Outlier Analysis and Artificial Neural Network for the Noncontact Nondestructive Evaluation of Immersed Plates

Bagheri, Abdollah; Pistone, Elisabetta; Rizzo, Piervincenzo

doi:10.1080/09349847.2015.1022677

Cited by 15 publications

(11 citation statements)

References 45 publications

(64 reference statements)

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“…Damage detection is generally performed by unsupervised algorithms, as the inspection data corresponding to the damage state is not known a priori. One such algorithm is outlier analysis (OA) [70] where damage sensitive features are extracted from the signals, and the aim is to identify if the measured signal has deviated from the baseline distribution by defining a threshold. This threshold depends on the baseline data and its statistical representation.…”

Section: Damage Detection Methodologiesmentioning

confidence: 99%

“…For multivariate analysis, a vector of damage index is formed by extracting a number of features and classical methods of multi-variate statistics such as principal component analysis (PCA) is applied to them. For the multi-variate case, OA can be performed using a self-organising mapping technique [72] or Mahalanobis squared distance [70]. An artificial neural network (ANN) based method was applied in [70] for damage classification and it was able to outperform OA for damage detection with just one feature.…”

Section: Damage Detection Methodologiesmentioning

confidence: 99%

“…For the multi-variate case, OA can be performed using a self-organising mapping technique [72] or Mahalanobis squared distance [70]. An artificial neural network (ANN) based method was applied in [70] for damage classification and it was able to outperform OA for damage detection with just one feature. However, for implementing an ANN based approach, data from the structure with known types and levels of damage is required.…”

Section: Damage Detection Methodologiesmentioning

confidence: 99%

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Pipeline Health Monitoring to Optimise Plant Efficiency

Dhutti¹,

Gan²

2019

Power Plants in the Industry

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This chapter presents technological innovations that support asset integrity management-a crucial activity for optimising plant efficiency. In ageing thermal and geothermal power plants, critical assets such as steam piping are subject to high pressures and temperatures that accelerate damage mechanisms. Traditionally, the critical locations of these assets undergo routine inspection which is both costly and time consuming and affects the plant reliability and energy availability. There is an increasing trend in the application of non-destructive testing (NDT) and information technologies to in-service monitoring of these assets. The aim of this chapter is to provide a comprehensive overview of the state-of-the-art monitoring technologies for steamlines, with a focus on high temperature ultrasonic guided wave techniques. The enabling technologies, which include high temperature sensors, diagnostic data analysis algorithms and their monitoring performances, are reviewed. These technological advancements enable inspection without interruption of plant operations, and provide diagnosis and prognosis data for condition-based maintenance, increasing plant safety and its operational efficiency.

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Section: Damage Detection Methodologiesmentioning

confidence: 99%

Section: Damage Detection Methodologiesmentioning

confidence: 99%

Section: Damage Detection Methodologiesmentioning

confidence: 99%

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Pipeline Health Monitoring to Optimise Plant Efficiency

Dhutti¹,

Gan²

2019

Power Plants in the Industry

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“…The framework of OA is available in classical statistical textbooks 20,21 while its first application to diagnose structural damage is attributed to Worden 22 who detected the reduced stiffness of a simulated lumpedmass system using the dynamic transmissibility function associated with vibration-related data. Despite the vast literature on OA applied to vibration data, [22][23][24][25][26][27][28] infrared images, 29 electromechanical impedance signatures, 30,31 and guided ultrasonic waves, [32][33][34][35][36][37][38][39] and the recent studies about the application of HNSWs for SHM and NDE, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] there are no scientific studies about the use of unsupervised learning algorithms to process solitary waves data. The work presented in this article aims at filling this lack of knowledge by proposing a new monitoring paradigm that can be divided into three steps.…”

Section: Introductionmentioning

confidence: 99%

Outlier analysis of nonlinear solitary waves for health monitoring applications

Zheng

Rizzo

Nasrollahi

2019

Structural Health Monitoring

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The structural health monitoring/nondestructive evaluation method based on the generation and detection of highly nonlinear solitary waves is emerging as a cost-effective technique to monitor or inspect a variety of structures and materials. These waves possess unique characteristics not seen in conventional ultrasounds. Outlier analysis is a statistic tool able to identify anomalies in data that diverge from a set of baseline data. Although outlier analysis has received considerable attention for defect detection using modal data, guided ultrasonic waves, or other nondestructive approaches, its application for the analysis of solitary waves has never been explored. In the study presented in this article, the use of outlier analysis in terms of discordancy test and Mahalanobis squared distance was investigated to enhance the damage detection capability of a monitoring system based on highly nonlinear solitary waves. Two experiments were performed to demonstrate the procedure. In the first experiment, a thick steel plate was probed with a solitary wave transducer placed above the plate, and damage was simulated in terms of a foreign object magnetically attached to the bottom of the plate, at different distances from the transducer. In the second experiment, two aluminum plates were placed above each other in dry contact with the top plate subjected to localized, mostly hidden, defects. The transducer used in the first experiment was in this second test encased in a small cart with wheels to scan the sample at discrete positions. For both experiments, a few features were extracted from the time waveforms and fed to a univariate and a multivariate analysis that compared the testing data to a set of baseline data. The results show that the outlier analysis significantly improves the ability to detect damage using solitary waves.

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“…[26][27][28][29] In the study presented in this article, we replaced the laser with a focused transducer to generate tone bursts centered at 500 kHz. The system consists of a pulsed laser or a focused immersion transducer to insonify the waveguide and an array of immersion ultrasonic transducers (IUTs) to sense leaky Lamb waves.…”

Section: Introductionmentioning

confidence: 99%

Guided ultrasonic wave testing of an immersed plate with hidden defects

Bagheri

Rizzo

2015

Opt. Eng

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This paper presents the results of an experimental study in which guided ultrasonic waves are used for the contactless nondestructive testing of a plate immersed in water. In the experiment, narrowband leaky Lamb waves are generated using a focused transducer and are detected with an array of five immersion sensors arranged in a semicircle. The ultrasonic signals are processed to extract a few damage-sensitive features from the time and frequency domains. These features are then fed to an artificial neural network to identify the presence of hidden defects, i.e., defects devised on the surface of the plate not facing the probing system. We find that the noncontact inspection system and the signal processing technique enable the classification of the plate health with a success rate >75%. Bagheri and Rizzo: Guided ultrasonic wave testing of an immersed plate with hidden defects Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/16/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 011003-5 January 2016 • Vol. 55(1) Bagheri and Rizzo: Guided ultrasonic wave testing of an immersed plate with hidden defects Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/16/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 011003-7 January 2016 • Vol. 55(1) Bagheri and Rizzo: Guided ultrasonic wave testing of an immersed plate with hidden defects Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/16/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 011003-10 January 2016 • Vol. 55(1) Bagheri and Rizzo: Guided ultrasonic wave testing of an immersed plate with hidden defects Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/16/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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Outlier Analysis and Artificial Neural Network for the Noncontact Nondestructive Evaluation of Immersed Plates

Cited by 15 publications

References 45 publications

Pipeline Health Monitoring to Optimise Plant Efficiency

Pipeline Health Monitoring to Optimise Plant Efficiency

Outlier analysis of nonlinear solitary waves for health monitoring applications

Guided ultrasonic wave testing of an immersed plate with hidden defects

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