A new generation of sensors is poised to revolutionize predictive maintenance

Dec. 26, 2007
New software and emerging technologies are simplifying condition monitoring and streamlining the process of predictive maintenance.

Success in a predictive maintenance program might be constrained if the technician must rely on indirect or imprecise measurements, if the batteries in measuring equipment fail, or if data communications are limited. Gradually such constraints are being overcome. New software and emerging technologies are simplifying condition monitoring and streamlining the process of predictive maintenance.

Miniature sensors cozy up to bearings: Purdue University researchers, working with the U.S. Air Force, are using microelectromechanical systems (MEMS) technology to detect when critical jet engine bearings are close to failing. The miniaturized wireless sensors directly monitor engine bearing temperature. Data generated indicates whether the bearing is about to fail and predicts how long it will last before failure.

The sensors have been shown to detect impending temperature-induced bearing failure significantly earlier than conventional sensors, which indirectly monitor bearings using the temperature of engine oil. The MEMS-based sensors operate without batteries -- they’re powered through inductive coupling — and temperature data is wirelessly transmitted. To support military applications, the devices are being designed to withstand temperatures as high as 300°C (572°F).

MEMS machines combine electronic and mechanical components on a microscopic scale. “The MEMS technology is critical because it needs to be small enough so it doesn't interfere with the performance of the bearing itself,’ says Farshid Sadeghi, a Purdue professor of mechanical engineering. “And the other issue is that it needs to be able to withstand extreme heat.” Although the initial target is military aircraft, virtually anything with an engine could benefit from the new sensor technology. It has potential for applications in harsh manufacturing environments as well as transportation, distribution and warehouse fleet management.

Self-powered sensors are battery-free: Battery maintenance can be costly and difficult when condition-monitoring sensors are installed in confined spaces or at remote locations. Clarkson University researchers have developed a bridge monitoring sensor technology that generates its own energy from the vibrations of passing vehicles. The hermetically-sealed wireless sensor eliminates the need for batteries and can conceivably remain on a bridge for decades without requiring maintenance.

New York State Route 11 Bridge was the test platform. An electromagnetic generator installed on a steel bridge girder harvests energy at the bridge. When vehicles pass, the structure vibrates and the generator produces electrical energy to power the wireless sensor. Typically, each bridge requires several sensors to monitor structural integrity and other variables, and report changes that could indicate a potential failure. Replacing battery-powered sensors with self-powered sensors can eliminate substantial battery and maintenance labor costs and the risk of communication gaps caused by dead batteries.

In addition to bridges, the researchers are applying the energy-harvesting technology to power sensors in passenger cars. The concept also can be leveraged in the industrial sector for continuous monitoring and proactive maintenance of critical vibrating applications, including lift trucks and rotating machinery such as motors, pumps, fans and turbines.

KCF Technologies, for instance, is developing vibration-energy-harvesting devices to power wireless sensor nodes for use in industrial production lines, power generation systems, vehicles and buildings. The company anticipates the technology will expand the use of wireless sensors, generating benefits such as reduced industrial pollution and energy consumption in addition to eliminating battery replacement costs.

Data aggregation and analysis

Equipment condition data generated by sensors and other sources is most useful if it can be easily aggregated, analyzed and logged. The InFusion Condition Manager from Invensys was recently upgraded to display equipment condition and maintenance information on plant process control and engineering HMI workstations, rather than solely on the Invensys Avantis.PRO enterprise asset management system. InFusion v. 2.2 also can feed data to a variety of plant historian packages, making the data and actions available to other plant and enterprise systems.

The system collects and analyzes real-time diagnostics from any plant production asset – for example sensors, actuators, motors, dryers and compressors. It captures data originating from a array of sources, including intelligent instrumentation, fluid and vibration analysis, advanced process control and loop tuning software. It then triggers the necessary maintenance, operations or engineering actions based on rules, conditions and customer-defined algorithms and models.

Web Services technology allows InFusion Condition Manager to communicate with enterprise systems such as ERP, EAM and MES. Microsoft .NET technology is leveraged to provide the asset and business intelligence information.

E-mail Contributing Editor Sheila Kennedy, managing director of Additive Communications, at [email protected].

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