Reliability of Machinery Using Fatigue Damage Accumulation Due to Random Vibrations

1978 ◽  
Vol 100 (4) ◽  
pp. 619-625
Author(s):  
G. D. Xistris ◽  
T. S. Sankar ◽  
G. L. Ostiguy

A new approach to machinery maintenance using fatigue damage accumulation theory is presented. The vibration generated by industrial equipment is related to the stress history experienced by the internal machine elements at the corresponding measurement points assuming a linear elastic and isotropic behavior. The resultant stress history is modeled as a piecewise stationary, Gaussian wide band process. Employing Miner’s linear damage hypothesis in conjunction with available constant amplitude fatigue data, expressions for the expected accumulated fatigue damage and its variance are developed. A machinery maintenance program based on the accumulated damage parameters calculated directly from the properties of the exhibited vibration history is proposed. The main advantage of this method is that it provides equipment reliability in terms of known and measurable system properties.

2004 ◽  
Vol 46 (6) ◽  
pp. 309-313
Author(s):  
Yutaka Iino ◽  
Hideo Yano

2013 ◽  
Vol 81 (4) ◽  
Author(s):  
Son Hai Nguyen ◽  
Mike Falco ◽  
Ming Liu ◽  
David Chelidze

Estimating and tracking crack growth dynamics is essential for fatigue failure prediction. A new experimental system—coupling structural and crack growth dynamics—was used to show fatigue damage accumulation is different under chaotic (i.e., deterministic) and stochastic (i.e., random) loading, even when both excitations possess the same spectral and statistical signatures. Furthermore, the conventional rain-flow counting method considerably overestimates damage in case of chaotic forcing. Important nonlinear loading characteristics, which can explain the observed discrepancies, are identified and suggested to be included as loading parameters in new macroscopic fatigue models.


1984 ◽  
Vol 110 (11) ◽  
pp. 2585-2601 ◽  
Author(s):  
Loren D. Lutes ◽  
Miguel Corazao ◽  
Sau‐lon James Hu ◽  
James Zimmerman

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