Sensor placement optimization (SPO) is usually applied during the structural health monitoring sensor system design process to collect effective data. However, the failure of a sensor may significantly affect the expected performance of the entire system. Therefore, it is necessary to study the optimal sensor placement considering the possibility of sensor failure. In this article, the research focusses on an SPO giving a fail-safe sensor distribution, whose sub-distributions still have good performance. The performance of the fail-safe sensor distribution with multiple sensors placed in the same position will also be studied. The adopted data sets include the mode shapes and corresponding labels of structural states from a series of tests on a glider wing. A genetic algorithm is used to search for sensor deployments, and the partial results are validated by an exhaustive search. Two types of optimization objectives are investigated, one for modal identification and the other for damage identification. The results show that the proposed fail-safe sensor optimization method is beneficial for balancing the system performance before and after sensor failure.

Product yield reflects the potential product quality and reliability, which means thathigh yield corresponds to good quality and high reliability. Yet consumers usuallycouldn’t know the actual yield of the products they purchase. Generally, the products thatconsumers get from suppliers are all eligible. Since the quality characteristic of theeligible products is covered by the specifications, then the observations of qualitycharacteristic follow truncated normal distribution. In the light of maximum likelihoodestimation, this paper proposes an algorithm for calculating the parameters of fullGaussian distribution before truncation based on truncated data and estimating productyield. The confidence interval of the yield result is derived, and the effect of samplesize on the precision of the calculation result is also analyzed. Finally, theeffectiveness of this algorithm is verified by an actual instance.

We revisit the stochastic model of Alai et al. (2009) for the Bornhuetter-Ferguson claims reserving method, Bornhuetter & Ferguson (1972). We derive an estimator of its conditional mean square error of prediction (MSEP) using an approach that is based on generalized linear models and maximum likelihood estimators for the model parameters. This approach leads to simple formulas, which can easily be implemented in a spreadsheet.