In this example, the maintenance staff of a train yard must detect defective wheels on a train. The current method of detection consists of a railroad worker striking a wheel with a hammer and listening for a different resonance that identifies a flaw. Automated surveillance must replace manual testing, because manual surveillance is too slow, too prone to error, and too crude to detect subtle defects. An automated solution also adds the power of dynamic testing, because the train wheels can be in service during the test, instead of standing still.

The automated solution to detect potentially defective train wheels needs to have the following characteristics:

  • Detect even subtle signs of defects quickly and accurately.
  • Gather data when a train travels during a normal trip.
  • Collect and analyze data in real time to simplify programming and to increase speed and accuracy of results.

The Train Wheel PtByPt VI offers a solution for detecting defective train wheels.

Note This example focuses on implementing a point-by-point analysis program in LabVIEW. The issues of ideal sampling periods and approaches to signal conditioning are beyond the scope of this example.