A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

Worden, Keith; Bull, Larry; Gardner, Paul A.; Gosliga, Julian; Rogers, Timothy J.; Cross, Elizabeth J.; Papatheou, Evangelos; Lin, Weijiang; Dervilis, Nikolaos

doi:10.3389/fbuil.2020.00146

Cited by 27 publications

(13 citation statements)

References 26 publications

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“…In this paper, the PBSHM framework described in Worden et al (2020) , is specifically applied to aerospace structures. There are challenges in describing aircraft geometry that arise from its complexity, that are not found when describing the relatively simple geometries found in bridges, for example.…”

Section: Discussionmentioning

confidence: 99%

“…Such populations would be heterogeneous, but would share commonalities of structure that could be exploited for PBSHM. Foundations for a general theory of PBSHM have begun with the papers: Bull et al (2020) ; Gosliga et al (2020) ; Gardner et al (2020a) , and an overview of the ‘story so far’ was presented in Worden et al (2020) .…”

Section: Introductionmentioning

confidence: 99%

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On the application of population-based structural health monitoring in aerospace engineering

Brennan

Gosliga²,

Gardner³

et al. 2022

Front. Robot. AI

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One of the major obstacles to the widespread uptake of data-based Structural Health Monitoring so far, has been the lack of damage-state data for the (mostly high-value) structures of interest. To address this issue, a methodology for sharing data and models between structures has been developed–Population-Based Structural Health Monitoring (PBSHM). PBSHM works on the principle that, if populations of structures are sufficiently similar, or share sections which can be considered similar, then data and models can be shared between them for use in diagnostic inference. The PBSHM methodology therefore relies on two key components: firstly, identifying whether structures are sufficiently similar for successful transfer of diagnostics; this is achieved by the use of an abstract representation of structures. Secondly, machine learning techniques are exploited to effectively transfer information between the structures in a way that improves damage detection and classification across the whole population. Although PBSHM has been conceived to deal with large and general classes of structures, much of the detailed developments presented so far have concerned bridges; the aim of this paper is to provide similarly detailed discussions in the aerospace context. The overview here will examine data transfer between aircraft components, as well as illustrating how one might construct an abstract representation of a full aircraft.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the application of population-based structural health monitoring in aerospace engineering

Brennan

Gosliga²,

Gardner³

et al. 2022

Front. Robot. AI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, this site dependency can be increased by the feature selection process -the features that are selected are for the training turbine; these might possibly differ for the cross-validation turbine. One possible way to circumvent this would be to have a population-based model (Antoniadou et al, 2015;Worden et al, 2020) which would use the data of both turbines during training. However, for our current study, this would impede the cross-validation as only two turbines are available.…”

Section: Fatigue Rate (Del) Estimationmentioning

confidence: 99%

Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups

et al. 2022

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Abstract. The sustained development over the past decades of the offshore wind industry has seen older wind farms beginning to reach their design lifetime. This has led to a greater interest in wind turbine fatigue, the remaining useful lifetime and lifetime extensions. In an attempt to quantify the progression of fatigue life for offshore wind turbines, also referred to as a fatigue assessment, structural health monitoring (SHM) appears as a valuable contribution. Accurate information from a SHM system can enable informed decisions regarding lifetime extensions. Unfortunately direct measurement of fatigue loads typically revolves around the use of strain gauges, and the installation of strain gauges on all turbines of a given farm is generally not considered economically feasible. However, when we consider that great numbers of data, such as supervisory control and data acquisition (SCADA) and accelerometer data (of cheaper installation than strain gauges), are already being captured, these data might be used to circumvent the lack of direct measurements. It is then highly relevant to know what is the minimal sensor instrumentation required for a proper fatigue assessment. In order to determine this minimal instrumentation, a data-driven methodology is developed for real-world jacket-foundation offshore wind turbines (OWTs). In the current study the availability of high-frequency SCADA (1 Hz) and acceleration data (>1 Hz) as well as regular 10 min SCADA is taken as the starting point. Along these measured values, the current work also investigates the inclusion of an estimate of the quasi-static thrust load using the 1 s SCADA using an artificial neural network (ANN). After data collection all data are transformed to features on a 10 min interval (feature generation). When considering all possible variations a total of 430 features was obtained. To reduce the dimensionality of the problem this work performs a comparative analysis of feature selection algorithms. The features selected by each method are compared and related to the sensors to decide on the most cost-effective instrumentation of the OWT. The variables chosen by the best-performing feature selection algorithm then serve as the input for a second ANN, which estimates the tower fore–aft (FA) bending moment damage equivalent loads (DELs), a valuable metric closely related to fatigue. This approach can then be understood as a two-tier model: the first tier concerns itself with engineering and processing 10 min features, which will serve as an input for the second tier that estimates the DELs. It is this two-tier methodology that is used to assess the performance of eight realistic instrumentation setups (ranging from 10 min SCADA to 1 s SCADA, thrust load and dedicated tower SHM accelerometers). Amongst other findings, it was seen that accelerations are essential for the model's generalization. The best-performing instrumentation setup is looked at in greater depth, with validation results of the tower FA DEL ANN model showing an accuracy of around 1 % (MAE) for the training turbine and below 3 % for other turbines, with a slight underprediction of fatigue rates. Finally, the ANN DEL estimation model – based on two intermediate instrumentation setups (combinations of 1 s SCADA, thrust load, low quality accelerations) – is employed in a farm-wide setting, and the probable causes for outlier behaviour are investigated.

