Fault-Tolerant Analysis for Active Steering Actuation System Applied on Conventional Bogie Vehicle

Active steering system can drastically improve dynamic behaviors of the railway vehicle but will also introduce safety-critical issues. The fault-tolerant analysis therefore is essential for the design and implementation of this technology. In this work, an approach based on Risk Priority Number from Failure Mode and Effect Analysis is established to present quantitative assessment for fault tolerance of actuation system. This method is adopted to compare proposed nine different active steering schemes where two different hydraulic actuators are considered, and additional passive spring or redundant structure is implemented as back-up to ensure the safety. In case studies, the impacts of typical failure modes are investigated through multi-body simulation and quantified by Severity factor. Finally, the fault tolerance of different actuation schemes is compared by RPN values

Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms

In recent years, significant studies have focused on monitoring the track geometry irregularities through measurements of vehicle dynamics acquired onboard. Most of these studies analyse the vertical irregularity and the vertical vehicle dynamics since the lateral direction is much more challenging due to the non-linearities caused by the contact between the wheels and the rails. In the present work, a machine learning-based fault classifier for the condition monitoring of track irregularities in the lateral direction is proposed. The classifiers are trained with a dataset composed of numerical simulation results and validated with a dataset of measurements acquired by a diagnostic vehicle on the straight track sections of a high-speed line (300 km/h). Classifiers based on decision tree, linear and Gaussian support vector machine algorithms are developed and compared in terms of performance: good results are achieved with the three algorithms, especially with the Gaussian support vector machine. Even though classifiers are data driven, they retain the essence of lateral dynamics

Monitoring of Alignment Level (AL)and Cross Level (CL) Track Geometry Irregularities from Onboard Vehicle Dynamics Measurements Using Probabilistic Fault Classifier

Condition monitoring of track geometry irregularities from onboard measurements is a cost-effective method for daily surveillance of track quality. The monitoring of Alignment Level (AL) and Cross Level (CL) track irregularities is challenging due to the nonlinearities of the contact between wheels and rails. Recently, the authors proposed a signal-based method in combination with a machine learning (ML) fault classifier to monitor AL and CL track irregularities based on bogie frame accelerations. The authors concluded that the Support Vector Machine (SVM) fault classifier outperformed other traditional ML classifiers. Thus, an important question arises: Is the previously reported decision boundary an optimal boundary? The objective of this research investigation is to obtain an optimal decision boundary according to theory of probabilistic classification and compare the same against the SVM decision boundary. In this investigation, the classifiers are trained with results of numerical simulations and validated with measurements acquired by a diagnostic vehicle on straight track sections of a high-speed line (300 km/h). A fault classifier based on Maximum A Posterior Naïve Bayes (MAP-NB) classification is developed. It is shown that the MAP-NB classifier generates an optimal decision boundary and outperforms other classifiers in the validation phase with classification accuracy of 95.9 ± 0.2%and kappa value of 80.4 ± 0.6%. Moreover, the Linear SVM (L SVM) and Gaussian-SVM (G SVM) classifiers give similar performance with slightly lower accuracy and kappa value. The decision boundaries of previously reported SVM based fault classifiers are very close to the optimal MAP-NB decision boundary. Thus, this further strengthens the idea of implementing statistical fault classifiers to monitor the track irregularities based on dynamics in the lateral plane via in-service vehicles. The proposed method contributes towards digitalization of rail networks through condition-based and predictive maintenance

Epidemiology of hematopoietic cancers in north of Iran: results of Mazandaran population based cancer registry

Objective: Hematopoietic malignancies include leukemias, lymphomas, plasma cell tumors, myelodysplastic syndromes, and malignant mastocytosis. There are very rare population-based epidemiological studies in hematopoietic cancers in Iran. The aim of this study was to describe the epidemiology of hematopoietic cancers in the north of Iran. Patients and Methods: This longitudinal study was carried out on cancer incidence data of Mazandaran Population-based Cancer Registry (MazPCR), which is responsible for recording cancer cases in the population of all counties of Mazandaran province (except Babol, which is covered by the Babol University of Medical Science) in 2014. We used SPSS 16 (SPSS Inc., Chicago, IL, USA) for statistical analysis. Chi-Square test was used and p-values < than 0.05 were considered statistically significant. Results: The Age-Standardized incidence Rates (ASRs) of leukemia were 8.2 and 5.4 per 100,000 in men and women, respectively. The ASRs of lymphoma among men and women were 7.1 and 3.2 and the ASRs of multiple myeloma were 1.7 for men and 1.8 for women. The ASRs of lymphoma and multiple myeloma in urban areas were higher than rural, while the ASR of leukemia in rural areas was higher. Conclusions: The ASRs of leukemia and lymphoma were higher in males and the ASR of multiple myeloma was higher in females in this study. The ASRs of leukemia and Hodgkin’s lymphoma in the MazPCR coverage area were higher than estimated ASRs in the world and Iran, while ASRs of Non-Hodgkin’s lymphoma were lower than those estimated for the world and Iran