Wireless Vibration Monitoring Breezing into Wind Industry

Wind turbines can, and do, fail. And, when they do fail, they do so in spectacular fashion. A quick search on YouTube for “wind turbine failures” yields numerous videos of exploding wind turbines, wind turbines on fire, or involved in other high-stress calamities.

However, predictive maintenance with wireless vibration monitoring can offer a solution.  By using wireless vibration monitoring to continuously monitor the health of wind turbines, wind turbine operators can access information that can be used to reduce operational downtime.  By tracking vibration patterns that indicate an urgent need for maintenance the operator can take action to repair the turbine before a catastrophic failure occurs.

Furthermore, by using the vibration monitoring information to indicate when maintenance activities need to be performed the operator can reduce unnecessary maintenance and shutdowns, enabling additional significant savings to be realized.  First, let us look at five specific phenomenon that vibration monitoring may detect to avoid excessive downtime or failure.  Then, we will take a look at the potential value of wireless vibration monitoring on a hypothetical fleet of 100 turbines.  Finally, a real-world use case will be discussed.

Premature Bearing Failure due to Shaft Misalignment and Other Causes

Shaft misalignment is one potential source of premature failure.  Misalignment caused by a bent, offset, or angled shaft can cause excessive loads on the bearings, leading to premature failure of the bearings.  Other causes are improper lubrication or over-greasing, contamination, and over-speed.  With vibration monitoring, and predictive maintenance, there is a greater chance of detecting impending failure allowing the turbine operator to make repairs before a more costly and time consuming failure occurs.

According to a study, bearing failures are the most common overall failure mode.  In less than 1MW generators bearing failure accounts for generator failures roughly 20 percent of the time.  In the case of 1MW to 2 MW generators, bearing failures are responsible for about 70 percent of instances.  Last, the study found that in greater than 2MW generators, bearing failure is the cause of failure a little less than 60 percent of the time.

Generator Failure due to Shaft Misalignment and Other Causes

In some instances, failures due to shaft misalignment and other causes can cause more expensive generator failure.  This can cause extended downtime and dramatically increase the total cost of failure.  With monitoring and effective correction of the problem’s root cause, an operator should be able to avoid generator failures by detecting bearing failure before total failure of the generator occurs.  Even though immediate success may not happen when trying to prevent generator failures, having access to historical data that precedes a generator failure can be extremely helpful in identifying a pattern and preventing additional failures once the cause is understood.

A wireless vibration sensor mounted on a wind turbine gearbox

Premature Gearbox Failure

According to a study completed by the U.S. Department of Energy’s National Renewable Energy Laboratory, gearboxes are one of the most expensive components of a wind turbine system, and yet they often require major repair well before the design life of a wind turbine has been reached.  For this reason, it is paramount that gearboxes are monitored, as monitoring has the potential to detect possible failures before they actually occur allowing the turbine operator to conduct repairs that are less expensive and require less downtime. 

While monitoring cannot prevent normal gearbox wear such as broken or worn gear teeth, wireless vibration monitoring can be used to detect the increased vibration levels that accompany a broken or worn gear tooth.  As a result, wind turbine operators can than make the necessary repairs to the gear with the broken, or worn, tooth before that failure cascades into a failure that is more catastrophic and requires more costs and downtime.

Avoidance of Unnecessary Preventive Maintenance

Some preventive (scheduled) maintenance procedures may be unnecessary, but are performed on a regular basis, often quarterly, based on overall fleet wide guidelines.  However, with a steady flow of diagnostic information on each turbine through continuous monitoring, an operator can more intelligently choose which turbines should receive maintenance attention, and which do not need any immediate attention.  This can create significant savings by reducing unnecessary repairs. 

In addition, since failures rarely, if ever, follow a schedule; it is possible that a large failure could occur between scheduled maintenance periods.  With continuous monitoring it is possible to detect potential failures between scheduled maintenance activities, further reducing the need for reactive repairs. 

