Predictive maintenance combines data about hardware, software, and service components in order to determine the maintenance requirements for mechanical assets. Monitoring emerging failures, predicting capacity overruns, identifying breakdowns, and determining remaining asset life are all aspects of predictive maintenance. AIOps, the use of Artificial Intelligence for IT Operations, is sometimes used for predictive maintenance.
Anticipating and preparing for failure has long been a fact of life for mechanical operations. Until recently, routine replacement of a part after a specified time was the most common form of avoiding a part’s failure in service. This form of scheduled preventive maintenance is helpful. But not all parts fail at the same rate, and premature replacement is waste based on averages and approximation. Moreover, a system that relies on scheduled maintenance alone will not detect actual or imminent failure of a part that’s prematurely defective. Another strategy for reducing downtime was to replace all the parts when one failed, and it was unclear which one, but this strategy has a clear high-cost downside.
What makes predictive maintenance so important?
There are other benefits as well. Depending on the industry, contractual service level agreements (SLAs) may require organizations to maintain delivery of service or materials on a strict 24×7 basis, or face penalties, and even fines. In other cases, equipment failure can cause a loss of revenue because of interruptions to supply chains, loss of inventory, customer churn, and other obvious consequences owing to operational slowdown. Predictive maintenance can help mitigate all of these potential consequences of system downtime.
The use of statistical analysis, sensor monitoring, advanced analytics, and AI to more accurately predict when a failure will occur offers a great improvement. With sensors continually monitoring the health of each part, a monitoring system can alert you in advance of a failure. This is the core benefit of putting a predictive maintenance program in place: you replace only near-defective parts, thus saving labor and the expense of unnecessary parts replacement while maintaining high uptime. Plus, a good predictive maintenance system gives you time to schedule maintenance at the least disruptive time for the business.
