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Operating and non-operating temperatures as well as power and duty cycles affect electronic component reliability. The ability to predict the reliability of a printed circuit board (PCB) is important during the early part of the design process. This paper explores the effects of using both single-value assumptions and probability distributions for environmental and operating stresses in a standards-based reliability model of a PCB by comparing the predictions with the measured field data.First, failure and shipping data for a power interface circuit board from a motor drive that has been in production for over two years was obtained. An adjustment factor for the data was developed to account for non-operational storage time. A Weibull analysis was performed on the adjusted data set and L1, L10, and MTBF reliability metrics were computed. Next, a reliability model was constructed using the bill of materials, circuit schematic, and RIAC 217Plus reliability prediction standard.The environmental and operating profiles were then defined using 217Plus defaults, worst-case, and probability distributions. Deterministic predictions were made using the 217Plus default and worst-case profiles and predicted values were compared with the field data. Stochastic predictions were made using Monte Carlo simulation techniques and also compared to field data. Predictions using the Monte Carlo technique were found to have a lowest maximum error of 22%. The discrepancy between the predictions and field for L1 and L10 reliability were reduced by using a Weibull shape parameter that matched the field data instead of the default value of one. Finally, a correction factor was developed to further improve the predictions.

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