To meet increasing competition, get products to market in the shortest possible time, and satisfy heightened customer expectations, products must be made more robust and fewer failures must be observed in a short development period. In this circumstance, assessing product reliability based on degradation data at high stress levels becomes necessary. This assessment is accomplished through accelerated degradation tests (ADT). These tests involve over stress testing in which instead of life product performance is measured as it degrades over time. Due to the role these tests play in determining proper reliability estimates for the product, it is necessary to scientifically design these test plans so as to save time and expense and provide more accurate estimates of reliability for a given number of test units and test time. In ADTs, several decision variables such as inspection frequency,the sample size, and the termination time at each stress level are important. In this research, an optimal plan is developed for the design of accelerated degradation test with a reciprocal Weibull degradation data using the mean time to failure (MTTF) as the minimizing criteria. A non linear integer programming problem is developed under the constraint that the total experimental cost does not exceed a pre-determined budget. The optimal combination of sample size, inspection frequency and the termination time at each stress level is found. A case example based on Light Emitting Diode (LED) example is used to illustrate the proposed method. Sensitivity analyses on the cost parameters and the parameters of the underlying probability distribution are performed to assess the robustness of the proposed method.

The lighting retrofit method is adopted as one of the solutions to reduce lighting energy consumption and improve lighting quality in existing buildings. Lighting controls and energy-efficient light sources are used to achieve the goals of the lighting retrofit. Nowadays, Light-Emitting Diodes (LEDs) are replacing traditional lighting technology owing to their high efficiency and longevity. One of the advantages of LEDs is the controllability function, which allows users to set the light level according to their preferences. This saves more energy and satisfies users’ lighting needs. However, over time, the performance of lighting retrofit projects deteriorates subject to failure of the retrofitted lights. Therefore, to maintain the performance of lighting retrofit projects, maintenance must be planned and performed. The impacts of the users’ lighting level requirements on LEDs’ life characteristics and lighting system performance are investigated by using lighting controls. Light and occupancy sensors adjust artificial light to the light level required by users and detect the presence of users in the zones, respectively. Light sensors measure the average illuminance in the zones. The measured illuminance is compared to the users’ set illuminance; if the measured illuminance is higher than the users’ set illuminance, lamps are dimmed to meet users’ lighting preference, when the measured illuminance is less than the users’ set illuminance, lamps in the zone are replaced by new ones. The dimming level in each zone at each sampling interval is used to estimate the operating junction temperature, thereafter the degradation rate and luminous flux are calculated. Light levels at workspace are modelled using the lumen method. This model helps to quantify energy savings and predict when lamps will fail to deliver the required light levels. In existing studies, users’ lighting level requirements are neglected when investigating the lifetime of the lighting system; however, users’ profile and driving schemes affect the operating conditions of a lighting system. From the simulation results, it is noted that lumen output degradation increases when the user’s set illuminance is above the illuminance required under normal operating conditions and decreases when the user’s set illuminance is below the illuminance required under normal operating conditions. Increased lumen output degradation shortens the lifetime of LEDs and reduces energy savings, while decreased lumen output degradation extends the lifetime and increases energy savings. Generally, lighting retrofit projects contain a large lighting population; investigating when each lamp will fail can be time-consuming and costly. In this research, a mathematical model is formulated to model LEDs’ failure by analysing the statistical properties of the lumen degradation rates. Based on the statistical properties of the degradation rates, the cumulative probability of failure distribution and the survival function are modelled. The formulated survival function is incorporated into the lighting maintenance optimization problem to balance energy savings and maintenance costs. A case study carried out shows that, in 10 years, the optimal lighting maintenance plan would save up to 59% of lighting energy consumption with acceptable maintenance costs. It is found that the proposed maintenance plan is more cost-effective than full maintenance. It is concluded that lumen degradation failure should be considered when investigating the performance of lighting retrofit projects, as this may not only affect energy savings but also reduce the level of illumination, which can cause visual discomfort. The initial investment costs of LEDs are still a barrier to the implementation of LED lighting systems in residential buildings. Energy-efficiency projects often face hurdles to access capital investments because decision-makers and funders do not have enough information about operational savings the project can provide and specific financial requirements applied to efficiency investment. In this research, an optimization model is formulated to give decision-makers and funders detailed information about the performance and operational savings that a LED lighting retrofit project can offer and its economic viability. The lumen degradation failure model developed is used to monitor and estimate the energy savings, and the optimal maintenance plan is scheduled to replace failed lamps. In the existing studies, the economic analysis of the lighting retrofit projects is assessed based on lighting population decay due to burnout failure while in this research economic analysis is assessed by considering the lumen degradation failure. The case study results show that the substitution of halogen light bulbs with LED light bulbs could save up to 291.4 GWh of energy consumption, and reduce 273:92 103 tons of CO2 emissions over 10-year period. The optimization model formulated is effective to help the decision-makers and funders to quantify the savings and assess the economic viability of the LED lighting retroïnˇA˛t project. This optimization model can help the decision-makers and funders to make an informed decision. / Thesis (PhD (Electrical Engineering))--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / PhD (Electrical Engineering) / Unrestricted