Application of the Physics of Failure Approach to Reliability Prediction for Design Guidance of Commercial Appliance Components

Author(s):  
Mark W. Steiner ◽  
Ken Zagray ◽  
Surya Ganti ◽  
Omar Hasan

Abstract The physics of failure approach to reliability prediction considers fundamental failure mechanisms such as fatigue, wear, corrosion and creep that can shorten the useful life of a product. The work described here uses this approach for commercial appliance components and presents three examples. This paper also outlines a methodology for developing rate modification factors. These rate modification factors can be used for evaluating design changes, early estimation of failure rates, planning accelerated life tests and assessing risks in component application alternatives. Although the physics of failure approach is not new, the application to relatively inexpensive light service commercially based components extends the application beyond the realm of military and heavy industrial equipment. Simplifying assumptions and the use of manufacturer’s material properties are utilized to create a “cookbook” approach for development of design charts useful for improving product reliability. The principal objective of the work presented here is to demonstrate the development of the design charts and their application to commercially based products.

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2163
Author(s):  
Tarek Berghout ◽  
Mohamed Benbouzid ◽  
Leïla-Hayet Mouss

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.


2004 ◽  
Vol 126 (6) ◽  
pp. 1047-1054 ◽  
Author(s):  
Timothy Krantz ◽  
Clark Cooper ◽  
Dennis Townsend ◽  
Bruce Hansen

Hard coatings have potential for increasing gear surface fatigue lives. Experiments were conducted using gears both with and without a metal-containing, carbon-based coating. The gears were case-carburized AISI 9310 steel spur gears. Some gears were provided with the coating by magnetron sputtering. Lives were evaluated by accelerated life tests. For uncoated gears, all of 15 tests resulted in fatigue failure before completing 275 million revolutions. For coated gears, 11 of the 14 tests were suspended with no fatigue failure after 275 million revolutions. The improved life owing to the coating, approximately a sixfold increase, was a statistically significant result.


Author(s):  
LOON-CHING TANG

We present two alternative perspectives to the current way of planning for constant-stress accelerated life tests (CSALTs) and step-stress ALT (SSALT). In 3-stress CSALT, we consider test plans that not only optimize the stress levels but also optimize the sample allocation. The resulting allocations also limit the chances of inconsistency when data are plotted on a probability plot. For SSALT, we consider test plans that not only optimize both stress levels and holding times, but also achieve a target acceleration factor that meets the test time constraint with the desirable fraction of failure. The results for both problems suggest that the statistically optimal way to increase acceleration factor in an ALT is to increase lower stress levels and; in the case of CSALT, to decrease their initial sample allocations; in the case of SSALT, to reduce their initial hold times. Both problems are formulated as constrained nonlinear programs.


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