The jointly developed system, which will make use of condition monitoring sensor technology and data analysis systems, will be offered by ClassNK as a cloud-based service to shipowners, managers and operators from June 2013.
From April to July 2012, ClassNK, IHIMU and IBM Japan carried out a joint research project to investigate methods for the early detection of machinery abnormalities.
Making use of ship machinery performance data provided by the IHIMU Group, ClassNK and its partners analysed how machinery performance changed in the situations where malfunctions occurred.
This process was made possible by new data analysis technology developed by IBM Research in Tokyo which can automatically identify hidden dependencies between operational parameters and identify sensor anomalies, allowing noise and false positives to be automatically removed from the sensor data.
When research confirmed that the new technology can effectively analyse the data from sensors connected to onboard machinery, ClassNK began working to adapt the system for use in the maritime industry.
ClassNK’s new ship maintenance management system will build on the technology used in IHIMU’s ADMAX shipboard management software, which is already in use on more than 700 vessels, and IBM’s Maximo asset management software system.
The IBM Maximo Enterprise Asset Management (EAM) system is already used in power generation, manufacturing, real estate and other industries to manage maintenance and reduce the lifecycle costs of machinery and other capital intensive assets.
The system itself will make use of IBM’s cloud service to ensure the availability of maintenance information anywhere in the world.
In order to efficiently record maintenance data onboard ships, even when internet access is not available, IBM will also jointly develop a mobile Enterprise Asset Management application for the new management software, using its Worklight mobile application platform.
This mobile architecture aims to allow maintenance data to be recorded onboard and accessed by managers or owners from anywhere in the world via mobile devices.
In order to ensure the effectiveness of the sensor data analysis technology the new system will also be verified on existing bulk carriers, oil tankers, and container carriers equipped with DU’s Lifecycle Administrator (LC-A) system, a sensor based system for condition based and preventive maintenance which makes use of sensor data to determine the condition of diesel engines and other engine room machinery.
In addition to assessing the effectiveness of the new analysis technology, the tests will also confirm the effect of real ocean conditions and differences between individual ships on the sensor data.
While LC-A requires a specialist to develop an analysis model for each vessel on an individual basis, with IBM’s new technology and extensive testing on actual vessels, ClassNK says that its new maintenance system should minimise the need for a custom built analysis model, increasing the scope of system application and allowing it to be used immediately on almost all vessels.
The goal of the new service is to help owners and managers detect machinery abnormalities at the earliest possible moment and predict where malfunctions are likely to occur, thus allowing them to prevent machinery malfunction and lengthen machinery lifespan, while also reducing lifecycle costs.
This research project is one of more than 100 R&D projects currently being carried out as part of ClassNK’s ‘Practical R&D for Industry’ programme, which unites partners from both inside and outside the maritime community to develop new solutions to the challenges faced by the shipping and shipbuilding industries.
IHIMU, which will soon merge with Universal Shipbuilding under the name Japan Marine United to become Japan’s largest shipbuilder, will use the data and expertise developed as part of this project to improve the ship support service of its lifecycle business, which will be one of the company’s key market segments following the merger.