Maintenance 4.0 in wind farms

Bringing smart analytics to clean energy

Supply chain logistics can be challenging for wind farms due to the cost and complexity of transporting spare parts to remote locations. AI-driven Industrial Analytics helps wind farms reduce spending on spare parts by extending asset life, while offering new opportunities for incremental revenue.

Why is IIoT for Predictive Maintenance so attractive to Wind Farms? 

Up to 30% of the levelized cost per kWh produced over the lifetime of a turbine can be attributed to Operation and Maintenance.  

Unscheduled wind turbine downtime can last a week or more per year, and in some cases, significantly more time. Because wind turbines are usually installed in remote locations, it is often expensive to transport heavy replacements. In the case of a generator, transportation can take more time than the actual repair. It is estimated that over 70% of wind turbine downtime is due to major repairs. 

For operators, the high O&M cost is an opportunity to generate incremental revenue, which is driving the interest in IIoT Predictive Maintenance. 

The Limitations of Current Maintenance Techniques

Predictive Maintenance (PdM) using SCADA data is the default system used in Wind Farms today. How does it work? Sensor data from turbines are monitor based. If manually set control limits are breached, operators are alerted.  

However, there are two major limitations. First, only a limited amount of sensor data can be monitored. In many instances, the root cause of machine failure is from unknown sources. With traditional PdM, unless the root cause was caused by one of the selected sensors, it will not be detected.   

The second limitation is based on how the SCADA data is monitored. If control thresholds are breached, alerts are generated. However, in many cases, once the control threshold has been breached, it is already too late to prevent machine failure. 

The lack of visibility into evolving downtime reduces the effectiveness of PdM. 

PdM 4.0: Unsupervised Machine Learning for Asset Management

There are different methodologies used for IIoT Predictive Maintenance. Let’s start with the term “Supervised Machine Learning.” With Supervised Machine Learning, the learning algorithm is “trained” using human guidance and labels of abnormal and normal wind turbine conditions.  

In the case of a wind turbine, the learning algorithm needs to understand the physical layout of the machinery based on its blueprints. Additionally, it needs training on mechanical processes. Once new data is analyzed, machine failure can be recognized based on the training of the algorithm. 

This level of training is not required for Unsupervised Machine Learning. For instance, the algorithm does not need to be trained using knowledge of the mechanical processes of the wind turbine or its blueprints. 

Automated Unsupervised Machine Learning is the next level of Machine Learning. In this case, models are self-maintaining and self-learning and can be applied to various sensors and machine types.  

The SKF Enlight AI model combines the latest in Machine Learning with our decades of asset maintenance knowledge and expertise to deliver a highly scalable Predictive Maintenance solution. 

What is the Economic Benefit of IIoT Predictive Maintenance?

The goal of IIoT Predictive Maintenance is to provide alerts of asset degradation and evolving failure early enough to prevent unscheduled downtime. If operators are alerted to failure, they can reduce workloads while parts are ordered. Conversely, Reactive Maintenance is both expensive and more time-consuming.   

The overarching goal of IIoT Predictive Maintenance is lower O&M costs and higher yield rates. 

Let’s look at some specific examples. The estimated O&M cost for Germany, the UK and Denmark are between 1.2 c€ and 1.5 c€ per kWh of wind power produced. It is estimated that approximately 50% of this is allocated to insurance, administrative costs, and other overhead expenses. Using IIoT Predictive Maintenance, one can expect a reduction in replacement and labor costs. 

Assuming a 20% reduction in the repair and maintenance portion of O&M costs, this would translate into annual cost savings of $11,383 for a 2.5-MW turbine and $34,148 for a 7.5-MW turbine. 

SKF Enlight AI

AI-driven Industrial Analytics Can Power Wind Farm Savings

The SKF Enlight AI model combines the latest in Machine Learning with our decades of asset maintenance knowledge and expertise. The result is a Predictive Maintenance product that not only detects emerging asset failure far in advance but also assists in the repair and prevention of reoccurrences of these failures in the future.   

In this constantly adapting system, AI-driven insights prolong machine health for the specific asset affected, as well as for assets throughout and across plants. 

By applying Machine Learning products like Enlight AI to their Big Data, Wind Farms can make their already environmentally-friendly operations more efficient, reduce unnecessary costs and increase savings.


Interested in reading more Wind business related articles? Check out our Wind Farm Management Blog.

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