Mayorkinos Papaelias

Online condition monitoring of rolling stock wheels and axle bearings

The early detection of faults in rolling stock wheels and axle bearings is of paramount importance for rail infrastructure managers as it contributes to the safety of rail operations. In this paper we report on the key results that have arisen from the development and implementation of a novel condition monitoring system based on high-frequency acoustic emission and vibration analysis installed on a train. The novel system makes use of inexpensive and robust acoustic emission sensors and accelerometers, which can be easily installed on the axle bearing box with minimal intervention required. Experimental work carried out under actual conditions at the Long Marston rail track and on the Lisbon – Cas-Cais suburban line has proven that the developed system is capable of detecting wheel and axle bearing-related defects with various levels of severity.

An experimental study on the applicability of acoustic emission for wind turbine gearbox health diagnosis

Condition monitoring of wind turbine gearboxes has mainly relied upon vibration, oil analysis and temperature monitoring. However, these techniques are not well suited for detecting early stage damage. Acoustic emission is gaining ground as a complementary condition monitoring technique as it offers earlier fault detection capability compared with other more established techniques. The objective of early fault detection in wind turbine gearboxes is to avoid unexpected catastrophic breakdowns, thereby reducing maintenance costs and increase safety. The aim of this investigation is to present an experimental study the impact of operational conditions (load and torque) in the acoustic emission activity generated within the wind turbine gearbox. The acoustic emission signature for a healthy wind turbine gearbox was obtained as a function of torque and power output, for the full range of operational conditions. Envelope analysis was applied to the acoustic emission signals to investigate repetitive patterns and correlate them with specific gearbox components. The analysis methodology presented herewith can be used for the reliable assessment of wind turbine gearbox subcomponents using acoustic emission.

Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers

There is a significant rising in development of new concentrated solar plants due to global energy demands. Concentrated solar plant requires to improve the operational and maintainability in this industry. This article presents a new approach to identify defects in the solar receiver tubes and welds employing a simple electromagnetic acoustic transducer. The absorber tubes in normal working conditions must withstand high temperatures, which can cause the tubes to deteriorate in areas such as welding, or it can cause hot spots due to defects or corrosion. A proper predictive maintenance program for the absorber pipes is required to detect defects in the tubes at an early stage, reducing corrective maintenance costs and increasing the reliability, availability, and safety of the concentrated solar plant. This article presents a novel approach based on signal processing and pattern recognition for predictive maintenance employing electromagnetic acoustic transducers. Hilbert transform is used to obtain the envelope of the signal that is smoothed by wavelet transform. It reduces the probability of detecting false-positive alarms. The algorithm uses the distance of the sensors from the edges to perform a self-identification of signal events. The events are located using two possible ways of ultrasound propagation, forward and reverse, and the time of flight of each echo. The algorithm correlates the theoretical events with events found experimentally. These echoes could come from different paths due to the electromagnetic acoustic transducer that generates forward and reverse shear waves. The main novelty in this approach is that the detection and location of the defect is determined considering two echoes that come from the same defect, but they arrive at the sensor flowing by different paths. The results are obtained with a double validation by matching the echoes that meet certain conditions. It increases the accuracy of the inspection and reduces false alarms. The approach has been tested and validated in an experimental platform that simulates the concentrated solar plants.

Detection and evaluation of rail surface defects using alternating current field measurement techniques

Reliable and cost‐effective inspection of rail tracks is of paramount importance to ensure the safety of rolling stock operations. In this paper alternating current field measurement (ACFM) sensors are used to carry out experiments on artificially induced rail surface defects at various speeds using testing configurations that simulate actual inspection conditions found in the field. From the obtained results it can be clearly seen that the ACFM sensors can detect the artificially induced rail surface defects even when relatively significant lift-off is involved, i.e. ∼5 mm and that the effect of increasing speed on the amplitude of the Bx signal, which is directly related to the depth of the crack, is negligible. However, clustered defects cannot be easily resolved and the overall amplitude is related to the spacing of the defects within a cluster. The order of clustered defects also significantly influences the maximum amplitude of the recorded Bx signal. The validity of the results obtained from the tests on artificially induced defects was verified by conducting further ACFM tests on a rail sample removed from service that contained mild rolling contact fatigue cracks.

ULTRASONIC DETECTION OF SURFACE-BREAKING RAILHEAD DEFECTS

We recently presented measurements of defects on the railhead, using a novel pitch‐catch ultrasonic system comprising of two electro‐magnetic acoustic transducers (EMATs) generating and detecting Rayleigh waves. Current systems used on the UK rail network for detecting surface breaking defects are limited in speed ( 5 mm). The non‐contact EMAT system has the potential to operate at higher line speed, improving network inspection coverage. The current system detects signals and performs an FFT in less than 1 ms, and changes in the detected signal amplitude and frequency content are used to characterise defects. A new set of simulated defects on sections of rail have been produced, including half‐face slots machined normal to the railhead surface, clusters of angled slots, and pocket defects more typical of real defects. The smallest pocket defects are difficult to detect, with changes in signal amplitude and cut‐off falling close to the noise level. However, at chosen higher f...

