An electrical current that discharges through a bearing produces damage of this sort called fluting.

The purpose of condition monitoring is to predict failures. Vibration condition monitoring, when configured correctly (and most are not), gives a lead time to failures from 6 to 24 months on major drivetrain components. You may ask: How is that possible? It turns out that each component emits a vibration relative to its condition.

A good bearing for example, will not emit a particular vibration at a high level. A bad bearing will. When configured and monitored correctly using vibration condition monitoring, this emitted vibration is measured relative to heath of that component.

Unfortunately, there is a common misperception in the wind industry that a baseline vibration is essential to understand whether or not a component is failing. Tire pressure provides an analogy that might be helpful. For instance, do you need to know the tire pressure when you drive your car off the dealership? Of course not. You know when a tire goes flat.

The vibration signal comes from a generator bearing in a wind turbine after some period in service and before a lubrication. All graphs here plot an instant of vibration versus frequency.

While this trend example is not a perfect analogy, there are a few non-standard trends in vibration that are quite helpful from an O&M perspective.

Lubrication trends
One of the most commonly measured vibration signals tells of a lack of lubrication. This is particularly common in generators and main bearings where lubrication is essentially predicated on interval maintenance. How widespread of an issue is this?

The same bearing from the previous plot after lubrication. It shows a significant reduction in vibration.

Of the gigawatts of turbines currently monitored, most every site has a widespread lubrication problem. In the illustration, A pre-lubrication vibration measurement, vibration sensing has detected a high level of bearing vibration so a check of the lubrication interval was suggested.

The next scheduled lubrication was not overdue. You can see on the graph, Post-lubrication measurement, that lubrication did in fact initially reduce the vibration. A second application was needed as indicated by the second peak in the vibration trend.

Finally, after the second application of lubrication, the generator bearing vibration returned to normal. Observing the trend could let an O&M team avoid a nearly $80,000 charge for a new generator bearing. Several other generators on this particular site also displayed similar vibration characteristics. The chart, Vibration trends pre and post-lubrication, shows a representative wind farm and its vibrations.

The brown plot indicates high vibration in a bearing. With sufficient time, it’s easy to see a trend to greater vibration. (A trend, however, is not needed to determine that a vibration if too high. The orange line indicates an alert level while the red line indicates an alarm level. At the first blue arrow, the turbine was taken out of service for maintenance and returned sometime later at a vibration level lower than when the plot began.

It turns out that most vibration measured relative to the drivetrain health is towards the back half of the wind turbine, toward the generator. Most turbine models follow this same failure distribution as the higher the speed, the faster, and more frequently these components fail. That means on average, a main-bearing failure trend will take 24+ months to fail at 16 rpm. While the generator bearings will take 6+ months at 1,400 rpm.

Post repair trends
This is very basic. Vibration that measured high prior to repair should trend to a normal vibration level post repair.

The vibration from a main bearing indicates a lubrication problem.

Transient operational vibration trends
In this newer age of mining SCADA data for improvements in production, it is surprising how much CMS (condition monitoring system) data is available from properly configured systems that would assist with improvements.

While there are limited SCADA data inputs relative to accurate health prognostications, there are specific data inputs in CMS that illustrate loads and trends. Vibration-trend measurements make it easy to see spikes in component vibration under certain operating conditions. Here is what must happen to make better use of vibration data:

Vibration peak indicates an issue in the generator. Further electrical testing is needed to determine a root cause.

Correlate vibration data with operational data
The controllers in steam and gas turbines are preprogrammed to avoid critical speeds and resonances. These known operating conditions result in high vibration. There is room for the same approach in the wind industry given the ability to understand detrimental vibration under specific controller conditions.

Variables, such as pitch, yaw, and others create detrimental component vibration. There are a few considerations. For example, these vibration signals are specific to a component. As evidenced by these trends, certain operational conditions impact specific component health.

Hence, measurement trends capture two unique vibrations: one under specific operating conditions and a vibration specific to a component. For example, vibration measurements should show a significant spike in the main-bearing vibration when a yaw angle deviates from the wind direction. The spikes arise when the yaw deviates more than 6° on some towers.

The vibration signal indicates an electrical issue and bearing damage from fluting, a condition that occurs when a stray current discharges through a bearing.

That means a nacelle misaligned with the wind direction generates a load that imparts undue stress to downwind components. Vibration also measures this periodic spike in vibration in the rest of the drivetrain.

Generator test trends
Of the drivetrain failures, nearly 50% are located in the generator. Of these, about 66% manifest themselves in bearing failures. While these are easy to see and predict in condition monitoring data, the other 33% of failures are electrical in nature and less obvious.

However, vibration condition monitoring, when done correctly, can detect these failures in a general sense. Specific electric tests can point to subcomponent failures.

