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:
Non-AI-enabled solutions are unable to actively learn the normal baseline performance of a machine, thereby restricting their ability to detect performance deviations.
Inability to quickly discern critical data from non-critical data creates inaccurate machine health assessment and mistimed maintenance.
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.
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.
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.
produce recursive analysis, training, and processing that enables a continuous evolution of the AI model that takes place directly at the machine endpoint.
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.
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:
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