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The design and maintenance of turnouts implies considering a great number of external factors that have an effect on their structural integrity. The proper generalised decomposition (PGD) method is used in this paper to carry out a parametrised study of the vehicle-track interaction in a non-intrusive way. The reduced model is built using a defined domain for some of the influence parameters: the vehicle speed, the wheel/rail friction coefficient and the axle load. A major assumption of the PGD solution is its ability to express quantities of interest in a separated form. One of the goals is to show the relevance of separability in the field of railway dynamics. The example of a switch is chosen as a validation of method. The output of the PGD model is the dissipated energy per metre in the switch rail. The robustness of the reduced model is verified using the complete solution. Probabilistic simulations are carried out in order to explore the possibility of using the PGD solution as a numerical abacus in dynamic simulations of switches and crossings. These first tests show that the PGD solution is a promising reduction model method in railway dynamics. [ABSTRACT FROM AUTHOR]

Dynamic loads on engineering structures are often difficult to measure directly. Therefore, indirect identification methods based on dynamic responses are commonly used. However, this approach is often influenced by ill-conditioned matrices, noise interference, unknown structural and/or material parameters, and difficulty of constructing transfer functions when the traditional physics-based model is used. To address these issues, significant strides have been made in data-driven identification of dynamic loads, which are model-free and independent of structural characteristics. This paper tries to present a comprehensive review of dynamic load identification methods based on data-driven techniques, covering two aspects: load localization and load reconstruction. Features of the widely used data-driven techniques such as the geometric method, reference database method, machine learning methods including SVM-based methods and ANN-based methods and deep learning methods are discussed in detail. Additionally, this paper offers insight into the challenges and prospects of the data-driven techniques for dynamic load identification. The review aims to provide valuable insights for identifying dynamic loads in complex structures based on data-driven techniques and suggests future research directions. [ABSTRACT FROM AUTHOR]

This paper discusses a framework for fault detection in nonlinear processes. A novel subspace aided parity-based data-driven technique is proposed using the so-called just-in-time learning (JITL) approach. The idea of the proposed technique is to choose a set of the most similar data samples from the database for each test data sample using the JITL approach to address the nonlinearity problem. The parity vector is constructed for each test sample using the selected data samples to generate the residual signal. The salient features of the proposed technique include easy and simple implementation together with effectiveness in fault detection in the presence of disturbances and measurement noise. The fault detection scheme is so designed that it exhibits robustness against sensor noise and disturbances and sensitivity to faults. A case study for the fault detection of a nuclear research reactor (NRR) is presented to demonstrate the efficacy of the technique. The NRR is a highly nonlinear and complex process. Two faults of NRR, namely external reactivity insertion and control rod withdrawal, are successfully detected using the proposed approach. [ABSTRACT FROM AUTHOR]

Evaporation rate is one of the key parameters in determining the ecological conditions and it has an irrefutable role in the proper management of water resources. In this paper, the efficiency of some data-driven techniques including support vector regression (SVR) and artificial neural networks (ANN) and combination of them with wavelet transforms (WSVR and WANN) were investigated for predicting evaporation rates at Tabriz (Iran) and Antalya (Turkey) stations. For evaluating the performances of studied techniques, four different statistical indicators were utilized namely the root mean square error (RMSE), themeanabsolute error (MAE), the correlation coefficient (R), and Nash-Sutcliffe efficiency (NSE). Additionally, Taylor diagrams were implemented to test the similarity among the observed and predicted data. Outcomes showed that at Tabriz station, the ANN3 (third input combination that are air temperatures and solar radiation used by ANN) with RMSE of 0.701, MAE of 0.525, R of 0.990 and NSE of 0.977 had better performances in comparison with WANN, SVR and WSVR. So, the wavelet transforms did not have positive effects in increasing the precision of ANN and SVR predictions at Tabriz station. Also, approximately the same trend was seen at Antalya station. In other words, ANN5 (fifth input combination that are air temperatures, relative humidity and solar radiation used by ANN) with RMSE of 0.923, MAE of 0.697, R of 0.962 and NSE of 0.898 had a more accurate predictions among others. Conversely, wavelet transform reduced the prediction errors of SVR at Antalya station. So, the WSVR5 with RMSE of 1.027, MAE of 0.728, R of 0.950 and NSE of 0.870 predicted evaporation rates of Antalya station more precisely than other SVR models. As a conclusion, results from the current study proved that ANN provided reasonable trends for evaporation modeling at both Tabriz and Antalya stations. [ABSTRACT FROM AUTHOR]

Load prediction of tunnel boring machines (TBMs) is crucial for the design and safe operation of these complex engineering systems. However, to date, studies have mostly used only geological data, but the operation of TBMs also has an important effect on the load, especially its dynamic behavior. With the development of measurement techniques, large amounts of operation data are obtained during tunnel excavation. Mining these heterogeneous in-situ data, including geological data and operation data, is expected to improve the prediction accuracy and to realize dynamic predictions of the load. In this paper, a dynamic load prediction approach is proposed based on heterogeneous in-situ data and a data-driven technique. In this approach, the integration of heterogeneous in-situ data is conducted as follows: i) the geological data are extended to match the scale of the operation data using an interpolation method; ii) the categorical data and numerical data are fused through a proposed encoding method; and iii) the geological data are combined with the operation data according to the location of each operation datum. A data-driven technique, Random forest, is used to construct the prediction model based on the integrated heterogeneous in-situ data. The approach is applied to a collection of heterogeneous in-situ TBM data from a tunnel in China, and the results indicate that the approach can not only accurately predict the dynamic behaviour of the load but can also precisely estimate the statistical characteristics of the load. This work also highlights the applicability and potential of data-driven techniques in the design and analysis of other complex engineering systems similar to TBMs. [ABSTRACT FROM AUTHOR]

Abstract: Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipe''s probability to fail and then to optimize the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritize pipes for break inspection and then improve detection efficiency. [Copyright &y& Elsevier]

Abstract: This paper presents a case study of the application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine. Using measurement signals taken from the sensors/actuators which are present in a modern automotive vehicle, a data-driven diagnostic model is built for condition monitoring purposes. Detailed investigations have shown that measured signals taken from the experimental test-bed often contain redundant information and noise due to the nature of the process. In order to deliver a clear interpretation of these measured signals, they therefore need to undergo a ‘compression’ and an ‘extraction’ stage in the modelling process. It is at this stage that the proposed data-driven monitoring technique plays a significant role by taking only the important information of the original measured signals for fault diagnosis purposes. The status of the engine''s performance is then monitored using this diagnostic model. This condition monitoring process involves two separate stages of fault detection and root-cause diagnosis. The effectiveness of this diagnostic model was validated using an experimental automotive 1.9L four-cylinder diesel engine embedded in a chassis dynamometer in an engine test-bed. Two joint diagnostics plots were used to provide an accurate and sensitive fault detection process. Using the proposed model, small air leaks in the inlet manifold plenum chamber with a diameter size of 2–6mm were accurately detected. Further analyses using contribution to T 2 and Q statistics show the effect of these air leaks on fuel consumption. It was later discovered that these air leaks may contribute to emissions fault. In comparison to the existing model-based approaches, the proposed method has several benefits: (i) it makes no simplifying assumptions, as the model is built entirely from the measured signals; (ii) it is simple and straight-forward; (iii) there is no additional hardware required for modelling; (iv) it is a time and cost-efficient way to deliver condition monitoring (i.e. fault diagnosis application); (v) it is capable of pin-pointing the root-cause and the effect of the problem; and (vi) it is feasible to be implemented in practice. [Copyright &y& Elsevier]