Abstract

Torsional vibration signature analysis has shown the potential to detect shaft cracks during normal operation of rotating equipment. The method tracks characteristic changes in the natural torsional vibration frequencies that are associated with shaft crack propagation. The method is generally applicable to many types of rotating equipment. A rotor test bed was developed to investigate shaft cracking detection techniques to investigate the capabilities and limitations under realistic yet controlled conditions. The test bed has a sacrificial shaft mounted between a 35 hp drive and 70 hp load motor. A full suite of torsional and translational vibration sensors are deployed on the test bed. A lateral load can be applied to the rotating shaft via a hydraulic cylinder. A series of seeded fault tests were performed by growing a fatigue crack in the shaft. The crack is grown by rotating the shaft under lateral load to produce full reversal bending stresses. The vibration diagnostic features are acquired and trended as the crack grows. The testing objective is to develop the correlation and sensitivity between the shaft health (i.e., crack size) and the acquired diagnostic features. During baseline testing, before initiation of the fatigue crack, the torsional vibration diagnostic features were observed to be unstable. Further examination showed the torsional vibration diagnostic features were being affected by the laboratory temperature. The paper describes outcomes and observations related to the environmental effects on the shaft health diagnostics in controlled testing. The lab results are discussed in relation to what may be expected and the effect on the torsional vibration diagnostic features in an industrial setting. The controlled laboratory testing results and analysis assists in the interpretation of torsional vibration features for structural health diagnostics.KeywordsFatigue CrackHealth MonitoringTorsional VibrationTest StandLoad PlateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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