Variables acceptance reliability sampling plan for items subject to inverse Gaussian degradation process.

Area of Science:

• Quality Control
• Reliability Engineering
• Statistical Process Control

Background:

• Traditional acceptance reliability sampling plans often rely on item lifetimes or failure counts.
• These methods may not fully capture the behavior of items subject to degradation phenomena.
• Degradation levels offer a valuable alternative decision statistic.

Purpose of the Study:

• To develop a novel variables acceptance sampling plan utilizing degradation process information.
• To assume the degradation process follows the inverse Gaussian process for modeling.
• To enhance the reliability performance of accepted lots.

Main Methods:

• Development of a variables acceptance sampling plan based on degradation data.
• Modeling the degradation process using the inverse Gaussian distribution.
• Comparison of the proposed plan with conventional life test-based sampling plans.

Main Results:

• The proposed degradation-based sampling plan improves item reliability conditional on acceptance.
• Items tested with the new plan exhibit stochastically larger lifetimes post-test.
• The degradation-based plan shows advantages over traditional life test methods.

Conclusions:

• Degradation-based sampling plans offer a superior alternative for assessing product reliability.
• Utilizing degradation data enhances the predictive power of acceptance sampling.
• The inverse Gaussian process provides a suitable model for degradation phenomena in this context.