The tomography technique is an effective way to quantitatively evaluate damage from reconstruction imaging in structure health monitoring (SHM). The reconstruction algorithm for the probabilistic inspection of damage (RAPID) algorithm based on the signal difference coefficient (SDC) feature is a promising approach due to its superior performance. This paper focuses on the influence of different patterns of PZT (Lead Zirconate Titanate) sensor array configurations, i.e., the circular, square, and parallel array, on reconstruction image qualities for evaluating hole and crack damage. Variable shape parameters are applied to account for the unequal damage distances of different actuator-sensor pairs. Considering the directionality scattering fields of cracks, the angular scattering pattern of the SDC values are studied by simulation. The SDC variations for different groups of sensing paths at the same actuator are applied to predict the crack orientation. An improved RAPID algorithm is proposed by defining an additional SDC value of 1 in the path along the predicted crack orientation, which is determined by the point of the actuator causing the minimal SDC variation and the center point of the initial reconstruction image of the crack. The results show that the improved RAPID algorithm is effective for the evaluation of crack damage. Reconstruction image qualities with three PZT sensor array configurations for both holes and cracks are compared. The research is significant for selecting the PZT sensor array configuration in SHM.

With the burgeoning development of electric vehicles (EVs), the efficient and economical charging mode, represented by the multi-stage constant current (MsCC) protocol, becomes increasingly popular during daily usage. In this paper, the health indicators (HIs) extracted from the MsCC charging data sets based on the charging behaviour analysis of real-world EVs are discussed, and a novel data-driven strategy for battery state of health (SOH) estimation for the MsCC charging process is proposed. First, the battery HIs are acquired from the MsCC charging datasets and delicately sifted according to the user behaviour of real-world EVs. Next, the correlation between HIs and battery SOH is analyzed through a Pearson correlation coefficients (PCC) matrix to select highly relevant HIs and avoid redundancy. Finally, the mapping relationship is constructed by the long short-term memory (LSTM) model, and its effectiveness is verified by comparison with the other three methods. To emphasize the robustness and generality of the proposed strategy, two training and testing settings and different kinds of MsCC test datasets are applied to test the model performance. The results show that the LSTM-based structure realizes about 32%, 36%, and 49% improvement in battery SOH estimation compared to the benchmark methods, and a promotion of about 18%, 27%, and 10% in prediction accuracy is observed based on another dataset.

Large-aperture lenses are widely used to improve optical resolution and enlarge field of view of a precision optical system, and reasonable mounting can result in higher optical performance. Flexure mounts are usually utilized to minimize distortion of the lens under its own weight and must be accurately assembled so as to provide uniform support loads for the lens. The supporting loads produced by flexure mounts are difficult to be identical because of various uncertainties during assembly process, such as human operation and tool resolution. These nonuniform supporting loads may generate asymmetric deformation of the lens and deteriorate its performance. It is essential to explore the influence of uncertainty supporting loads on the optical performance. A Monte Carlo analysis method is proposed to investigate the optical performance of a lens under uncertainty supporting loads at different load levels. Patran Command Language is used for repetitive calculation of finite element model with random sample of supporting loads. The optical performance responses, such as the surface peak-to-valley and root-mean-square errors and Zernike coefficients, are calculated by fitting the surface deformation data, and their stochastic properties are researched by statistical testing. Consequently, the critical variation range of uncertainty supporting loads considering assembly process can be determined based on Three Sigma theorem with specific optical performance requirement. The results can be used to assign appropriate tolerance of flexure mounts during assembly and to optimize the supporting system of a high-precision optical system.