Engineers who design equipment with motors, transmissions, and bearings know the importance of detecting the onset of a failure and rectifying the problem quickly. Bearing failure can cause catastrophic damage to equipment. Imagine the results of a bearing failure on a railway car carrying your family.
A company called Track IQ has come up with a way to monitor acoustic signals from the bearings that support the weight of a railroad car on the outer axle of a wheelset. A wheelset comprises an axle that connects two flanged metal wheels that run on a track. The technique and system are called RailBAM (Railway Bearing Acoustic Monitor). According to Siemens, which distributes the RailBAM equipment, the monitor can detect damage to the wheelset bearings in trains sooner than other techniques, so railway operators can improve the reliability of rail transport and reduce maintenance costs.
The RailBAM system monitors the sounds of wheelset bearings as trains pass a set of sensors. It now monitors trains traveling at up to 160km/hour, though Track IQ plans to adapt the system to work with faster trains. Normally, railway operators would replace a wheelset every 1.2 million kilometers (745,000 miles). Or they would use a hot box to detect overheated bearing cases, which indicate a bearing failure. Now, though, a RailBAM system lets railroad shop workers replace wheelsets whenever the acoustic measurement data reveals the first signs of trouble.
According to Track IQ, an array of acoustic sensors improves spatial discrimination or directionality. Software uses geometric wheel measurements and acoustic characteristics to reduce crosstalk in the acoustic signals. As a result, the influence of a large fault on one axle does not diminish the reading from a small fault on an adjacent axle.
In Southampton, England, RailBAM equipment has monitored 45 trains with 9,000 wheelsets over a two-year span. As a result of this test, maintenance intervals for powered and nonpowered wheelsets increased by 10 percent and 50 percent, respectively.
Though preventive maintenance regimens often use accelerometers to detect problems in stationary rotating equipment, perhaps acoustic signatures could supplement their measurements in mechatronic systems. In a railway, all wheels have similar characteristics, and they run a fixed distance from acoustic sensors. These conditions reduce the unknowns in measurement algorithms. The variability of mechatronic devices might present a challenge to something similar to the Track IQ system, but it might still deserve a look -- or a listen.
Note: For some time, railroad cars in the US have used an automatic equipment identification system that relies on heavy-duty radio frequency tags attached to both sides of a car. A tag reader system associated with a RailBAM system would let the equipment identify the specific rail car with an out-of-spec bearing or bearings.