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“…Furthermore, this site-dependency can be increased by the feature selection process -the 495 features that are selected are for the training turbine; these might possibly differ for the cross-validation turbine. One possible way to circumvent this would be to have a population-based model (Antoniadou et al, 2015;Worden et al, 2020) use the data of both turbines during training. However for our current study, this would impede the cross-validation as only two turbines are available.…”

Section: Fatigue Rate (Del) Estimationmentioning

confidence: 99%

Data-driven farm-wide fatigue estimation on jacket foundation OWTs for multiple SHM setups

Santos

Noppe

Weijtjens

et al. 2021

Preprint

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Abstract. The sustained development over the past decades of the offshore wind industry has seen older wind farms beginning to reach their design lifetime. This has led to a greater interest in wind turbine fatigue, the remaining useful lifetime and lifetime extensions. In an attempt to quantify the progression of fatigue life for offshore wind turbines, also referred to as a fatigue assessment, structural health monitoring (SHM) appears as a valuable contribution. Accurate information from a SHM system, can enable informed decisions regarding lifetime extensions. Unfortunately direct measurement of fatigue loads typically revolves around the use of strain gauges and the installation of strain gauges on all turbines of a given farm is generally not considered economically feasible. However, when we consider that great amounts of data, such as Supervisory Control And Data Acquisition (SCADA) and accelerometer data (of cheaper installation than strain gauges), is already being captured, this data might be used to circumvent the lack of direct measurements. It is then highly relevant to know what is the minimal sensor instrumentation required for a proper fatigue assessment. In order to determine this minimal instrumentation, a data-driven methodology is developed for real-world jacket-foundation Offshore Wind Turbines (OWT). Firstly, high-frequent 1s SCADA data is used to train an Artificial Neural Network (ANN) that seeks to estimate the quasi-static thrust load, and able to accurately estimate the thrust load with a Mean Absolute Error (MAE) below 2 %. The thrust load is then, along with 1s SCADA and acceleration data, processed into 10-minute metrics and undergoes a comparative analysis of feature selection algorithms with the goal of performing the most efficient dimensionality reduction possible. The features selected by each method are compared and related to the sensors. The variables chosen by the best-performing feature selection algorithm then serve as the input for a second ANN which estimates the tower fore-aft (FA) bending moment Damage Equivalent Loads (DEL), a valuable metric closely related to fatigue. This approach can then be understood as a two-tier model: the first tier concerns itself with engineering and processing 10 minute features, which will serve as an input for the second tier. It is this two-tier methodology that is used to assess the performance of 8 realistic instrumentation setups (ranging from 10 minute SCADA to 1s SCADA, thrust load and dedicated tower SHM accelerometers). Amongst other findings, it was seen that accelerations are essential for the model's generalization. The best performing instrumentation setup is looked in greater depth, with validation results of the tower FA DEL ANN model show an accuracy of around 1 % (MAE) for the training turbine and below 3 % for other turbines, with a slight underprediction of fatigue rates. Finally, the ANN DEL estimation model – based on a intermediate instrumentation setup (1s SCADA, thrust load, low quality accelerations) – is employed in a farm-wide setting, and the probable causes for outlier behaviour investigated.

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A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

Cited by 27 publications

References 26 publications

On the application of population-based structural health monitoring in aerospace engineering

On the application of population-based structural health monitoring in aerospace engineering

Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups

Data-driven farm-wide fatigue estimation on jacket foundation OWTs for multiple SHM setups

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