Machine output and uptime are critical KPIs for every machine-intensive operation. Lack of real-time insights, unscheduled downtimes, and static maintenance routines all combine to create a machine ecosystem that performs well below its optimum capability. Primary limitations of preventive maintenance include:
Maintenance intervals are fixed, based on historical experience of machine failure. This legacy approach does not provide the performance observability or data granularity necessary to predict a malfunction.
Rigid time-based maintenance intervals often result in maintenance being performed before it is needed, resulting in loss of productivity and high maintenance costs.
Preventive maintenance solutions lack the AI-enabled observability and processing power required to produce real-time, forward looking, insights into the performance and health of critical IT and OT machines.
to accommodate specific operational or environmental conditions for the device or machine. Predictive maintenance routines are based on real-time analytics that provide insights into current and historical performance trends.
produce recursive analysis, training, and processing that enables a continuous evolution of the AI model that takes place directly at the machine endpoint.
provides automatic aggregation, analysis, and presentation of machine data. Asset owners can quickly customize asset analytics to meet their operational needs.
that learn, train, and evolve. Predictive maintenance is supported by workflows that are automated and intelligent, eliminating the need for human intervention in the maintenance scheduling process.
By bringing advanced endpoint AI capabilities to machine maintenance, MicroAI is helping expedite a transition from preventive to predictive maintenance. Machine-intensive companies in the manufacturing, telecom, industrial, and energy sectors are realizing a wide range of operational benefits.
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