In today’s industrial landscape, sustainability is no longer a peripheral concern; it is a central pillar of long-term strategy. As organizations strive to reduce their environmental footprint while maintaining their operational efficiency, we at Schneider Electric believe that sustainability can start with how we manage and maintain our assets.
By keeping industrial and electrical assets in service for longer, companies can reduce the energy and raw materials required for manufacturing. At the same time, they can minimize waste and help preserve finite abiotic resources.
In this blog, I will explore how Sustainable Asset Management, powered by digital twins can help organizations extend asset lifetime and reduce environmental impact. This blog is part of a series and focuses on the circular part of sustainable asset management, looking on how to extend the lifetime of assets.
Rethinking product lifetime: From Ex-Ante to Ex-Post
Traditionally, the expected lifetime of an asset, an equipment, has been defined using an Ex-Ante approach, based on laboratory tests in the design stage, or performance assumptions under standardized mission profiles which are embedded in the Product Category Rules use in lifecycle assessment. While useful for initial planning, this method often fails to capture the complexity and variability of real-world operations.
In contrast, an Ex-Post approach leverages real-time usage data, collected through IoT sensors and remote monitoring, to estimate the true condition and remaining useful life of assets. This shift allows for more accurate, dynamic, and context-aware decision-making. It acknowledges that environmental conditions, operational stress, and maintenance practices all play a critical role in how equipment ages over time.
End of Use is not necessarily the End of Life
A key insight in lifetime extension is the distinction between end of use and end of life, as formalized in the IEC 60050-193 standard. According to this framework, an asset may be withdrawn or retired due to an evolution in process or technology, or operational needs, this is the end of use. However, this does not necessarily mean that the asset has reached the end of life in terms of functional capability or safety. In many cases, such equipment can be refurbished, upgraded, repurposed, or reused elsewhere within the organization, especially when its condition has been validated through ageing analysis. Ageing analysis is the process of evaluating how an asset’s condition evolves over time due to operational stress, environmental exposure, and wear. It involves collecting and analyzing data on factors such as temperature, vibration, corrosion, and electrical load.
This analysis helps determine whether an asset is still fit for use, can be refurbished, or should be retired. For instance, a transformer that has been in service for 20 years might still be operationally sound if ageing indicators show minimal degradation—allowing it to be refurbished. Components may also be harvested to replenish spare part inventories, provided they meet quality and reliability criteria.
This perspective opens the door to more circular practices, where assets are not simply discarded but reintegrated into the value chain. By “using again” in new and productive ways, organizations can reduce waste, preserve resources, and extend the value of their investments or unlock new cost savings.
The role of digital twins in lifetime extension
To precisely maintain ageing equipment, organizations need tools that provide deep real-time visibility into asset condition. This is where digital twins come into play. A digital twin is a virtual representation of a physical asset, enriched with real-time data and advanced analytics that simulate its behavior and degradation over time, thereby predicting maintenance before it is required.
Two levels of resolution models can be applied to the digital twin. This dual approach allows companies to balance cost and better accuracy, tailoring their strategies to the criticality of each asset:
- A medium-resolution model uses general sensors and physics-based analytics to provide broad recommendations, typically temperature sensor and humidity variables sensor.
- A high-resolution model combines advanced asset specific sensors, see below the example for a new generation of Medium Voltage Circuit breaker. These sensors work together with both physics-based and data-driven analytics to provide detailed, customized insights.
For example, extensive digital sensors have been implemented in EvoPacT, a new, more robust, and smart Vacuum Circuit Breaker. Those sensors tackle:
- the mechanism speed sensor, through rotative speed measurement,
- the vacuum interrupter erosion gap sensor,
- the tripping coils current sensor,
- the charging motor current sensor.
Thanks to those sensors, the development of AI-driven ageing failure prediction could be started. Machine Learning algorithms require datasets for training, and these datasets must include examples of failures. Algorithms are trained to predict failures and can only do so if they are trained on failure data.
This was made possible through accelerated aging campaigns conducted until failures occurred. The development was first focused on the erosion gap sensor that effectively captures several ageing mechanisms occurring in the contacts and the mobile poles. The algorithm that was developed aims at predicting the remaining number of operations before reaching a critical level of the erosion gap. This algorithm achieved an accuracy of over 90% in predicting the remaining number of operations.
Usage in condition-based maintenance
This principle is also used for AI-driven Condition-Based Maintenance (CBM) – a proactive maintenance strategy that employs the Ex-Post approach i.e. real-time monitoring and diagnostics to determine the optimal time for servicing equipment. Instead of following a fixed schedule, CBM uses data and analytics to assess the actual condition of assets, allowing maintenance to be performed only when necessary. Traditional CBM relies on threshold-based alerts and rule-based diagnostics. However, AI-driven CBM enhances this by using machine learning models to detect subtle patterns, predict failures earlier, and recommend optimal interventions.
A multi-criteria decision framework
Once the digital twin is in place, it becomes feasible to explore a range of lifetime extension strategies. These may include adjusting maintenance policies, modifying operational parameters, reallocating assets, or collaborating with suppliers to extend support and spare part availability.
Importantly, these decisions are not made in isolation. A robust framework considers multiple criteria:
- Financial metrics, such as Net Present Value (NPV), to ensure economic viability
- Environmental impact, including auditable carbon emission calculations
Risk factors, such as safety, production continuity, and uncertainty in future conditions
This multi-dimensional approach ensures that lifetime extension is not only technically feasible but also strategically sound.
Why this matters to you?
Extending the life of your assets isn’t just good for the planet; it’s a smart business strategy. By adopting a circular asset management approach, you can extend the life of your electrical assets by up to 50%.
At Schneider Electric, we support this transformation through a circular economy framework built on three core principles: Use Better, Use Longer, and Use Again. With AI-driven condition-based maintenance, 24/7 remote monitoring, and the expertise of over 6,000 specialists and 300 in-house data scientists, we help you make informed decisions that extend asset life and reduce waste. Our take-back, repair, and recycling services ensure that when assets do reach the end of their first journey, they are managed responsibly. At Schneider Electric we act at each step of the product lifecycle to support your sustainability goals.
Based on MasterPacT practices which consist in following our experts’ recommendations to reduce humidity and prevent corrosion.
To learn more about our circularity journey, please visit our website.
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