Search

The accelerated Degradation testing (ADT) experiments are important technical methods in reliability studies. Different type of accelerating degradation models has developed with the time and can be used in different types of situations. However, it has become necessary for the manager to test how many numbers of unit should be tested at a particular stress level so that the cost of testing is less. Accelerated Degradation testing (ADT) is preferred to be used in mechanized industries to obtain the required information about the reliability of product components and materials in a short period of time. Accelerated test conditions involve higher than usual pressure, temperature, voltage, vibration or any other combination of them. Data collected at such accelerated conditions are extrapolated through a physically suitable statistical model to estimate the lifetime distribution at design condition stress the life data collected from the high stresses the need to be extrapolated to estimate the life distribution under the normal-use condition. A special class of the ADT is the step-stress testing which regularly increases the stress levels at some pre-fixed time points until the test unit fails. Such experiments allow the experimenter to run the test units at higher-than-usual stress conditions in order to secure failures more quickly. The Inverse Gaussian process is flexible in incorporating random effects and explanatory variables. The different types of models based on IG process are random drift model, random volatility model and random drift-volatility model. In this paper we have considered random drift model for the study on stochastic degradation models for simple step-stress model using inverse Gaussian process observed in degradation problems. [ABSTRACT FROM AUTHOR]

The reliability study of such incredibly reliable items is inappropriate for the use of failure time data analysis and testing methodologies. More trustworthy information can be obtained from degradation data than from standard censored failure-time data, especially in cases where few or no failures are anticipated. The market for lighting has given a lot of attention to high-power white light emitting diodes (HPWLEDs). But as one of the more dependable electronic goods, it may not be expected to fail in either a traditional or even an accelerated life test. DDDM, or data-driven degradation methodology, is used in this research. Using data on lumen maintenance gathered from the IES LM-80-08 lumen maintenance test standard and based on the general degradation path model, the dependability of HPWLED was predicted. Testing such devices in typical working situations, and occasionally even under worse conditions, is difficult enough without trying to collect an adequate amount of time-to-failure data. Modern items are made with superb quality and high reliability in mind. Some safety-critical parts and systems are even made to last for an incredibly long time in order to prevent the disastrous effects of probable breakdowns. A cumulative damage model based on stochastic degradation processes has been developed in this paper. A suitable numerical representation is used to support the analytical findings. As a result, the degradation analysis approach has been developed to address dependability modeling issues using data on product degradation gleaned from historical records or degradation testing. [ABSTRACT FROM AUTHOR]