Improper or Ineffective Repairs

Continuous monitoring also provides before and after data on the operational status of machinery.  This information can be used to confirm that a maintenance activity has or has not achieved the desired result.  In this case, savings are achieved by reducing repeat maintenance procedures form occurrences of improper maintenance (such as over greasing) or failure to catch and repair the root cause of the problem.

Additional Fleet-Wide Cost Savings Opportunities

Savings also can be achieved through increased overall awareness of operating conditions.  These other savings opportunities include:

• Troubleshooting operations (For example, in the hours after start-up, or immediately after maintenance.)

• Early notification of unusual vibration

• Enhancing alertness to recurring problems as evidenced by patterns in monitored data


• Faster response based on better understanding of maintenance solutions

Savings at a Hypothetical Wind Farm

A 2006 report from Sandia Laboratories showed that maintenance costs range from $0.005 to $0.006/kWh for new turbines and escalate to approximately $0.018 to $0.022/kWh after 20 years of operation. The cumulative operation and maintenance costs represent 75 percent to 90 percent of a turbine’s investment cost.  Based on supporting research, a monitoring system can potentially reduce maintenance costs by 5.7 percent, which would offer an energy production cost savings of roughly $0.0011/kWh for turbines that have an age of more than a few years.

The Sandia report described that one of the primary ways to reduce maintenance cost is through condition monitoring.  This is in part due to the fact that unscheduled maintenance accounts for 30 percent to 60 percent of the total maintenance cost and generally increases as the project matures.

As an example, of how this knowledge can be practically applied (even with smaller wind turbines), let us consider a typical small wind farm with 100, 250kW turbines.  As shown in the table, we will assume that our hypothetical wind farm has an average of 15 occurrences of premature bearing failure due to shaft misalignment, three occurrences of generator failure due to shaft misalignment and other causes, two occurrences of gearbox failures, 40 instances of unnecessary preventive maintenance, and 40 cases of improper or ineffective repairs per year.

Given the above assumed failure rates, and a well implemented predictive maintenance program where five points in each turbine are monitored with wireless vibration sensors, our wind farm operator can save approximately $378,780 in total costs.  Based on current market costs for predictive maintenance technologies the operator will see a return on his, or her, investment in approximately a year or less.  (See the table for a breakdown of individual the areas of savings.)

Finally, by utilizing wireless vibration monitoring systems with new advances in sensing technology, the installation of the monitoring system can be completed in under an hour and can be done during a maintenance call.  Moreover, with Cloud technologies, not only can sensor information be seen locally, but turbine operators can view turbine vibration data from anywhere in the world.  With these two additional abilities, quick install and Cloud technologies, monitoring wind turbines can be easier and less costly when compared to traditional wired sensor systems or manual inspections.

Wind turbine Monitoring: a Real-world Use Case

Wireless vibration monitoring of wind turbines is not just an idea, it is already at work in the UK.  While the program that was developed was not a result of a particular failure, a wireless vibration monitoring system was put in place to help gather diagnostic information on wind turbines in real time, so that response times to problems can be faster and downtime can be reduced.

A wireless vibration sensor mounted on the blade root of a wind turbine

With such vibration data, the turbine operator can identify where a fault may be and send the proper repair personnel and equipment to correct the issue.  Then, after the problem has been fixed, the turbine operator can reevaluate the problem and monitor vibration to see if it returns.  If it does, the operator often can use the gathered data to figure out what the problem might be, an ability that grows with experience.

The effectiveness of wireless vibration monitoring can be seen in a trend chart screen shot taken from the installation that showed increasing vibration levels in one wind turbine’s generator that suggested to the operator a potential shaft alignment problem.

Furthermore, in this case the turbine operations and maintenance organization is also subject to penalties for excessive downtime.  Avoiding these penalties is further motivation for implementing predictive maintenance programs.

While wireless vibration monitoring, along with other proactive maintenance practices, have the potential to help keep operators’ wind turbines from playing a starring role in a YouTube horror movie, the greater benefit of the monitoring of wind turbines comes from helping wind farms realize greater operational savings and profits.

Lead image: a UK wind turbine equipped with wireless vibration monitoring technology, via KCF

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Christopher Shannon is a Product Analyst at KCF Technologies.

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