Our system continuously monitors connected equipment without human intervention and will automatically warn of future failure or underperformance of the items being monitored.
Utilising advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques it is intended to provide sufficient early warning of failure so that repairs can be conducted while on active deployment prior to equipment failure that could impact the equipment’s ability to remain operational.
Our technology uses a sensing system comprising a number of discrete sensing elements distributed along the equipment or platform that is being monitored. The sensors have been designed to be intrinsically safe and are immune from nor generate electromagnetic interference. This allows the sensors to operate in environments in which traditional electrical sensors cannot be used or do not perform well.
The system uses instantaneous statistical monitoring, frequency monitoring, together with a suite of ML classification and forecasting algorithms to identify the current and likely future state of items of plant. The system both identifies immediate performance degradation and predicts future performance degradation of operational concern using AI and ML.
The system offers multiple output parameters: raw multi-axis vibration and temperature data; current and future plant status classifications to assist with maintenance scheduling and procurement activities as well as outputs required for reports, data streams and user interfaces.
Asset Value Drivers
Reduce Maintenance Costs
Increase life of critical Assets
Increase Operational Availability
Maintain Defence Capability Edge
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