No training is required due to its highly interactive graphical user interface.
No delays for machinery condition diagnosis. An embedded powerful diagnostics and troubleshooting algorithm is running in the background of HAT, providing in just a few seconds meaningful condition reports.
Extremely Low bandwidth requirements:
Limited bandwidth requirements. HAT, when connected to the ship’s internet, transmits only an encoded data file of diagnostics results and machinery fault indicators for sharing the information with superintendents and fleet managers and not entire vibration data files for post processing by onshore engineers.
Business Analytics and fleet KPIs
Instant sharing of information. Machinery report is uploaded to an On-Line Condition Monitoring and Business Intelligence platform. From there the information is securely accessible to the ship’s superintendents and fleet managers. This feature gives to fleet managers the unique advantage of instant information, without delays for analysis by onshore engineers or due to ship network congestion.
Machine Learning Algorithm
Improvement through lifetime. Machinery fault indicators, are transmitted through ship’s network and populates a data lake of vibration levels and fault indicators of similar machinery types. A Supervised Machine Learning algorithm is continuously processing the data and updates HAT diagnostics algorithm, minimizing by that the probability of false positive or false negative events.
HAT algorithm is automatically updated when connected to the ship’s internet. Updates are automatically installed without the user intervention.
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