These tests take little time per generator and are trend-able. For example, surge, partial discharge, polarization index, resistance tests are all trend-able and helpful in determining failures in the generator windings, insulation, and wire.

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Up-tower wind-turbine repairs average $5,000 to $6,000 for a generator bearing change out, and $80,000 to $90,000 for an unpredicted failure that requires a crane. The wide price differences show that condition-monitoring systems can have a tremendous return per incident. However, not all products are equivalent or effective for monitoring each turbine component. It is important to do your research.

The advantage of predictive maintenance is that it lets wind operators proactively plan repairs or replacements, and only when needed so as to avoid unnecessary and costly up-tower jobs. One recent study showed that most wind farms still use a reactive maintenance system for turbines, and that wind operators could save millions of dollars by using new preventive-maintenance technologies that identify problems before they result in unplanned downtime.

One way wind operators benefit from predictive maintenance is by first setting alerts to signal when a diagnostic signal crosses a specified set point. Then, for example, if an operator were alerted to a temperature increase in a turbine’s generator, ideally a wind tech would quickly fix the issue before it becomes a serious problem.

However, a recent study cited in Wind Energy O&M Report 2017, New Energy Update compared the performance of predictive and condition-based O&M strategies, and found that a predictive approach was not always the most effective. The report used a scoring strategy to measure the performance of different sensor configurations under various key component failure scenarios. The research examined 3-MW turbines on a 630-MW wind farm, and 2-MW turbines on a 420-MW capacity wind farm.

Results showed that a condition-based monitoring strategy using all sensors (except oil sensors) was the optimal O&M strategy for the 630-MW wind farm, but the preventative approach was more effective for the smaller, 420-MW project. Overall, a preventive strategy was more costly, and particularly under a gearbox-failure scenario.

So how does a wind operator effectively choose a condition-monitoring system (CMS) for a wind project? According to David Clark, President, CMS Wind, it is an important question. The answer should be wind-farm specific. “Part of the problem is the erroneous assumption that all condition-monitoring systems for wind turbines are the same,” he says. “That’s like saying all cars are the same.”

Although condition monitoring is now typically considered a must in the industry, the sheer number of different systems available has led to problems. “Not all systems are equal,” says Clark. “And buying an ineffective condition-monitoring system is costly, it can be damaging and has contributed to a slower acceptance of CMS in the industry. It is truly a buyer-beware scenario.”

Clark says that contrary to the advertising, not all systems are ideal for an application and typically more than one tool is necessary. For example, oil-related sensors and filtering systems are typically focused on gearbox monitoring (which make up about 50% of the drivetrain failures), while vibration-based systems tend to work on the drivetrain as a whole. Conditions and events, such as icing or lightning, also require the correct sensors and properly configured CMS.

“Take sensors, for example. There are only so many signals and measurements one can pull from a sensor. Unfortunately, there are many variables beyond just the sensor and hardware installed in a nacelle,” says Clark. “And if the core CMS system has incorrect hardware, issues go undetected.”

An evaluation of seven different condition-monitoring systems (CMS) installed by OEMs and aftermarket vendors show the odds are high that a system will have improper sensors or the sensors be mounted incorrectly. The end results will be missing detections and false alarms.

To compound this, when measurements are not correctly established, expect missed detections and false alarms. “It is also important to consider a system’s software capability to store alarm and measurement criteria, and perform analysis for proper preventative maintenance,” he adds.

When assessing potential risks to a CMS program, Clark says there are a few points to consider.

• Incomplete data. This includes a lack of full data and access to report configuration, or simply no one properly reviewing the available information.
• Poor analysts. Incorrect calls and lack of experience in wind has, in some ways, created an illusion that CMS is ineffective.
• Ineffective monitoring. A poor system choice for a specific wind farm, or poor communication between analysts and wind operators.
• Ease of use. Data must be available in real time and in simple, easy-to-understand terms or formats for proper reporting, analysis, and understanding.
• Predictive maintenance. Proper component monitoring can lead to significant cost savings. Case in point: a single onshore event involving a crane is $300,000 on average for a 1.5-MW wind turbine. Avoiding the crane cost by predicting the failure using predictive CMS drops the per-event cost to $12,000 to $15,000.

Ideally, a CMS will also include predictive analysis. “However, just because a wind turbine is optioned with preventive monitoring does not mean that it is the best solution or even a viable one. Do your research,” Clark advises. “Education is invaluable and key to a productive, well-run wind farm.”

This article was part of the 2018 Renewable Energy Guidebook. View the full publication here.

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In this WindTalk podcast, CMS Wind President David Clark discusses three basic approaches to CMS. He also deciphers facts from some common CMS myths to help wind-farm owners and operators maximize their assets.

The post Myths and facts about condition-monitoring systems for wind turbines appeared first on Windpower Engineering & Development.