How the Renewables Sector Is Getting a Boost with Asset Failure Prediction

Introduction

In the renewable energy sector, equipment or asset failure can spell serious negative consequences for any company. Renewable energy companies are switching to data-driven predictive maintenance to protect themselves from unexpected failures. Technologies like the IoT and AI are driving the adoption of predictive maintenance in the sector.

As an integral part of predictive maintenance, real-time asset failure prediction can improve uptime and optimize maintenance costs.

But what exactly is asset failure prediction, and how does it work? Let’s discuss it in full detail in this blog.

What Is Asset Failure Prediction?

In recent times, asset-centric industries, including renewable energy companies, have seen equipment failures and safety incidents associated with their machines. In fact, unplanned machine downtime reportedly costs $50 billion a year to industrial organizations.

Asset failure is the exact manifestation of functional failure. For instance, a solar panel has a functional failure when it does not generate the expected volume of solar energy. Without any identified reason or cause, the solar panel is simply found to have low energy output. In the real world, there are multiple reasons for the functional failure of the asset.

Data-based asset failure prediction is a form of predictive maintenance that uses intelligent sensors to predict when an equipment failure is likely to occur. With this method, energy companies can allocate necessary resources to prevent downtime, thus saving on any part replacements.

Next, let’s discuss the business benefits of asset failure prediction in the energy sector.

Business Benefits of Asset Failure Prediction

Successful predictive maintenance in any industry depends on real-time measurement of assets and their performance levels. The asset failure prediction model can predict asset failures based on previous failure patterns.

Here are the top 5 benefits of asset failure prediction for the renewables sector as part of predictive maintenance:

1. Lower Asset Downtime

Regular monitoring of important assets can cut machine failures significantly. Based on this principle, asset failure prediction provides real-time data on the health and performance of every asset. This means maintenance engineers can take action before equipment failure.

2. Lower Maintenance Costs

Predictive maintenance in the renewable industry can reduce capital expenditures in equipment maintenance. By predicting asset failures, energy companies can prevent unexpected downtime and its associated costs. They can also reduce asset repair costs by adapting to data-powered maintenance schedules.

3. Improved Customer Satisfaction

In the renewables sector, asset failure has a direct impact on customers. Utility companies try to avoid any unexpected outages to prevent any inconvenience to their customers. With asset failure prediction, customers are notified in advance about any possible outage or failure. This helps in improving customer satisfaction.

4. Longer Asset Lifetime

By detecting asset-related problems at the earliest, predictive maintenance can also increase the service life of the asset. Energy companies can also reduce the severity of damage caused by a malfunctioning component – and prevent it from affecting other parts.

5. Improved Safety

Along with the accurate prediction of asset failures, predictive maintenance can address any safety-related risks to maintenance teams or asset operators. Maintenance teams can quickly take corrective actions and mitigate these safety risks.

What are the concerns in the renewables sector about applying this technology? Let’s discuss them next.

Industry Concerns About Asset Failure Prediction

The renewable energy industry handles massive volumes of data from their field assets and IoT sensors. While some players leverage this data using predictive maintenance, others are hesitant to implement this model.

Among the main challenges, energy companies fail to understand the business impact of any asset behavior. For example, how does increasing machine uptime improve the company’s bottom line? Energy companies need a top-down approach to identify their business objectives and map them to the machine outcomes.

An additional challenge is the lack of the right data. While utility companies generate massive data volumes, it is complicated for them to extract the right data. For accurate predictions, predictive maintenance models must receive the right and relevant data.

With the increase in connected renewable assets and IoT devices, renewable companies need to maintain a complex ecosystem of products, processes, and services. The challenge for them is to extract real-time data insights from this ecosystem of physical assets and networks.

Digital twins can facilitate this by creating a replica of this complex ecosystem. With digital twin technology, renewable energy companies can effectively:

• Troubleshoot problems in equipment located in remote locations
• Visualize the entire asset ecosystem in real-time
• Connect disparate systems and enable traceability
• Manage the complexities of this ecosystem of renewable assets

Conclusion

Across the growing renewable energy sector, companies are adopting predictive maintenance to reduce downtime and improve the service life of their installed assets. Asset failure prediction is an efficient tool to predict and prevent the failure of renewable assets.

As a solution provider to the energy industry, Pratiti Technologies provides a range of services, including asset monitoring and predictive maintenance. With our technical expertise, we can create a digital twin version of your physical plant, which is useful in improving operational efficiency and performance.

Want to be part of our growing digital ecosystem? Contact us today!

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