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This paper describes the numerical and experimental investigation of the nonlinear vibration of a bladed Jeffcott rotor. The nonlinearity in the system is due to discontinuities caused by multiple contacts with an outer ring as well as the nonlinear deformation of the massless blades. Contacts occur since the rotor shaft is initially misaligned by displacing the outer ring in one direction. The aim of the paper is to develop a simple model of bladed rotor and verify whether the global dynamics of the numerical simulations can be observed experimentally. The experimental rig and data acquisition are presented in detail together with the experimental procedures. The results between the numerical simulation and experiments are compared in terms of bifurcation diagrams and waterfall plots. An overall correlation is observed between the numerical and experimental study in the case of stiff blades, with differences mainly in localized frequency ranges due to parameter variation.

The rail fastening system forms an indispensable part of the rail tracks and needs to be periodically inspected to ensure safe, reliable and sustainable rail operations. Automated visual inspection has gained significant importance for fastener inspection in recent years. Position accuracy, robustness, and practical limitations due to the complex environment are some of the major concerns associated with this method. This study investigates the combined use of image processing and deep learning algorithms for detecting missing clamps within a rail fastening system. The images used for this study was acquired during field inspections carried out along the Borlänge-Avesta line in Sweden. The image processing techniques proposed in this study enabled the improvement of the fastener position and removal of redundant information from the fastener images. In addition, image augmentation was carried out to enhance the data set, ensure experimental reliability and replicate practical challenges associated with such visual inspection. Convolutional neural network and ResNet-50 algorithms are used for classification purposes, and both the algorithms achieved over 98% accuracy during training and validation and over 94% accuracy during the test stage. Both the algorithms also maintained a good balance between the precision and recall scores during the test stage. CNN and ResNet-50 algorithms were also tested to analyse their performances when the clamp areas were covered. CNN was able to accurately predict the fastener state up to 70% of clamp area occlusion, and ResNet-50 was able to achieve accurate predictions up to 75% of clamp area occlusion.

The rail fastening system plays a crucial role in railway tracks as it ensures operational safety by fixing the rail on to the sleeper. Early detection of rail fastener system defects is crucial to ensure track safety and to enable maintenance optimization. Fastener inspections are normally conducted either manually by trained maintenance personnel or by using automated 2-D visual inspection methods. Such methods have drawbacks when visibility is limited, and they are also found to be expensive in terms of system maintenance cost and track possession time. In a previous study, the authors proposed a train-based differential eddy current sensor system based on the principle of electromagnetic induction for fastener inspection that could overcome the challenges mentioned above. The detection in the previous study was carried out with the aid of a supervised machine learning algorithm. This study reports the finding of a case study, along a heavy haul line in the north of Sweden, using the same eddy current sensor system mounted on an in-service freight train. In this study, unsupervised machine learning models for detecting and analyzing missing clamps in a fastener system were developed. The differential eddy current measurement system was set to use a driving field frequency of 27 kHz. An anomaly detection model combining isolation forest (IF) and connectivity-based outlier factor (COF) was implemented to detect anomalies from fastener inspection measurements. To group the anomalies into meaningful clusters and to detect missing clamps within the fastening system, an unsupervised clustering based on the DBSCAN algorithm was also implemented. The models were verified by measuring a section of the track for which the track conditions were known. The proposed anomaly detection model had a detection accuracy of 96.79% and also exhibited a high score of sensitivity and specificity. The DBSCAN model was successful in clustering missing clamps, both one and two missing clamps, from a fastening system separately.

Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&Cs. For practicality, installation of wired or wireless sensors along the S&C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&C regarding squat defects.

Wheelsets form an indispensable part of the railway rolling stock and need to be periodically inspected to ensure stable, safe, reliable, and sustainable rail operation. Wheel profiles are usually inspected and measured in a workshop environment using handheld equipment or by utilizing wayside measuring equipment. A common practice for both methods is to measure the wheel profile at one position along the circumference of the wheel, resulting in a one-slice measurement strategy, based on the assumption that the wheel profile has the same shape independent of the measurement position along the wheel. In this article, the representability of a one-slice measurement strategy with respect to the wheel profile parameters is investigated using handheld measurement equipment. The calculated range of standard deviation of the parameters estimated such as flange height, flange width, flange slope, and hollow wear from the measurements shows a spread in the parameter value along the circumference of the wheel. As an initial validation of the results, measurements from the wayside monitoring systems were also investigated to see if a similar spread was visible. The spread was significantly higher for flange height, flange width, and flange slope estimated from wayside measurement equipment than for the same parameters estimated using the handheld measurement equipment.

Ballasted tracks are common in the railway system as a means of providing the necessary support for the sleepers and the rails. To keep them operational, tamping and other maintenance actions are performed based on track geometry measurements. Ballast particle rearrangement, which is caused by train load, is one of the most important factors leading to track degradation. As a result, when planning maintenance, it is vital to predict the behaviour of the ballast under cyclic loading. Since ballast is a granular matter with a nonlinear and discontinuous mechanical behaviour, the discrete element method (DEM) was used in this paper to model the ballast particle rearrangement under cyclic loading. We studied the performance of linear and nonlinear models in simulating the settlement of the sleeper, the lateral deformation of the ballast shoulder and the porosity changes under the sleeper. The models were evaluated based on their ability to mimic the ballast degradation pattern in vertical and lateral direction. The linear contact model and the hysteretic contact model were used in the simulations, and the effect of the friction coefficient and different damping models on the simulations was assessed. An outcome of this study was that a nonlinear model was proposed in which both the linear and the hysteretic contact models are combined. The simulation of the sleeper settlement and the changes in the porosity under the sleeper improved in the proposed nonlinear model, while the computation time required for the proposed model decreased compared to that required for the linear model.

Hydropower rotors and pumps have the specificity to be oriented vertically, meaning that the bearing forces have to be evaluated at each time-step depending on the position of the rotor for dynamical analyses. If the bearing forces cannot be evaluated analytically, a suitable numerical method should be used to calculate the pressure distribution over the bearing domain. This process can be computationally expensive as it should be performed for each discrete time-step. As a result, a comparison between the spectral method, the finite difference method, and the finite element method is performed to investigate which method is more adapted to dynamical analysis of the bearing. It is observed that the spectral method has the advantage of having a reasonable simulation time for any eccentricity magnitude with a moderate number of interpolation points. However, this method should be restricted to simple bearing models such as plain bearings or multilobe bearings due to the advantage of finding a global numerical solution directly on the entire bearing/pad domain.