A one dimensional numerical approach for computing the eigenmodes of elastic waves in buried pipelines

Ultrasonic guided waves are often used in the detection of defects in oil and gas pipelines. It is common for these pipelines to be buried underground and this may restrict the length of the pipe that can be successfully tested. This is because acoustic energy travelling along the pipe walls may radiate out into the surrounding medium. Accordingly, it is important to develop a better understanding of the way in which elastic waves propagate along the walls of buried pipes, and so in this article a numerical model is developed that is suitable for computing the eigenmodes for uncoated and coated buried pipes. This is achieved by combining a one dimensional eigensolution based on the semi-analytic finite element (SAFE) method, with a perfectly matched layer (PML) for the infinite medium surrounding the pipe. This article also explores an alternative exponential complex coordinate stretching function for the PML in order to improve solution convergence. It is shown for buried pipelines that accurate solutions may be obtained over the entire frequency range typically used in long range ultrasonic testing (LRUT) using a PML layer with a thickness equal to the pipe wall thickness. This delivers a fast and computationally efficient method and it is shown for pipes buried in sand or soil that relevant eigenmodes can be computed and sorted in less than one second using relatively modest computer hardware. The method is also used to find eigenmodes for a buried pipe coated with the viscoelastic material bitumen. It was recently observed in the literature that a viscoelastic coating may effectively isolate particular eigenmodes so that energy does not radiate from these modes into the surrounding [elastic] medium. A similar effect is also observed in this article and it is shown that this occurs even for a relatively thin layer of bitumen, and when the shear impedance of the coating material is larger than that of the surrounding medium

Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry.This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on amachine learning algorithm that generates a baseline for the identification of deviations fromthe normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information.The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature

Monitoring a reverse osmosis process with kernel principal component analysis: A preliminary approach

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context

Enhancement of ultrasonic guided wave signals using a split-spectrum processing method

Ultrasonic guided wave (UGW) systems are broadly utilised in several industry sectors where the structural integrity is of concern, in particular, for pipeline inspection. In most cases, the received signal is very noisy due to the presence of unwanted wave modes, which are mainly dispersive. Hence, signal interpretation in this environment is often a challenging task, as it degrades the spatial resolution and gives a poor signal-to-noise ratio (SNR). The multi-modal and dispersive nature of such signals hampers the ability to detect defects in a given structure. Therefore, identifying a small defect within the noise level is a challenging task. In this work, an advanced signal processing technique called split-spectrum processing (SSP) is used firstly to address this issue by reducing/removing the effect of dispersive wave modes, and secondly to find the limitation of this technique. The method compared analytically and experimentally with the conventional approaches, and showed that the proposed method substantially improves SNR by an average of 30dB. The limitations of SSP in terms of sensitivity to small defects and distances are also investigated, and a threshold has been defined which was comparable for both synthesised and experimental data. The conclusions reached in this work paves the way to enhance the reliability of UGW inspection

Improved defect detection of guided wave testing using split-spectrum processing

© 2020 by the authors. Ultrasonic guided wave (UGW) testing is widely applied in numerous industry areas for the examination of pipelines where structural integrity is of concern. Guided wave testing is capable of inspecting long lengths of pipes from a single tool location using some arrays of transducers positioned around the pipe. Due to dispersive propagation and the multimodal behavior of UGW, the received signal is usually degraded and noisy, that reduce the inspection range and sensitivity to small defects. Therefore, signal interpretation and identifying small defects is a challenging task in such systems, particularly for buried/coated pipes, in that the attenuation rates are considerably higher compared with a bare pipe. In this work, a novel solution is proposed to address this issue by employing an advanced signal processing approach called “split-spectrum processing” (SSP) to minimize the level of background noise and enhance the signal quality. The SSP technique has already shown promising results in a limited trial for a bar pipe and, in this work, the proposed technique has been experimentally compared with the traditional approach for coated pipes. The results illustrate that the proposed technique significantly increases the signal-to-noise ratio and enhances the sensitivity to small defects that are hidden below the background noise.Greenwich University (Internal funds code 13265-0641-R08584); Innovation UK fund managed by The Welding Institute (TWI) Ltd. (reference 102077, project IC0513 (TWI project 30034) in partnership of Brunel University

An Artificial Intelligence Neural Network Predictive Model for Anomaly Detection and Monitoring of Wind Turbines Using SCADA Data

Copyright © 2022 The Author(s). The industry 4.0 has created a paradigm shift in how industrial equipment could be monitored and diagnosed with the help of emerging technologies such as artificial intelligence (AI). AI-driven troubleshooting tools play an important role in high-efficacy diagnosis and monitoring processes, especially for systems consisting of several components including wind turbines (WTs). The utilization of such approaches not only reduces the troubleshooting and diagnosis time but also enables fault prevention by predicting the behavior of different components and calculating the probability of near future failure. This not only decreases the costs of repair by providing constant component’s monitoring and identifying faults’ causes but also increases the efficacy of the apparatus by lowering the downtimes due to the AI-driven early warning system. This article evaluated, compared, and contrasted eight different artificial neural network (ANN) models for diagnosis and monitoring of WTs that predict the machinery’s system failure based on internal components’ sensor signals and generation temperature. This article employed a machine learning model approach with two hidden layers using multilayer linear regression to achieve its objective. The developed system predicted the output of the WT’s generator temperature with an accuracy of 99.8% with 2 months in advance measurement prediction

Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images

Data Availability Statement: Data available on request due to restrictions eg privacy or ethical.Copyright © 2022 by the authors.Ultrasonic time-of-flight diffraction (TOFD) is a non-destructive testing (NDT) technique for weld inspection that has gained popularity in the industry, due to its ability to detect, position, and size defects based on the time difference of the echo signal. Although the TOFD technique provides high-speed data, ultrasonic data interpretation is typically a manual and time-consuming process, thereby necessitating a trained expert. The main aim of this work is to develop a fully automated defect detection and data interpretation approach that enables predictive maintenance using signal and image processing. Through this research, the characterization of weld defects was achieved by identifying the region of interest from A-scan signals, followed by segmentation. The experimental results were compared with samples of known defect size for validation; it was found that this novel method is capable of automatically measuring the defect size with considerable accuracy. It is anticipated that using such a system will significantly increase inspection speed, cost, and safety.The research leading to these results has received funding from the UK’s innovation agency, Innovate UK, under grant agreement No. 103991. The research has been undertaken as a part of the project Amphibious robot for inspection and predictive maintenance of offshore wind assets (iFROG). The iFROG project is a collaboration between the following organizations: Innovative Technology and Science Ltd., Brunel University London, TWI Ltd., and ORE Catapult Development Services Ltd

Structural health monitoring of above-ground storage tank floors by ultrasonic guidedwave excitation on the tank wall

Abstract: There is an increasing interest in using ultrasonic guided waves to assess the structural degradation of above-ground storage tank floors. This is a non-invasive and economically viable means of assessing structural degradation. Above-ground storage tank floors are ageing assets which need to be inspected periodically to avoid structural failure. At present, normal-stress type transducers are bonded to the tank annular chime to generate a force field in the thickness direction of the floor and excite fundamental symmetric and asymmetric Lamb modes. However, the majority of above-ground storage tanks in use have no annular chime due to a simplified design and/or have a degraded chime due to corrosion. This means that transducers cannot be mounted on the chime to assess structural health according to the present technology, and the market share of structural health monitoring of above-ground storage tank floors using ultrasonic guided wave is thus limited. Therefore, the present study investigates the potential of using the tank wall to bond the transducer instead of the tank annular chime. Both normal and shear type transducers were investigated numerically, and results were validated using a 4.1 m diameter above-ground storage tank. The study results show shear mode type transducers bonded to the tank wall can be used to assess the structural health of the above-ground tank floors using an ultrasonic guided wave. It is also shown that for the cases studied there is a 7.4 dB signal-to-noise ratio improvement at 45 kHz for the guided wave excitation on the tank wall using shear mode transducers

INVESTIGATION ON THE USE OF POWER ULTRASONIC TO IMPROVE THE LASER WELDING OF ALUMINIUM ALLOYS

There is a rising interest on the autonomous laser welding of Aluminium alloys due to the quality of the weld, productivity and the simplicity of implementation. Unlike high grade alloys (i.e. Al 1100 which has excellent weldability), laser welding of low grade Alloys (i.e. Al 6063 which has poor weldability) has a higher demand due to material strength and cost benefits. However, laser welding of Alloys such as Al 6063 are challenging due to the material composition which has a poor weldability. Current study investigates the possibility of using high power ultrasonic during the laser welding process, to reduce voids during solidification and optimize the laser welding process. A finite element-based numerical study was undertaken to evaluate the propagation of ultrasonic waves and their interaction with the incremental weld seam. The plate sample (before joining) used in this study is a 300 x 150 x 3 mm (height, width and thickness respectively). A parametric study was conducted to obtain the resonant frequency of the sample plate and the optimum power level in order to tune the power ultrasonic system. A 3-D laser Doppler vibrometry experiment was conducted to validate the finite element results. There is a good agreement between numerical and experimental results. Based on the results, 40 kHz 60 W transducers need to be used for ultrasonication in order to improve the laser welding of Al 6063 using power ultrasonic. Furthermore, transducer topology was also investigated in order to optimize the system performance

Bolt looseness detection using Spectral Kurtosis analysis for structural health monitoring

Bolt looseness is a major problem for high valued infrastructure assets such as bridges. Historical data indicates failure in the operating bridges is related to bolted joints. This reveals defects generated by small components can leads to major problem thus early detection is required. In this paper, a novel methodology to characterize the bolt looseness using optimal filtering of vibration data is proposed. A Spectral Kurtosis (SK) based optimal filter is designed to extract frequencies that are generated by bolt looseness. The filter is capable of extracting weak signatures (hidden in acceleration data) that are generated by bolt looseness. The proposed approach is demonstrated through experiments. Results have indicated that the proposed approach can be reliable and effective in detecting loose bolts of a structure subjected to fluctuating loads.Innovate UK under grant agreement No. 103883. The research has been undertaken as a part of the project entitled “SmartBridge”. The SmartBridge project is a collaboration between the following organisations: James Fisher and Sons plc, TWI Ltd, Brunel University London & Innvotek Ltd