The power industry often struggles to keep its equipment, machinery, and other assets working efficiently, while trying to reduce the cost of maintenance and time-sensitive repairs. As a result, predictive maintenance solutions are gaining importance as industry players become aware of the growing maintenance costs and downtime caused by unexpected machinery failures.

The adoption of predictive maintenance technology will deliver failure prediction, fault diagnosis, failure-type classification, and the recommendation of relevant maintenance actions. Predictive maintenance also employs non-intrusive testing techniques to evaluate asset performance trends and includes other methods such as thermodynamics, acoustics, vibration analysis, and infrared analysis.

However, not all companies are equal when it comes to their capabilities and investments in the key themes that matter most to their industry. Understanding how companies are positioned and ranked in the most important themes can be a key leading indicator of their future earnings potential and relative competitive position.

According to GlobalData’s thematic research report, Predictive Maintenance in Power, leading adopters include: Orsted, Enel, EDF, E.ON, Iberdrola and Duke Energy.

Insights from top ranked companies

Orsted

Orsted is a global leader in offshore wind power and supplies large-scale and cost-competitive offshore wind energy, onshore wind energy, and solar energy solutions. It has implemented a digital strategy to enhance productivity and reduce costs. The company has utilized Microsoft’s advanced analytics, artificial intelligence, and cloud to leverage data from its offshore wind turbines which has enabled Orsted to save both time and resources. The technology has helped Orsted to analyze and optimize vast quantities of data captured from sensors on wind turbines. The company is also using cloud-based tools in its new wind projects through which has reduced the time for computation of foundations from a few weeks to 4-8 hours. Orsted has also conducted automated robotic inspection on offshore wind turbine blades at the Burbo Bank Extension offshore wind farm, located in the Irish Sea, as part of its predictive maintenance strategy.

Enel

Enel SpA is an Italian multinational manufacturer and distributer of electricity and gas company. In May 2019, Enel Green Power (a subsidiary of Enel SpA), approved the PresAGHO project (Predictive System and Analytics for Global Hydro Operation) which introduced a predictive maintenance model to address potential faults in hydroelectric power plants. ABB was selected to deliver the Ability Asset Performance Management solution, enabling Enel Green Power’s 33 hydroelectric plants, comprised of about 100 units, to move from hours-based maintenance to predictive and condition-based maintenance.

EDF

EDF Energy Ltd (EDF Energy), a subsidiary of the EDF Energy Group Holdings Limited has utilized Schneider’s EcoStruxure Maintenance Advisor solution, which has helped the company to save over $1 million by minimising equipment damage and lost production. The company has also adopted Emerson’s AMS Suite predictive maintenance software to enable optimization of maintenance strategies at one of its combined cycle gas turbine (CCGT) power stations at West Burton,  the UK.

To further understand the key themes and technologies disrupting the power industry, access GlobalData’s latest thematic research report on Predictive Maintenance in Power.

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