue and more efficient operations, at a lower cost.
