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Predictive Maintenance with Analog Sensor AI PowerBrain™
The Analog Sensor AI PowerBrain™ processes mixes of sensor data to learn the behaviour of complex assets, systems and machines under certain cirumstances and states - and detect and predict them.
All industries, use cases, machines and facilities where analog sensor data monitoring and understanding adds value can benefit from this unique and super flexible AI PowerBrain™.
Features
AI Processing of Analog Sensor Data
Various measurements such as temperature, pressure, RPM and speed can easily be processed by our AI module in systems on site at low memory and computing performence requirements.
Mixed Sensor Data Processing
Several sensors data streams can be processed at once allowing deeper insight into the asset condition, its current behaviour and state.
Training and Recognition of States
The users on site or before deployment can define and train certain sensor data stream mixes and conditions as states or indicators of approaching states, for example maintenance need.
Moving Equipment
Across all industries moving equipment such as vehicles and drives can easily be monitored and service need detected as early as technically possible. e.g. immediately after a harvester started driving the AI PowerBrain™ can detect whether the hydraulic pressure is normal for the current speed and temperature, enabling warnings of hydraulic issues before arrival at its far away harvesting destination.
Energy Consuming Assets
Across all industries power consuming equipment such as electric motors and gearboxes can easily be monitored and service need detected. e.g. a changed power consumption at a certain pumping volume may indicate wear and tear of the pump or an issue with the motor/gearbox and the need for maintenance service before serious defects occur.
AI Power Grids
Across the energy industry there is a need for automatic analysis and learning of normal and unusual behaviour and power flows and losses - helping detect need for service and/or re-configuration much earlier. e.g. an AI enabled secondary substation or Ring Main Unit (RMU/Transformer) can learn the usual power consumption at its low voltage feeders and detect additional prosumers as well as consumers, helping the Distribution Network Operator (DNO) balance the power grid and optimize infrastructure capacities.
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Offering Impressions
Whitepapers
Future of AI (pdf)
Today's applications and vision of future developments of AI
AI Predictive Maintenance Business Case (pdf)
Covers value drivers for predictive asset maintenance and provides a guide to help you identify the use case and assess the business case for AI Predictive Maintenance.
Deutsch: SYS-TEC and PowerBrain.shop electronic Edge AI
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