Machine Health Monitoring -
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Machine Health Monitoring

Transforming mission-critical machines from opaque entities into assets that are fully transparent and that deliver deeper insights for active fine-tuning of performance.

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Why It’s Important

Despite continued evolution of Industry 4.0 initiatives, many operations still lack the AI-enabled observability required to produce real-time, forward looking, insights into the performance and health of critical IT and OT machines. This lack of endpoint intelligence severely limits an operation’s ability to maximize machine health while also optimizing machine output. Problem areas include:

  • Ecosystem observability

    Inability to learn

    Non-AI-enabled solutions are unable to actively learn the normal baseline performance of a machine, thereby restricting their ability to detect performance deviations.

  • Lack of discernibility

    Inability to quickly discern critical data from non-critical data creates inaccurate machine health assessment and mistimed maintenance.

  • Non-customized control

    Legacy asset management solutions do not adequately address the diversity and complexity of today’s machine ecosystems and fail to provide the asset-centricity required to deliver customized observability and control.

  • Inability to predict

    The inability to generate timely, accurate, actionable, predictive analytics is a prohibitive limitation in fully optimizing the performance and security of OT and IT machines and devices.

How We Do It

Utilizing next-generation, lightweight, embedded and edge AI capabilities, MicroAI brings real-time health monitoring to any type of IT or OT machine, providing the deep insights needed to fully optimize machine performance and health. A new observability paradigm that includes:
  • Endpoint intelligence

    Embedded AI

    that learns the normal operating behavior of an individual machine or a group of machines and that provide real-time insights into performance against those learned baselines.

  • 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.

  • Insight mining and distribution

    via a centralized visualization engine that provides continuous observability into the status of connected machines and devices, supported by intelligent workflows that automate the process of performance alert notifications and implementation of corrective actions.

What It Delivers

MicroAI’s machine health monitoring methodologies are providing operations in the manufacturing, telecom, and industrial sectors with unparalleled monitoring capabilities for their mission-critical machine assets. Next-generation advantages that include:

  • At-a-glance visualization of real-time performance and events across an entire ecosystem of machines.
  • Fast-track issue identification and corrective action coupled with the ability to identify recurring problems based on historical analytics.
  • Elimination of downtimes due to unnecessary maintenance activities and/or unforeseen malfunctions.
  • Machine lifespan extension via real-time health monitoring, process-driven mitigation actions, and predictive analytics.
  • Improved OEE scores as a result of all the above.