Avoid maintenance surprises: Summit 2010, Sept 7 to 9

SmartSignal, a condition monitoring firm, will host its Summit 2010 on Sept 7 thru 9 at the Fairmont Hotel, Chicago. Find more conference details, download a pdf agenda, and register at: www.smartsignal.com/summit.aspx
At Summit 2008, over 200 SmartSignal customers gathered to discuss best practices, hear about product developments, listen to industry gurus, and network with peers. The summit organizers are glad to be back in 2010 with an even better Summit. The company says it has a lot to talk about, with multiple new product and service offerings and over 30 new customers from around the world since 2008—new users who can bring fresh perspectives on how they optimize operations.
To make Summit 2010 useful and engaging, the organizers have:

1. Content provided almost entirely by peers talking straight talk to peers about real-world stories: how they implement Predictive Analytics and Diagnostics solutions into businesses. What works, how to get quick results, and how to overcome challenges.
2. More time to ask questions and interact—with a new program of customer panels followed by Q&A.
3. Access to experts and current users to learn about SmartSignal products and services.
4. An entire track of training sessions, including CEU credit courses on equipment maintenance & reliability and featuring “The Reliability Game.”
5. Vision of a transformational maintenance breakthrough with reports from pioneering customers.

A growing list of engaging customer speakers from companies such as APS, Alyeska, BP Alaska, Caterpillar, Chevron, Consumers Energy, Constellation Energy, Edipower, Entergy, Gas Natural Fenosa, Invenergy, Laborelec, Mirant, New Harquahala, RRI Energy, RWE Npower, Scottish and Southern Energy, SRP, We Energies, and others.

How to monitor 975 turbines at 14 wind plants

Invenergy owns and operates just such a fleet in the U.S., along with other clean energy generation facilities in North America and Europe. To help maintenance personnel track multiple sensor signals at each site, the firm selected SmartSignal Corp. (smartsignal.com) to install SmartSignal EPI*Center Predictive Diagnostic software, which will be “watching” all 975 GE 1.5 MW turbines at all Invenergy U.S. wind generation projects from one centralized location. The rollout comes after a collaboration between the two companies along with a three-month test drive at Invenergy’s Forward site in Wisconsin. The agreement calls for SmartSignal to deploy, maintain, and provide support services for EPI*Center, which will be hosted by Invenergy.

The predictive diagnostic software detects developing mechanical and instrumentation failures for its wind customers. The company develops personalized empirical models for all turbines, each of which is composed of a sophisticated combination of interrelationships of all related sensors that affect turbine performance. These unique turbine models automatically adapt to fluctuations in wind speed, direction, shear, turbulence from ambient conditions, and equipment performance. In real time, the monitoring system analyzes all the data collected in the nacelle–literally tens of thousands of data points daily on a typical wind farm–and detects and notifies wind farms of impending problems, letting owners focus on fixing problems early and efficiently. SmartSignal initiated Predictive Diagnostics for equipment health in the power generation industry. Its solutions protect equipment in Europe, Asia, Africa, and North America–in wind, coal, combined cycle, hydro, and nuclear power plants.

The next generation in wind power asset management

Wind operators must squeeze out every watt they can when the wind is blowing. To do so, wind projects must be reliable and maintained with minimum cost. With variable winds, high costs, and slim margins, everything has to work right to make sure that wind is attractive alternative power and a sound economic investment. So if a turbine is to work 20 years or more before retiring, it better be properly designed and maintained.

Standard procedures: Not working

Most wind turbines are maintained by a combination of traditional schedule-based preventive maintenance and threshold-based alarm systems. A problem with scheduled maintenance is that the standard six-month interval between inspections may be too long to detect an emerging problem. And fixed-threshold alerts, typically set by OEMs, activate too late to support proactive maintenance. That’s because the alerts are intended to protect equipment from catastrophic damage and can’t take into account a wide range of normal wind-turbine operating conditions and unit-to-unit manufacturing variances. As a result, typical fixed-threshold-alert systems do not detect problems until after a failure occurs.

Likewise, traditional condition-monitoring and predictive maintenance tools, such as vibration analysis, oil analysis, and thermography, are limited because of the difficulty in accessing the typical wind-turbine nacelle, the variable nature of the machine, and the time limitations and analytic capabilities of the technicians using them.

Ideally, equipment maintenance should only be performed when something needs fixing. Most preventive maintenance works on the idea of regularly inspecting or servicing equipment to address potential failures before they progress. However, given the huge variations in operating profile and environment, it’s easy to see that the regular, fixed inspection interval of traditional preventive maintenance may not catch critical emerging problems in the wind environment.

The conventional power industry, however, leads the industrial world in predicting impending equipment problems before they occur. And it is doing so using a technology directly applicable to the wind industry. In fact, several wind companies, Invenergy in Chicago for one, already use this technology to get early warnings, avoid surprises, and improve control of their operations. They reduce risk exposed by existing condition-monitoring tools and leverage SCADA data to remotely detect emerging problems by using predictive analytics.

Briefly, predictive analytics precisely identifies impending problems by detecting subtle changes in equipment operation. It finds problems earlier than OEMs’ alerting systems or other condition monitoring approaches, and well within traditional alarm limits.

A predictive analytics primer

It’s a real-time solution that works by analyzing SCADA data once every 5 to 10 minutes. Predictive analytics compares real-time data to software models of equipment when operating in good condition, and compensates for normal variations due to load and ambient conditions. Further, the method uses software models customized for individual pieces of equipment to provide the earliest possible warning of emerging problems. It readily integrates with an existing data infrastructure and it’s quick and easy to deploy, maintain, and use.

The method needs no new sensors and analysts need not review masses of SCADA data. Instead, the software analyzes data and alerts analysts only when it detects an exception, providing ample time to plan and respond. And, by using algorithms to identify pattern changes, the analysis is highly accurate.

For wind applications, the software uses models customized for each individual turbine, which compensates for fluctuations in wind speed, direction, and ambient conditions. In real time, the software compares data collected in the nacelle to the model–literally tens of thousands of data points every 5 to10 minutes across a fleet–and notifies maintenance and engineering of impending problems. Owners then focus on fixing problems early, before catastrophic damage occurs.

Take a gearbox for example. During the initial system configuration, a gearbox model would be “trained” using representative data provided from a data historian such as OSI PI (a data historian is a database for storing time-series data from instrumentation). Typically, one year of data would be used to train the model. In live operation, data from relevant sensors on the gearbox, such as for vibration and temperatures, along with operational state information, such as power output and ambient temperature, would be compared to the model. It would then provide an “estimate” of what each value should be, based on how it was trained from the historical data. If the actual value statistically differs from the model estimate, the system generates an alert. Technicians would review the sensors in alert and develop a preliminary diagnosis of the problem. A next step would typically be further on-machine investigation or use of other techniques, such as oil sampling.

Best practices

Given the high capital intensity of the wind-power business, reliable, long-term operation of the equipment is critical for generating positive returns and continued industry growth. It won’t take many major equipment failures before the long-term profitability of a farm is lost. As assets age, performing major work only when needed will be critical to maintaining economic viability.

Remote monitoring and condition-based maintenance approaches will be required to maintain financial returns because wind turbines are hard to access and don’t receive the same “walk-around” monitoring typical of industrial plants. Although wind has unique characteristics, wind turbines are just another kind of machine and successful operators will take advantage of best practices from other industries to outstrip their competition.