Moving Towards Intelligent Prevention
M2M uses sensors attached to assets to take preventive maintenance a step further by making it predictive. Sensors continuously generate data about those assets and transmit it to a control center, where it is converted from data to meaningful information in the form of a dashboard or notifications, where a dispatcher monitors and responds to the activity.
For example, a pump component might start vibrating more than normal or the sound of a machine changes in pitch, indicating something unusual is occurring. The sensor picks up on that change and emits an alert to the dashboard - or to a smartphone or other device - warning that the asset is projected to fail in the next 36 hours. This gives the dispatcher or operator a chance to schedule a repair to prevent costly downtime of the equipment. By employing this predictive approach, utilities can maximize their equipment uptime.
With today's data collection capabilities, we can compile statistics on age, failure rates, hours of use, environmental conditions and inspections for each asset, and even compare them against a whole fleet of like assets. Patterns and trends will emerge that can guide the organization in developing an effective prescriptive maintenance plan. As a result, maintenance is performed according to what the machines tell you, not the calendar or a predetermined metric. We can now repair or replace only the components that need it, when they need it.
Analytics tools help extract insights from the data to predict when equipment needs maintenance, repair or replacement before an actual outage occurs.