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Shipboard Machine Monitoring for Predictive Maintenance

Shipboard Machine Monitoring for Predictive Maintenance. Presented by Jing Li http://www.sensorsmag.com/sensors/article/articleDetail.jsp?id=314716&pageID=1. The Loch Rannoch Project. Background

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Shipboard Machine Monitoring for Predictive Maintenance

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  1. Shipboard Machine Monitoring for Predictive Maintenance Presented by Jing Li http://www.sensorsmag.com/sensors/article/articleDetail.jsp?id=314716&pageID=1

  2. The Loch Rannoch Project • Background BP’s Loch Rannoch is a 1000 ft., 132,000 ton oil tanker that shuttles oil from a storage vessel to an oil-processing terminal.

  3. The Loch Rannoch Project • Environment • the ship had compartments that could be shut off by watertight doors • significant vibration from the main engines, generators, and thrusters • the temperature inside the engine room was between 80oF and 100oF

  4. The Loch Rannoch Project • Goal Make use of wireless sensor networking technology to monitor critical rotating machinery, such as the pumps and motors in the starboard engine room. Vibration data can help people evaluate how a machine is wearing and when should do maintenance.

  5. System Design • Sensors 150 Rockwell Automation accelerometers mounted on the machinery, hard-wired to Intel motes, which were mounted in a metal enclosure roughly 2 ft. from the machine. • Sensing data vibration data

  6. System Design The gateways collected the mote clusters’ data and communicated with other gateways via IEEE 802.11 radios using a mesh network architecture, ultimately passing the data to a controller gateway. • Network Architecture The motes transmitted the newly converted digital data to an Intel gateway.

  7. Challenges • The Multipath Effect Problem: The metal structure of the ship and the closely situated machinery made the engine room one of the worst possible environments for RF communications. Solution: mesh networking architecture

  8. Challenges • What To Do with All Those Data Problem: the high volume of data generated by the sensors monitoring the machines with the low data rate protocol Solution: • 32-bit microprocessor • do a lot of the processing at the edge of the network, instead of in back-end servers • XML-based system that allows data abstraction and querying of the data

  9. Challenges • More Power Problem: how to get power, how to use it and how to ensure there would be enough Solution: • use low-power radios built to the 802.15.4 standard • implement power-management circuitry on the motes • reduce the volume of data transmitted • power harvesting

  10. Results • Before • operators used handheld devices to check one accelerometer at a time every six to eight weeks • quite small amount of data collected • Now sample automatically every 18 hours

  11. Lessons learned in the project • Sensor networks work in hostile environment • The choices of radio and network architecture are important • Advanced platforms are a good match for this type of application

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