An Improved Time-Domain Damage Detection Method for Railway Bridges Subjected to Unknown Moving Loads

There is considerable interest in structural health monitoring (SHM) and damage detection of bridges and considerable progress has been made in this field in recent years. However, several challenges such as sensitivity to low levels of damage and identification without the knowledge of the moving load remain and need to be precisely investigated by researchers. The current work addresses such challenges and proposes an efficient response sensitivity-based model updating procedure in time-domain for damage identification of railway bridges subjected to unknown moving loads. The bridge is modelled as an Euler-Bernoulli beam and the train is modelled as a set of sprung masses passing over the beam. Structural damage is considered as a reduction in the modulus of elasticity of the elements. Sensitivity analysis and Tikhonov regularization methods are adopted and used to solve the inverse problem of the model updating. To verify the efficiency of the model, two numerical models with multiple damage scenarios subjected to unknown moving loads are analyzed. In addition, the efficiency of the proposed method in the presence of measurement noise is also verified. Numerical results reveal that the proposed model-updating procedure simultaneously identifies structural damages as well as the unknown moving loads with an acceptable accuracy. The effect of critical parameters such as mass and speed of the moving vehicle on the accuracy of identification results is investigated as well. Based on the findings of this research, the proposed method can be adopted and applied to online and long-term health monitoring of real bridge structures

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Online condition monitoring of railway wheelsets

The rail industry has focused on the improvement of maintenance through the effective use of online condition monitoring of rolling stock and rail infrastructure in order to reduce the occurrence of unexpected catastrophic failures and disruption that arises from them to an absolute minimum. The basic components comprising a railway wheelset are the wheels, axle and axle bearings. Detection of wheelset faults in a timely manner increases efficiency as it helps minimise maintenance costs and increase availability. The main aim of this project has been the development of a novel integrated online acoustic emission (AE) and vibration testing technique for the detection of wheel and axle bearing defects as early as possible and well before they result in catastrophic failure and subsequently derailment. The approach employed within this research study has been based on the combined use of accelerometers and high-frequency acoustic emission sensors mounted on the rail or axle box using magnetic hold-downs. Within the framework of this project several experiments have been carried out under laboratory conditions, as well as in the field at the Long Marston Test Track and in Cropredy on the Chiltern Railway line to London

MULTI‐PHYSICAL MODELLING AND PROTOTYPING OF AN ENERGY HARVESTING SYSTEM INTEGRATED IN A RAILWAY PNEUMATIC SUSPENSION

The aim of this PhD thesis is the investigation of an energy harvesting system to be integrated in a railway pneumatic spring to recovery otherwise wasted energy source from suspension vibration. Exploiting the piezoelectric effect to convert the mechanical energy into an electrical one, the final scope consists on the use of this system to power supply one or more sensors that can give useful information for the monitoring and the diagnostics of vehicle or its subsystems. Starting from the analysis of the energy sources, a multi‐physical approach to the study of an energy harvesting system is proposed to take into account all physics involved in the phenomenon, to make the most of the otherwise wasted energy and to develop a suitable and affordable tool for the design. The project of the energy harvesting device embedded in a railway pneumatic spring has been carried out by means of using a finite element technique and multi‐physics modelling activity. The possibility to combine two energy extraction processes was investigated with the purpose of making the most of the characteristics of the system and maximize the energy recovering. Exploiting commercial piezoelectric transducers, an experimental activity was conducted in two steps. A first mock‐up was built and tested on a shaker to develop the device and to tune the numerical model against experimental evidence. In the second step a fullscale prototype of an air spring for metro application with the EH system was realized. In order to test the full‐scale component, the design of a new test bench was carried out. Finally, the Air spring integrated with the EH device was tested and models validated