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Just-in-time Maintenance

There is nothing new about sticking monitors into machines to notice when some variable wanders outside its normal range. The temperature gauge that’s been in automobiles for decades is one simple example-the mechanical equivalent of having patients walk around with thermometers in their mouths.

But the remote monitoring emerging today is of a different magnitude altogether. Think of strapping several dozen monitoring tools onto the patient-blood pressure and heart rate, electrocardiogram, brain wave sensor and more, with the doctor following the data remotely, analyzing it with reference to each patient’s medical history, and then offering regular diagnoses that might include advice on when to pop an aspirin before a headache even occurs. That is the sort of continuous checkup that is now becoming practical for sophisticated machinery.

The value of such supercharged monitoring is obvious to anyone who has ever missed a meeting because a flight was canceled or lost electricity because some part in a utility substation broke. Catching problems early means less expensive repairs, less downtime and less disruption of service and schedules. At the same time, knowing what is going on inside an engine or other piece of equipment can provide the confidence to hold off on a replacement or repair until it is necessary. The ultimate goal is just-in-time maintenance-knowing exactly what repairs to make and when. “There are two kinds of mistakes-replacing too soon, and replacing too late,” Hahn says. “We want to minimize both.”

Many companies have been designing and building equipment so that its health can be monitored during operation. Asea Brown Boveri, the giant European industrial conglomerate, puts remote-diagnostics capability into the propulsion systems it makes for cruise ships and other large vessels; an onboard computer collects operating data and forwards it via satellite to Helsinki for analysis. Turbine Technology Services operates a facility in Orlando, FL, that remotely monitors data from the turbines used in power plants. Other firms monitor the performance of computer equipment such as servers and routers, while the heating, ventilation and air-conditioning systems in many large buildings are equipped with devices that allow engineers to spot problems by observing such variables as airflow and temperature.

But perhaps no company has done more work in these areas than General Electric. A diversified conglomerate with some two dozen divisions, GE manufactures complex industrial equipment-power turbines and ship propulsion systems, in addition to aircraft engines and locomotives-alongside consumer items such as appliances and lighting products. GE is at the forefront of remote monitoring and diagnostics in a variety of fields, says Nick Heyman, an analyst at Prudential Securities who follows the company. Heyman points specifically to GE’s leadership in monitoring of aircraft engines; it was a GE monitoring station outside Cincinnati that spotted the delamination in the engine of the Auckland-bound Airbus. Other GE facilities keep an eye on locomotives, merchant-ship engines, gas turbines and medical imaging devices. The company’s applied-statistics program, founded in 1975 at the corporate R&D center in Schenectady, has grown to be one of the world’s most respected groups in that discipline, and it has provided the ammunition for several GE divisions to develop remote monitoring and diagnostics capabilities. The developments at GE therefore offer a case study of how remote monitoring and diagnostics is transforming the way that complex technological systems are kept in top working condition.

Figuring out from afar what’s wrong with a piece of complex machinery entails two separate but related functions. One is to detect anomalies as soon as they appear; that’s what happened with the Airbus. Complementing that is the forecasting of problems before they even arise. Both capabilities have improved tremendously over the past few years-thanks, Hahn says, to a convergence of three very different advances. One is the development of smaller, lighter-weight sensors. Second is the tremendous growth in computing power. Third-and perhaps most important-is the emergence of new statistical techniques that allow researchers to distill useful information from mountains of data.

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