Experts speak: Challenges of Predictive Maintenance in Power & Utilities

Predictive maintenance uses performance monitoring and equipment condition monitoring to predict possible failures, and thereby decrease the chances of failure and downtime through preemptive maintenance. The market for predictive maintenance technologies is anticipated to register a CAGR of 27.6% over the period 2022 – 2027

At 3rd Predictive Maintenance in Power & Utilities conference, we are gathering experts and decision-makers from the energy industry, namely power generator companies, power plant owners and operators, O&M operators, and experts responsible for asset management for benchmarking predictive maintenance best practices. We asked our panel of experts about the biggest challenges and the trends in predictive maintenance for power generation and utilities.

Mario Augusto, Itaipu


Mario Augusto Caetano dos Santos – Maintenance Engineer, Itaipu Binacional, Brazil

Many power generation companies and utilities face the same challenge: providing energy and services with quality and competitive costs while managing aging assets. It’s not an easy task! Predictive maintenance as an approach within the asset managment strategy has the ability to drive efforts (especially budget) based on risk analysis nd KPIs. Today, the growth of data science and associated technologies has taken predictive maintenance to a new level.”


Luiz Andre Moyses Lima,voltaliaLuiz André Moysés Lima –  Senior Renewable Resources Analyst, Voltalia, Brazil

“Large fleets that span multiple wind farms may include turbines of different models, brands and characteristics. The same principle applies for solar plants. One of the greatest challenges related to Predictive Maintenance is to create and automatize accurate prediction processes while still keeping them general enough, so that they work properly for different equipment, locations and weather conditions.

Kristofer Jakobsson

Kristofer Jakobson –  Senior Data Scientist, Fortum, Sweden

“AI tools show great potential in improving both the operation and maintenance of energy assets, but there are many obstacles on the way from proof-of-concepts to a scalable, robust and maintainable system. Fortum’s ongoing digitalization journey for the hydropower fleet provides concrete examples of both such obstacles and possible solutions

Teemu Vekkeli, Eurus EnergyTeemu Vekkeli – O&M Technical Manager, Eurus Energy Europe, The Netherlands

“The biggest challenge for predictive maintenance is the required change in mindset. Operations and Maintenance organizations will need to adapt their daily operations to generate real benefits from a predictive approach. Asset owners will need to be the drivers of this change. The industry as a whole will benefit from collaboration of owners and operators.”

Annie-Marie, Hydro-Quebec Anne-Marie Giroux – Research Scientist, Hydro-Quebec, Canada

Availability of data from sensors connected to assets and the development of AI capabilities and other analytic tools make possible the transformation of classic systematic maintenance to one that is adjusted to the actual condition and risk of failure of a specific equipment. The implementation of early anomaly detection, which helps in adapting inspection frequencies, remains challenging for assets that are not serial made and which behaviour changes with the natural conditions (water levels, water and air temperat…res, …) like hydropower units.

Physics-based digital twins may not only detect anomalies, but also identify their cause (diagnostic) and ultimately do some prognosis, helping the asset owner to plan the right maintenance action at the right time, and thus minimize maintenance costs while preventing outages. The development of digital twins of such complex equipment is a very challenging task that we started to address by splitting the whole unit in smaller parts: turbine, generator, shaft and bearings, speed governor and excitation system.

João Fontes Machado, EDP

João Fontes Machado – Data Analytics & Automation, EDP, Portugal

“The main goal for predictive maintenance is the same it has always been: maximizing the success rate of problem detection, while providing the exact cause. Being able to remotely tackle as many issues as possible is also of tremendous importance – given that it will reduce costs for both the client and the service provider, whenever it is the case.”

Martina Tamburini, edisonMartina Tamburini – Head of Datalab Upstream & Staff, Edision Spa, Italy

“The fast development of renewables in the coming years brings a big challenge to energy companies on O&M activities because plants will become more and more numerous and geographically distributed. Companies have to create a flexible infrastructure that could easily scale up by leveraging digital tools and advanced use of data from a predictive point of view: this represents both a technological and cultural tra”sformation.”

Join 3rd Predictive Maintenance in Power & Utilities on May 10 – 11, 2022 to demonstrate how a well-established PdM strategy affects overall power generation and ROI in terms of effective asset management, reduced downtime, and better outage control with experts from Edison, EDF, Hydro-Quebec, Itaipu Binacional, Engie, Voltalia, Fortum, Eurus Energy  and more.

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