Condition
monitoring can save you a lot of money. Just ask the engineering staff
at Panhandle Energy, a Houston-based pipeline company. New monitoring technology led staffers there to find a previously undiscovered crack in a compressor impeller only two weeks into a trial period that management had negotiated with SmartSignal Corp., of Lisle, Ill., and Invensys Process Systems, of Foxboro, Mass.

Had the crack been allowed to propagate, the engineers say that the resulting failure would have been catastrophic. They estimate that the cost would have included both an $800,000 repair bill and lost sales.

Another reason for the surge in interest is the developments that have been occurring in the technology. Today’s sensors, for example, often contain a measure of intelligence that not only can enrich the data that they report but also often make rudimentary decisions. Software companies such as Avantis have built on this intelligence by devising ways to sift through and make use of the massive amounts of data flowing into already overloaded information systems.

The latest generation of condition-monitoring software draws what Cooper calls context from the data. “Simple vibration monitoring, for example, doesn’t really tell you much until you put the measurements in the context of [other parameters like] RPM (revolutions per minute), throughput,
temperature and maintenance life,” he says. “Putting the data into context turns condition monitoring into condition management.”

The key for doing this is the modeling software that software companies have developed. Some, for example, make sense of stress-wave, infrared, acoustical, and other data, pre-conditioning the data for the higher-level knowledge management systems that are available today.

SmartSignal, for example, applies empirically based modeling software that its engineers have developed for various kinds of equipment. Using what the software developer calls similarity-based modeling, the software not only customizes the models to each of piece of equipment, but also applies sophisticated pattern-recognition algorithms to monitor how its operating variables change in dynamic conditions.

“As anyone in operations can tell you, each piece of equipment has its own personality,” says John Kerastas, a spokesman for the company. “One unit might run a little hot, but another might run a little cold, even though the two are the same model and make.” One explanation is that different people probably build them on different days. Another is that differences in installation, maintenance and operation also cause slight differences in performance.

Early warning

By weighing the relations in a much larger set of data than is typical, the models offer what Kerastas calls a more holistic and “personal” look at each piece of equipment. “It provides early warnings for small deviations from what is normal for that particular unit,” he says. Even though the operating parameter in question might fall within the normal operation range, the model will flag deviations that are abnormal given other readings.

Kerastas claims that this holistic approach saved Detroit Edison about  $1 million. At one of the energy company’s plants, the software monitoring the primary air fan warned an analyst that the motor was running at about 90 degrees Celsius.  The temperature was 20 degrees below the alarm level, but it was well above what it should have been, given the input from the other sensors.

During the next scheduled shutdown for maintenance, the technicians removed and cleaned the motor, which was very dirty. Had the fan motor temperature­—high for that time of year—not been detected, the equipment could have failed, causing a forced outage and a half-unit derate of 300 megawatts (MW) for as long as five days.

Tailor-made models

Another example of modeling software is ROMeo, optimization and modeling software from Invensys’ SimSci-Esscor business that is used by oil refineries and ...