FORESIGHT MARITIME

OUR TECHNOLOGY

6

Autonomy & Realibility

Maintenance alarms are automatically set by the system which has statistical probability of false alarms in the domain of 1 in a million.

4

Sensor fusion & multisensorial approach

Embedded analytics can make use of sensor fusion and interpret, e.g. acoustic, vibration, temperature, oil, etc. signals. Advanced mathematical techniques are used to automatically fuse information from Condition Indicator (CI) algorithms into Health Indexes (HI).

3

Embedded analytics & data compression 

Component diagnostics is performed at the sensor level using Condition Indicator (C.I.) algorithms.
Data sampled at 100 000 samples per second are compressed to few Kilobytes directly at the sensor level.

5

Simplicity

Machinery health index (HI) allows real time comparison of components health status. The end user does not need to be a condition monitoring expert to understand and act on the data.

7

Actionalbility

Our Remaining Useful Life (RUL) prognostics models target warnings at -250 hours lifetime and have confidence intervals associated with them. A text message or an email is automatically sent to the operator when a machinery component is out of tolerances.

8

Integrability

The team behind the technology has developed the hardware and software. We have the complete knowledge and can integrate it on various IT systems. 

Foresight Maritime addresses 8 technical advances tackling the machine monitoring business case:
 

1

Practicality 

Our sensors (e.g. vibration, acoustics, temperature, etc.) are bolted or glued on the asset shell. One single cable powers and transmit data for up to 100 sensors.

2

Data quality

Signal conditioning and data conversion occurs within the sensor package which is a Faraday cage.

USER EXPERIENCE
 
LINKS
ABOUT

tjjm@machineprognostics.no

Tel: 0047 46442695

Machine Prognostic

c/o MIL labs

H building, office H3 047

Jon Lilletuns vei 9,

4879 Grimstad, Norway

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