Mathematics for Reliability Engineering

2021 ◽  
Keyword(s):  
Reliability Engineering

Reliability Engineering. Study on "Fibro-Composites". Morphology of PBT/PE Blend.

1998 ◽  
Vol 47 (12) ◽  
pp. 1270-1275 ◽  
Author(s):  
Kazuo KITAGAWA ◽  
Takeshi SEMBA ◽  
Hiroyuki HAMADA
Keyword(s):  
Reliability Engineering ◽  
Engineering Study

Signature Analysis: Statistical Models and Their Application to FA

1996 ◽  
Author(s):  
C.K. Lakshminarayan ◽  
S. Pabbisetty ◽  
O. Adams ◽  
F. Pires ◽  
M. Thomas ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Failure Analysis ◽  
Statistical Models ◽  
Essential Elements ◽  
Assessment Model ◽  
Signature Analysis ◽  
Risk Assessment Model ◽  
Sample Sizes ◽  
Reliability Engineering ◽  
Semiconductor Chips

Abstract This paper deals with the basic concepts of Signature Analysis and the application of statistical models for its implementation. It develops a scheme for computing sample sizes when the failures are random. It also introduces statistical models that comprehend correlations among failures that fail due to the same failure mechanism. The idea of correlation is important because semiconductor chips are processed in batches. Also any risk assessment model should comprehend correlations over time. The statistical models developed will provide the required sample sizes for the Failure Analysis lab to state "We are A% confident that B% of future parts will fail due to the same signature." The paper provides tables and graphs for the evaluation of such a risk assessment. The implementation of Signature Analysis will achieve the dual objective of improved customer satisfaction and reduced cycle time. This paper will also highlight it's applicability as well as the essential elements that need to be in place for it to be effective. Different examples have been illustrated of how the concept is being used by Failure Analysis Operations (FA) and Customer Quality and Reliability Engineering groups.

scholarly journals IMPROVING THE RELIABILITY OF BODY ASSEMBLY LINE EQUIPMENT

1996 ◽  
Vol 03 (01) ◽  
pp. 11-24 ◽  
Author(s):  
KAKURO AMASAKA ◽  
HIROHISA SAKAI
Keyword(s):  
Conventional Method ◽  
Assembly Line ◽  
Failure Modes ◽  
Lead Times ◽  
Reliability Engineering ◽  
Production Lines ◽  
Line Production ◽  
Equipment Reliability ◽  
New Models ◽  
Short Time

It is necessary to establish higher levels of equipment reliability in a short time, the market demands ever shorter lead times for the release of new models. Also, the demand for new-model cars is very strong immediately after their introduction. The conventional method for enhancing equipment reliability is by screening alone. However, this requires screening operations on production lines and so has been an obstacle to line production and prevented shortening of lead times. We are now able to dramatically enhance equipment reliability in a very short time by detecting failure modes and forecasting the number of occurrences using a scientific technique based on reliability engineering.

Is high-altitude mountaineering Russian roulette?

2013 ◽  
Vol 9 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Edward K. Cheng
Keyword(s):  
High Altitude ◽  
Failure Rate ◽  
Rate Model ◽  
Reliability Engineering ◽  
Significant Difference ◽  
Inference Techniques ◽  
Decreasing Failure Rate

AbstractWhether the nature of the risks associated with climbing high-altitude (8000 m) peaks is in some sense “controllable” is a longstanding debate in the mountaineering community. Well-known mountaineers David Roberts and Ed Viesturs explore this issue in their recent memoirs. Roberts views the primary risks as “objective” or uncontrollable, whereas Viesturs maintains that experience and attention to safety can make a significant difference. This study sheds light on the Roberts-Viesturs debate using a comprehensive dataset of climbing on Nepalese Himalayan peaks. To test whether the data is consistent with a constant failure rate model (Roberts) or a decreasing failure rate model (Viesturs), it draws on Total Time on Test (TTT) plots from the reliability engineering literature and applies graphical inference techniques to them.

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