What is it?
Predictive maintenance is the use of data analysis tools and techniques to track the state of equipment in real-time. The goal is to optimize the lifespan of machinery, foresee its future condition, avoid the risk of an unexpected breakdown, and reduce planned downtime, i.e., the amount of time when equipment is not working. This may result in decreased expenses and a rise in productive efficiency (the level at which one good can no longer be manufactured without interfering with the production of another).
How does it work?
Technologies employed for predictive maintenance include the Internet of Things, artificial intelligence, and predictive analytics. Connected sensors assemble data from equipment. Then this data is collected and analyzed by a specialized AI-powered tool like an enterprise asset management (EAM) or computerized maintenance management system (CMMS).
Artificial intelligence and machine learning determine the condition of machinery and detect potential faults. Next, a work order is appointed to the maintenance team, so technicians can proactively respond to the existing issues. This way, predictive maintenance has a positive impact on workflows, the scheduling process, and supply chains.