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Because new products enter the market rapidly, estimating their reliability is challenging due to insufficient historical data. User survey data about similar devices (e.g., older versions of the new device) can be used as the prior information in a Bayesian analysis integrated with evidence in the form of product returns, reliability tests, and other reliability data sources to improve reliability estimation and test specification of the new product. User surveys are usually designed for purposes other than reliability estimation. Therefore, extracting reliability information from these surveys may be tricky or impossible. Even when possible, the extracted reliability information contains significant uncertainties. This dissertation introduces the critical elements of a reliability-informed user survey and offers methods for collecting them. A generic and flexible mathematical approach is then proposed. This approach uses the survey and reliability test data of similar products, for example, an older generation of the same product as prior knowledge. Then it combines them through a formal Bayesian analysis with the reliability test data to estimate the life distribution of the new product. The approach models continuous life distributions for products exposed to many damage-induced cycles. It proposes discrete life distribution models for products whose failures occur within several damaging cycles. The actual cycles for various applicable damaging stress profiles are converted into the equivalent (pseudo) cycles under a reference stress profile. When damage-induced cycles are estimated from user surveys, they may involve biases, as is the nature of most nontechnical users’ responses. This bias is minimized using an approach based on the Kullback-Leibler divergence method. The survey data and other evidence from similar products are then combined with the test data of the new product to estimate the parameters of the reliability model of the new product.
The dissertation developed approaches to design reliability test specifications for a new product with unknown failure modes. The number of samples, stress levels, and the number of cycles for the accelerated life test are determined based on the manufacturer’s requirements, including the desired warranty time, the desired reliability with some confidence level at the warranty time, and the maximum number of samples. The actual use conditions (i.e., actual stress profiles and usage cycles) are grouped using clustering techniques. The centers of clusters are then used to design frequency-accelerated or stress-accelerated reliability tests. The application of the proposed reliability estimation approach and the test specification design approach is illustrated and used to validate the proposed algorithms using the simulated datasets for a hypothetical handheld electronic device with the failure mode of cracking caused by accidental drops. The proposed approaches can adequately estimate the reliability model and design test specifications for a wide range of consumer products. These approaches require reliability data about an existing product that is similar to the new product, however.