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Wind turbines using digital technology

Issues involved in expanding commercialization of wind power

In 2015, the worldwide capacity of renewable energy facilities exceeded that of coal-fired power.*1 With the aim of creating a low-carbon society, in July 2012, Japan put into effect the feed-in tariff scheme for renewable energy, stimulating the construction of solar and wind farms. In 2016, the full liberalization of the electrical retail business resulted in an increasing number of companies planning either to enter the power generation field or to expand their business. These market conditions engendered a need for the development, design, manufacturing, and sales of wind turbines optimized for Japan's environmental conditions. Utilities considering entering the field of wind power also sought assistance in the streamlining of maintenance and other such business operations.

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  • SUPPLIER
  • Hitachi
    Hitachi is a highly diversified company that operates eleven business segments: Information & Telecommunication Systems, Social Infrastructure, High Functional Materials & Components, Financial Services, Power Systems, Electronic Systems & Equipment, Automotive Systems, Railway & Urban Systems, Digital Media & Consumer Products, Construction Machinery and Other Components & Systems.Year founded: 1910Revenue: $94.0 billion (2014)TYO: 6501
  • INDUSTRIES
  • Energy
  • FUNCTIONS
  • Maintenance
  • CUSTOMER
  • Saibu Gas, a Japanese gas company based in Fukuoka, Japan. It supplies gas to the Northern Kyushu region, including in the area of Fukuoka, Saga, Nagasaki, and Kumamoto.

  • CONNECTIVITY PROTOCOLS
  • SOLUTION
  • Lumada IoT platform:

    Using proprietary data mining technology, this predictive diagnostics solution can infer causes of failures by collecting large amounts of operational data from sensors incorporated into the equipment of wind turbines and other power generation facilities, analyzing such with automated diagnostics technology, and detecting signs of failure. It also makes use of accumulated data on historical events. Another of its features is a user-friendly interface that contributes to the standardization of facility maintenance by removing the need to rely on the personal experience and instincts of expert engineers to determine faults.

  • DATA COLLECTED
  • SOLUTION TYPE
  • SOLUTION MATURITY
  • Mature (technology has been on the market for > 5 years)
  • OPERATIONAL IMPACT
  • Impact #1
    [Efficiency Improvement - OEE]

    Causes of failures can be investigated before faults occur.

    Impact #2
    [Cost Reduction - Maintenance]

    Inspections can be carried out at suitable times to reduce maintenance costs and prevent major accidents.

    Impact #3
  • QUANTITATIVE BENEFIT
  • USE CASES
  • Predictive Maintenance
    Predictive maintenance is a technique that uses condition-monitoring sensors and machine learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on predictive maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.
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