A multi-stress Accelerated Life Tests method for Smart Electricity Meter based upon the Life-Stress Model

2011 ◽  
Author(s):  
Bangyan Qi ◽  
Yufeng Sun ◽  
Weiwei Hu ◽  
Xiaoxue Ding
Keyword(s):  
Life Stress ◽  
Stress Model ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Life Tests

ACCELERATED LIFE TESTS ON WEIBULL MODEL WITH SOME PARAMETER CHANGE

1995 ◽  
Vol 02 (02) ◽  
pp. 121-131
Author(s):  
WEN-TAO HUANG ◽  
HUEI-TSAN LIN
Keyword(s):  
Life Stress ◽  
New Product ◽  
Weibull Model ◽  
Type I ◽  
Parameter Change ◽  
Life Distribution ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Life Tests ◽  
Censored Sampling

Most of literature on accelerated life tests always assume that the domain of level of stress is known so that the life-stress relationship keeps unchanged as long as the level of stress applied is in this domain. However, in many practical situations the upper bound of such domain is usually unknown, especially for a new product. To consider such problem, we focus on the Weibull model and assume that, through some transformation, there is a linear relationship between the concerned parameter (involved in life distribution) and stress level. It is permitted that this relationship may change when the level of stress exceeds some bound, however its linearity is unchanged. To save time, we consider the data are either from type I or type II censored sampling. Some simulation results are also provided.

scholarly journals Competing Risks Step-Stress Model with Lagged Effect under Gompertz Distribution

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3206
Author(s):  
Yuan Ma ◽  
Wenhao Gui
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Confidence Intervals ◽  
Competing Risks ◽  
Stress Model ◽  
Gompertz Distribution ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Life Tests ◽  
Lag Period

In many survival analysis studies, the failure of a product may be attributed to one of several competing risks. In addition, if survival time is long, researchers can adopt accelerated life tests, causing devices to fail more quickly. One popular type of accelerated life tests is the step-stress test, and in this test, the stress level changes at a predetermined point time. The manner that stress levels change abruptly and increase discontinuously has been studied extensively. This paper considers a more realistic situation where the effect of stress increases cannot be achieved all at once, but with a lag time, and we propose a step-stress model consisting of two independent competing risks with a lag period in which the failure time caused by different risks at different stress levels obey Gompertz distribution, and the range of lag period is predetermined. The unknown parameters are estimated by maximum likelihood estimation and least squares estimation. For comparison, asymptotic confidence intervals and percentile bootstrap confidence intervals are constructed. By using Monte-Carlo simulations, we obtain the means and mean square errors of the maximum likelihood estimates and the least squares estimates, as well as the mean lengths and coverage rates of the two confidence intervals, which show the performance of various methods. Finally, in order to illustrate the model and proposed methods, we analyze a dataset from a solar energy experiment.

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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