Predictive Maintenance -
page-template,page-template-full_width,page-template-full_width-php,page,page-id-32992,page-child,parent-pageid-32954,bridge-core-1.0.4,mega-menu-top-navigation,ajax_fade,page_not_loaded,,qode-title-hidden,qode_grid_1400,qode-content-sidebar-responsive,wpb-js-composer js-comp-ver-5.7,vc_responsive

Predictive Maintenance

Endpoint intelligence that powers a transition from static preventive routines to predictive capabilities that transform asset maintenance.

Schedule a Demo

Why It’s Important

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:

  • Time vs intelligence based

    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.

  • Unnecessary downtime

    Rigid time-based maintenance intervals often result in maintenance being performed before it is needed, resulting in loss of productivity and high maintenance costs.

  • machine-lack

    Opaque machine observability

    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.

How We Do It

MicroAI utilizes Endpoint AI technologies that enable equipment operators and stakeholders to gain deeper insights into the real-time status and heath of their machine assets. Asset-specific data is analyzed to predict when that asset will require maintenance. In this method, maintenance is performed based on actual machine-generated information as opposed to the legacy time-based approach. A methodology that includes:
  • Endpoint intelligence

    Customizable algorithms

    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.

  • observability

    Multidimensional behavioral algorithms

    produce recursive analysis, training, and processing that enables a continuous evolution of the AI model that takes place directly at the machine endpoint.

  • Data aggregation and visualization

    provides automatic aggregation, analysis, and presentation of machine data. Asset owners can quickly customize asset analytics to meet their operational needs.

  • Embedded workflows

    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.

What It Delivers

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.

  • Up to 50% reduction in machine downtime
  • Double-digit increases in machine productivity
  • Significant reductions in maintenance labor costs
  • Up to 15% improvement in OEE scores
  • Lower CoGS and improved competitive position
  • Low implementation cost and reduced management burden