Fuzzy-Bayesian-network-based Safety Risk Analysis in Railway Passenger Transport

This study presents a fuzzy Bayesian network (FBN) method to analyze the influence on the safety risk of railway passenger transport applying different risk control strategies. Based on the fuzzy probability of the basic event determined by the expert group decision method, the proposed FBN method can reasonably predict the probability of railway passenger safety risk. It is also proven that control the risk in the safety management of railway passenger transport will be the most effective way to reduce the risk probability of the railway passenger transport safety

Reliability analysis for automobile engines: conditional inference trees

The reliability model with covariates for machinery parts has been extensively studied by the proportional hazards model (PHM) and its variants. However, it is not straightforward to provide business recommendations based on the results of the PHM. We use a novel method, namely the Conditional Inference Tree, to conduct the reliability analysis for the automobile engines data, provided by a UK fleet company. We find that the reliability of automobile engines is significantly related to the vehicle age, early failure, and repair history. Our tree-structured model can be easily interpreted, and tangible business recommendations are provided for the fleet management and maintenance

Automatic condition monitoring system for crack detection in rotating machinery

Maintenance is essential to prevent catastrophic failures in rotating machinery. A crack can cause a failure with costly processes of reparation, especially in a rotating shaft. In this study, the Wavelet Packets transform energy combined with Artificial Neural Networks with Radial Basis Function architecture (RBF-ANN) are applied to vibration signals to detect cracks in a rotating shaft. Data were obtained from a rig where the shaft rotates under its own weight, at steady state at different crack conditions. Nine defect conditions were induced in the shaft (with depths from 4% to 50% of the shaft diameter). The parameters for Wavelet Packets transform and RBF-ANN are selected to optimize its success rates results. Moreover, ‘Probability of Detection’ curves were calculated showing probabilities of detection close to 100% of the cases tested from the smallest crack size with a 1.77% of false alarms.The authors would like to thank the Spanish Government for financing through the CDTI project RANKINE21 IDI-20101560

Analysis of Recurrent Neural Networks for Henon Simulated Time-Series Forecasting

Forecasting of chaotic time-series has increasingly become a challenging subject. Non-linear models such as recurrent neural networks have been successfully applied in generating short term forecasts, but perform poorly in long term forecasts due to the vanishing gradient problem when the forecasting period increases. This study proposes a robust model that can be applied in long term forecasting of henon chaotic time-series whilst reducing the vanishing gradient problem through enhancing the models ability in learning of long-term dependencies. The proposed hybrid model is tested using henon simulated chaotic time-series data. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the generated forecasts. Performance evaluation results confirm that the proposed recurrent model performs long term forecasts on henon chaotic time-series effectively in terms of error metrics compared to existing forecasting models

Computational based automated pipeline corrosion data assessment

Corrosion is a complex process influenced by the surrounding environment and operational systems which cannot be interpreted by deterministic approach as in the industry codes and standards. The advancement of structural inspection technologies and tools has produced a huge amount of corrosion data. Unfortunately, available corrosion data are still under-utilized. Complicated assessment code, and manual analysis which is tedious and error prone has overburdened pipeline operators. Moreover, the current practices produce a negative corrosion growth data defying the nature of corrosion progress, and consuming a lot of computational time during the reliability assessment. Therefore, this research proposes a computational based automated pipeline corrosion data assessment that provides complete assessment in terms of statistical and computational. The purpose is to improve the quality of corrosion data as well as performance of reliability simulation. To accomplish this, .Net framework and Hypertext Preprocessor (PHP) language is used for an automated matching procedure. The alleviation of deterministic value in corrosion data is gained by using statistical analysis. The corrosion growth rate prediction and comparison is utilized using an Artificial Neural Network (ANN) and Support Vector Machine (SVM) model. Artificial Chemical Reaction Optimization Algorithm (ACROA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) model is used to improve the reliability simulation based on the matched and predicted corrosion data. A computational based automated pipeline corrosion data assessment is successfully experimented using multiple In-Line Inspection (ILI) data from the same pipeline structure. The corrosion data sampling produced by the automated matching is consistent compared to manual sampling with the advantage of timeliness and elimination of tedious process. The computational corrosion growth prediction manages to reduce uncertainties and negative rate in corrosion data with SVM prediction is superior compared to A ^N . The performance value of reliability simulation by ACROA outperformed the PSO and DE models which show an applicability of computational optimization models in pipeline reliability assessment. Contributions from this research are a step forward in the realization of computational structural reliability assessment

Predicting component reliability and level of degradation with complex-valued neural networks

International audienceIn this paper, multilayer feedforward neural networks based on multi-valued neurons (MLMVN), a specific type of complex valued neural networks, are proposed to be applied to reliability and degradation prediction problems, formulated as time series. MLMVN have demonstrated their ability to extract complex dynamic patterns from time series data for mid- and long-term predictions in several applications and benchmark studies. To the authors' knowledge, it is the first time that MLMVN are applied for reliability and degradation prediction. MLMVN are applied to a case study of predicting the level of degradation of railway track turnouts using real data. The performance of the algorithms is first evaluated using benchmark study data. The results obtained in the reliability prediction study of the benchmark data show that MLMVN outperform other machine learning algorithms in terms of prediction precision and are also able to perform multi-step ahead predictions, as opposed to the previously best performing benchmark studies which only performed up to two-step ahead predictions. For the railway turnout application, MLMVN confirm the good performance in the long-term prediction of degradation and do not show accumulating errors for multi-step ahead predictions