Reliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases might lead to false conclusions. Survival approach is particularly designed to handle censored data for analysing the expected duration of time until one event occurs, which is rail failure in this paper. This paper proposes a deep Bayesian survival approach named BNN-Surv to properly handle censored data for rail useful lifetime modelling. The proposed BNN-Surv model applies the deep neural network in the survival approach to capture the non-linear relationship between covariates and rail useful lifetime. To consider and quantify uncertainty in the model, Monte Carlo dropout, regarded as the approximate Bayesian inference, is incorporated into the deep neural network to provide the confidence interval of the estimated lifetime. The proposed approach is implemented on a four-year dataset including track geometry monitoring data, track characteristics data, various types of defect data, and maintenance and replacement (M&R) data collected from a section of railway tracks in Australia. Through extensive evaluation, including Concordance index (C-index) and root mean square error (RMSE) for evaluating model performance, as well as a proposed CW-index for evaluating uncertainty estimations, the effectiveness of the proposed approach is confirmed. The results show that, compared with other commonly used models, the proposed approach can achieve the best concordance index (C-index) of 0.80, and the estimated rail useful lifetimes are closer to real lifetimes. In addition, the proposed approach can provide the confidence interval of the estimated lifetime, with a correct coverage of 81% of the actual lifetime when the confidence interval is 1.38, which is more useful than point estimates in decision-making and maintenance planning of railroad systems.

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Timely detection and identification of rail breaks are crucial for safety and reliability of railway networks. This paper proposes a new deep learning-based approach using the daily monitoring data from in-service trains. A time-series generative adversarial network (TimeGAN) is employed to mitigate the problem of data imbalance and preserve the temporal dynamics for generating synthetic rail breaks. A feature-level attention-based bidirectional recurrent neural networks (AM-BRNN) is proposed to enhance feature extraction and capture two-direction dependencies in sequential data for accurate prediction. The proposed approach is implemented on a three-year dataset collected from a section of railroads (up to 350 km) in Australia. A real-life validation is carried out to evaluate the prediction performance of the proposed model, where historical data is used to train the model and future ’unseen’ rail breaks along the whole track section are used for testing. The results show that the model can successfully predict 9 out of 11 rail breaks three months ahead of time with a false prediction of non-break of 8.2%. Predicting rail breaks three months ahead of time will provide railroads enough time for maintenance planning. Given the prediction results, SHAP method is employed to perform cause analysis for individual rail break. The results of cause analysis can assist railroads to plan appropriate maintenance to prevent rail breaks.@en

In view of its band gap of 2.2 eV and its stability, methylammonium lead bromide (MAPbBr₃) is a possible candidate to serve as a light absorber in a subcell of a multijunction solar cell. Using complementary temperature-dependent time-resolved microwave conductance (TRMC) and photoluminescence (TRPL) measurements, we demonstrate that the exciton yield increases with lower temperature at the expense of the charge carrier generation yield. The low-energy emission at around 580 nm in the cubic phase and the second broad emission peak at 622 nm in the orthorhombic phase originate from radiative recombination of charges trapped in defects with mobile countercharges. We present a kinetic model describing both the decay in conductance as well as the slow ingrowth of the TRPL. Knowledge of defect states at the surface of various crystal phases is of interest to reach higher open-circuit voltages in MAPbBr₃-based cells.

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Organic-inorganic halide perovskites (OIHPs) bring an unprecedented opportunity for radiation detection with their defecttolerance nature, large mobility-lifetime product, and simple crystal growth from solution. Here we report a dopant compensation in alloyed OIHP single crystals to overcome limitations of device noise and charge collection, enabling -ray spectrum collection at room temperature. CH₃NH₃PbBr₃ and CH₃NH₃PbCl₃ are found to be p-type and n-type doped, respectively, whereas dopant-compensated CH₃NH₃PbBr_2.94Cl_0.06 alloy has over tenfold improved bulk resistivity of 3.6×10⁹ φcm. Alloying also increases the hole mobility to 560 cm² V^-1 s^-1, yielding a high mobility-lifetime product of 1.8×10^-2 cm² V^-1. The use of a guard ring electrode in the detector reduces the crystal surface leakage current and device dark current. A distinguishable ¹³⁷Cs energy spectrum with comparable or better resolution than standard scintillator detectors is collected under a small electric field of 1.8Vmm^-1 at room temperature.

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The charge carrier lifetime in organic-inorganic perovskites is one of the most important parameters for modeling and design of solar cells and other types of devices. In this work, we use CH₃NH₃PbI₃ single crystal as a model system to study optical absorption, charge carrier generation, and recombination lifetimes. We show that commonly applied photoluminescence lifetime measurements may dramatically underestimate the intrinsic carrier lifetime in CH₃NH₃PbI₃, which could be due to severe charge recombination at the crystal surface and/or fast electron-hole recombination close to the surface. By using the time-resolved microwave conductivity technique, we investigated the lifetime of free mobile charges inside the crystals. Most importantly, we find that for homogeneous excitation throughout the crystal, the charge carrier lifetime exceeds 15 μs. This means that the diffusion length in CH₃NH₃PbI₃ can be as large as 50 μm if it is no longer limited by the dimensions of the crystallites.

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