On the sensor front, much of the progress can be traced to the same sorts of processing advances that have shrunk computer chips so remarkably over the past 20 years. Another factor is also at play, says Larry Abernathy, head of the diagnostics engineering team at GE Engine Services in Evendale, OH. That is the trend to replace mechanical controls with electronic ones that make it much easier to gather data. For example, today’s “fly-by-wire” jets are controlled by dozens of computers that know precisely what is going on in the various systems that they command.But getting the relevant data is rarely the whole battle. Knowing what to make of it is often the difficult part, and it is here that the other two advances-in computing and statistical analysis-come into play. Consider, for example, the challenge of figuring out what part or system might need attention on a 200,000-kilogram locomotive. Although trains may be commonly perceived as relics from the 19th century, modern locomotives are relentlessly high tech. “Think of them as rolling power plants,” says Joe Cermak, leader of the Remote Maintenance and Diagnostics Center of Excellence at GE’s Transportation Systems division in Erie, PA. The AC6000, GE’s newest and most powerful locomotive, boasts a 6,000-horsepower, turbocharged, fuel-injected diesel engine that spins an alternator to generate some four megawatts of electric power. That’s enough electricity to run 3,000 homes, but here it drives six independent traction engines that give the locomotive enough oomph to pull 100 cars at up to 120 kilometers per hour.
Two dozen microprocessors control the locomotive, allowing the entire operation to be directed by a single engineer sitting before a computer console in the locomotive’s cab. Sensors monitor nearly every variable of interest, from the locomotive’s speed and horsepower output to the voltages, torques and speeds of the individual traction motors to the battery voltage and current. All of this information is collected at GE’s service center in Erie, PA, where about 50 technicians and engineers monitor nearly 300 locomotives belonging to a major U.S. railroad.
Much of this information was available in some form 20 years ago. The difference now is its accessibility for analytical purposes. “We used to have data stored in file cabinets,” Hahn remembers. To make sense of the data required manually entering it into a computer. Even then, the computers were not fast enough to sift through more than a fraction of the information in a reasonable amount of time. As a result, it was very difficult to spot any but the most obvious problems.
Now that has changed. As Jason Dean, a systems engineer with GE’s Remote Monitoring and Diagnostics group, explains, even something as seemingly simple as spotting a clogged fuel filter was hit-or-miss. A stopped-up filter can cut a locomotive’s horsepower by 20 to 30 percent. That’s a deficit small enough to go unnoticed when the load is light, but one that can slow the train considerably when it is hauling many cars or heading up an incline. And one slow train can back up the entire rail network.
Diagnosing this malfunction is not straightforward. The major indication that a filter is plugged is increased fuel usage, Dean says. But other factors-such as air temperature, the horsepower being produced and the train’s speed-can cause fuel usage to vary by as much as 20 percent, and so the engineer who sees a drop in efficiency cannot know from that alone what’s at the root of the problem. The solution, in theory, is simple: collect historical data on each locomotive’s operations and apply statistical analysis to create a model of the train’s performance. If fuel usage jumps significantly above what the model predicts for a particular set of conditions, the fuel filter should be replaced.