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Predictive Maintenance: Sniffing Out Problems Before They Become Serious
Wait until something breaks and then call someone to repair it. That’s a reactive approach. It’s prescriptive maintenance management. But waiting until something breaks down before attempting to fix it can be expensive. Predictive maintenance is the alternative and tries to identify problems before they happen in order to avoid costly downtimes.
IoT devices and sensors have evolved significantly and now are able to continuously monitor equipment so as to provide an always up-to-date report on the health of equipment currently in service. Predictive maintenance often can provide dramatic savings by catching problems early on and by then limiting the need for actual in-person testing and observations.
Joe Devanesan, author at TechHQ, said that “reactive maintenance is problematic because it is linked with equipment breakdowns that abruptly halts activities and have the potential to lead to massive losses for the enterprise. Increased maintenance costs, shorter asset lifespans, lower safety, and inefficient maintenance labor practices are all a result of reacting to the breakdown, only when it happens.”
Martin McConnell, senior VP at Willis Towers Watson, said that “predictive tools, by not only predicting that a part may fail, but by providing various solutions for that pending failure, allow no time to be wasted in deciding which solution is best.”
IoT devices are helping Predictive Maintenance to be realized. The following are five capabilities that IoT devices have which allow their use to help realize the goal of predictive maintenance:
- Sensors – Sensors on IoT devices can collect data by being place on or near the machine being monitored
- Communication – IoT devices can communicate the data they collect back to the cloud or central edge computer
- Centralized Collection – Ultimately large amounts of information from all in-service devices can be collected, centralized, and analyzed.
- Reporting – Data can be analyzed and patterns across all machines in service can be studied. Reports can be generated when some machines aren’t operating as expected.
- Prediction – Analytics and AI can be used to predict possible future problems and recommend remedial actions which should be taken.
