A New Approach to Inference from Accelerated Life Tests.

1978 ◽  
Author(s):  
Frank Proschan ◽  
Nozer D. Singpurwalla
Keyword(s):  
New Approach ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Life Tests

A New Approach To Inference From Accelerated Life Tests

1980 ◽  
Vol R-29 (2) ◽  
pp. 98-102 ◽  
Author(s):  
Frank Proschan ◽  
Nozer D. Singpurwalla
Keyword(s):  
New Approach ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Life Tests

scholarly journals Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2163
Author(s):  
Tarek Berghout ◽  
Mohamed Benbouzid ◽  
Leïla-Hayet Mouss
Keyword(s):  
Transfer Learning ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Accelerated Life Tests ◽  
Learning Path ◽  
Accelerated Life ◽  
Unseen Data ◽  
Label Information ◽  
Life Tests ◽  
Ill Posed

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.

scholarly journals Increased Surface Fatigue Lives of Spur Gears by Application of a Coating

2004 ◽  
Vol 126 (6) ◽  
pp. 1047-1054 ◽  
Author(s):  
Timothy Krantz ◽  
Clark Cooper ◽  
Dennis Townsend ◽  
Bruce Hansen
Keyword(s):  
Magnetron Sputtering ◽  
Fatigue Failure ◽  
Hard Coatings ◽  
Spur Gears ◽  
Surface Fatigue ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Life Tests ◽  
Carbon Based

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.

PLANNING FOR ACCELERATED LIFE TESTS

1999 ◽  
Vol 06 (03) ◽  
pp. 265-275 ◽  
Author(s):  
LOON-CHING TANG
Keyword(s):  
Acceleration Factor ◽  
Test Time ◽  
Initial Sample ◽  
Lower Stress ◽  
Nonlinear Programs ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Stress Levels ◽  
Life Tests ◽  
Sample Allocation

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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