Independent Component Analysis for Improved Defect Detection in Guided Wave Monitoring

Guided wave sensors are widely used in a number of industries and have found particular application in the oil and gas industry for the inspection of pipework. Traditionally this type of sensor was used for one-off inspections, but in recent years there has been a move towards permanent installation of the sensor. This has enabled highly repeatable readings of the same section of pipe, potentially allowing improvements in defect detection and classification. This paper proposes a novel approach using independent component analysis to decompose repeat guided wave signals into constituent independent components. This separates the defect from coherent noise caused by changing environmental conditions, improving detectability. This paper demonstrates independent component analysis applied to guided wave signals from a range of industrial inspection scenarios. The analysis is performed on test data from pipe loops that have been subject to multiple temperature cycles both in undamaged and damaged states. In addition to processing data from experimental damaged conditions, simulated damage signals have been added to “undamaged” experimental data, so enabling multiple different damage scenarios to be investigated. The algorithm has also been used to process guided wave signals from finite element simulations of a pipe with distributed shallow general corrosion, within which there is a patch of severe corrosion. In all these scenarios, the independent component analysis algorithm was able to extract the defect signal, rejecting coherent noise

Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview

Rail tracks undergo massive stresses that can affect their structural integrity and produce rail breakage. The last phenomenon represents a serious concern for railway management authorities, since it may cause derailments and, consequently, losses of rolling stock material and lives. Therefore, the activities of track maintenance and inspection are of paramount importance. In recent years, the use of various technologies for monitoring rails and the detection of their defects has been investigated; however, despite the important progresses in this field, substantial research efforts are still required to achieve higher scanning speeds and improve the reliability of diagnostic procedures. It is expected that, in the near future, an important role in track maintenance and inspection will be played by the ultrasonic guided wave technology. In this manuscript, its use in rail track monitoring is investigated in detail; moreover, both of the main strategies investigated in the technical literature are taken into consideration. The first strategy consists of the installation of the monitoring instrumentation on board a moving test vehicle that scans the track below while running. The second strategy, instead, is based on distributing the instrumentation throughout the entire rail network, so that continuous monitoring in quasi-real-time can be obtained. In our analysis of the proposed solutions, the prototypes and the employed methods are described

The use of circumferential guided waves to monitor axial cracks

In this thesis, a monitoring system using circumferential guided waves is developed to detect axial cracking in piping and pipe components. Guided waves inspection is attractive as it is much faster than current slow manual inspection methods deployed in nuclear reactors however it is traditionally less sensitive. The use of guided waves in a monitoring configuration as a Structural Health Monitoring (SHM) technique has the potential to boost the traditionally low sensitivity of the technique while retaining the large volumetric coverage. If the guided wave transducers are fixed to the inspection geometry and take continuous measurements; a dataset can be developed that can be analysed using recently developed signal processing techniques to increase the sensitivity. In this project, a novel guided wave monitoring technique was developed based on a pitch-catch configuration of two transducers offset circumferentially and a defect detection technique based on multiple revolutions of the guided waves. This technique was shown to be a viable monitoring technique with commercially relevant sensitivity. The addition of a second pair of transducers circumferentially offset from the first pair was shown to significantly increase the sensitivity of the SHM system and give full volumetric coverage for the detection of axial defects. Defect detection capability was then demonstrated on a complex component containing several pipe features and a long duration monitoring experiment was performed which highlighted to need to improve the stability of the amplification for the SHM system before commercial adoption is possible. The aid this, two viable calibration techniques based were developed to improve the stability of the system based. This work has demonstrated the real-world viability of circumferential guided waves to detect axial cracks at industrially relevant sensitivity when used as a monitoring technique.Open Acces

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version