A B-spline approach to alternating current field measurement for railroad inspection

Reliable detection and evaluation of surface breaking defects in rails caused by rolling contact fatigue mechanisms is of paramount importance for the rail industry. Experimental work has showed that alternating current field measurement (ACFM) techniques are suitable for the high-speed inspection of rails. This paper presents an algorithm based on B-spline estimation that analyses the shape of the ACFM signal obtained during inspection under laboratory conditions.

Multivariable Analysis for Advanced Analytics of Wind Turbine Management

Operation and maintenance tasks on the wind turbines have an essential role to ensure the correct condition of the system and to minimize losses and increase the productivity. The condition monitoring systems installed on the main components of the wind turbines provide information about the tasks that should be carried out over the time. A novel statistical methodology for multivariable analysis of big data from wind turbines is presented in this paper. The objective is to analyse the necessary information from the condition monitoring systems installed in wind farms. The novel approach filters the main parameters from the collected signals and uses advanced computational techniques for evaluating the data and giving meaning to them. The main advantage of the approach is the possibility of the big data analysis based on the main information available.

Crossing Phenomena in Overhead Line Equipment (OHLE) Structure in 3D Space Considering Soil-Structure Interaction

Nowadays, the electric train becomes one of the efficient railway systems that are lighter, cleaner, quieter, cheaper and faster than a conventional train. Overhead line equipment (OHLE), which supplies electric power to the trains, is designed on the principle of overhead wires placed over the railway track. The OHLE is supported by mast structure which located at the lineside along the track. Normally, mast structure is a steel column or truss structure which supports the overhead wire carrying the power. Due to the running train and severe periodic force, such as an earthquake, in surrounding area may cause damage to the OHLE structure especially mast structure which leads to the failure of the electrical system. The mast structure needs to be discussed in order to resist the random forces. Due to the vibration effect, the natural frequencies of the structure are necessary. This is because when the external applied force occurs within a range of frequency of the structure, resonance effect can be expected which lead to the large oscillations and deflections. The natural frequency of a system is dependent only on the stiffness of the structure and the mass which participates with the structure, including self-weight. The modal analysis is used in order to calculate the mode shapes and natural frequencies of the mast structure during free vibration. A mast structure with varying rotational soil stiffness is used to observe the influence of soil-structure action. It is common to use finite element analysis to perform a modal analysis. This paper presents the fundamental mode shapes, natural frequencies and crossing phenomena of three-dimensional mast structure considering soil-structure interaction. The sensitivity of mode shapes to the variation of soil-structure interaction is discussed. The outcome of this study will improve the understanding of the fundamental dynamic behaviour of the mast structure which supports the OHLE. Moreover, this study will be a recommendation for the structural engineer to associate with the behaviour of mast structure during vibration.

Generalized Transmissibility Damage Indicator With Application to Wind Turbine Component Condition Monitoring

Frequency methods such as frequency spectrum analysis, frequency spike detection, demodulation, envelope spectrum method have been widely used for condition monitoring of engineering structural systems. Different from the conventional frequency methods, the transmissibility function (TF) represents the relationship between different system output responses such as, e.g., vibration and acoustic emission sensor measurements. This paper introduces a simple and effective generalized transmissibility damage indicator (GTDI) for TF-based condition monitoring. Unlike the conventional transmissibility damage indicator, the new GTDI can improve the detection sensitivity, reduces noise effects and avoid dynamic loadings effects. This is achieved by combining multiple groups of data to obtain more accurate transmissibility analysis, exploiting all the available TFs, and using multiple references. This has two advantages. First, it does not require any other priori knowledge about the system responses. Therefore, the method can be used for the condition monitoring of a wide range of components or systems. Furthermore, the method can be easily implemented using fast Fourier transform or power spectra density methods and therefore is computationally efficient. These make the method very suitable for implementing online real-time condition monitoring. The method is investigated by simulation studies and then applied to analyze the vibration data of the main bearing of operating wind turbines, producing very promising results.

Methods and Tools for the Operational Reliability Optimisation of Large-Scale Industrial Wind Turbines

Wind turbines (WT) maintenance management is in continuous development to improve the reliability, availability, maintainability and safety (RAMS) of WTs, and to achieve time and cost reductions. The optimisation of the operation reliability involves the supervisory control and data acquisition to guarantee correct levels of RAMS. A fault detection and diagnosis methodology is proposed for large-scale industrial WTs. The method applies the wavelet and Fourier analysis to vibration signals. A number of turbines (up to 3) of the same type will be instrumented in the same wind farm. The data collected from the individual turbines will be fused and analysed together in order to determine the overall reliability of this particular wind farm and wind turbine type. It is expected that data fusion will allow a significant improvement in overall reliability since the value of the information gained from the various condition monitoring systems will be enhanced. Effort will also focus on the successful application of dependable embedded computer systems for the reliable implementation of wind turbine condition monitoring and control technologies.