Some Recent Developments in SHM Based on Nonstationary Time Series Analysis

Many of the algorithms used for structural health monitoring (SHM) are based on, or motivated by, time series analysis. Quite often, detection methods are variants of approaches developed within the statistical process control (SPC) community. Many of the algorithms used represent mature theory and have a rigorous probabilistic or mathematical basis. However, one of the main issues facing SHM practitioners is that the structures of interest rarely respect the assumptions inherent in deriving algorithms. In the case of time series data, SPC-based approaches usually require the data to be stationary and, unfortunately, SHM data are often nonstationary because of benign variations in the environment of the structure of interest, or because of deliberate operational changes in the use of the structure. This nonstationarity can manifest itself as slowly varying trends on the data or in abrupt switches between regimes. Recent work in nonstationary time series methods for SHM has made considerable progress in accommodating nonstationarity and some of that work is discussed within this paper: in terms of understanding slowly varying trends, the cointegration algorithm from econometrics is presented; for understanding abrupt switches, Bayesian mixtures of experts are presented. Another issue in time series analysis is indirectly related to the assumption of linear behavior of structures and the impact of this assumption is briefly considered in terms of its effects on detection thresholds in SPC-like methods; again, progress has been made recently. Some issues still remain, and these are discussed also

Prediction of landing gear loads using machine learning techniques

This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction

Long-term monitoring and data analysis of the Tamar Bridge

Author's manuscript version. the version of record is available from the publisher via: doi:10.1016/j.ymssp.2012.08.026. Copyright © 2012 Elsevier Ltd. All rights reserved.A sound understanding of a structure’s normal condition, including its response to normal environmental and operational variations is desirable for structural health monitoring and necessary for performance monitoring of civil structures. The current paper outlines the extensive monitoring campaign of the Tamar Suspension Bridge as well as analysis carried out in an attempt to understand the bridge’s normal condition. Specifically the effects of temperature, traffic loading and wind speed on the structure’s dynamic response are investigated. Finally, initial steps towards the development of a structural health monitoring system for the Tamar Bridge are addressed. © 2012 Elsevier Ltd. All rights reserved

Oesophageal atresia is correctable and survivable in infants less than 1 kg

INTRODUCTION: Management of oesophageal atresia (OA) and trachea-oesophageal fistula (TOF) in babies of low birth weight is challenging especially when associated with other anomalies. Birth weight of <1500 g has previously formed part of a classification system designed to predict outcome, alongside the cardiac status of the patient. Improvements in neonatal care have led to increasing numbers of premature low birth weight infants surviving. The aim of this study was to look at the experience of our institution in the extremely low birth weight (ELBW) patients. METHODS: A retrospective review of our institutions OA database was performed from 1993 to June 2015. Patients of birth weight less than 1000 g were included. A review of our OA/TOF clinical database and notes review established the following; gestation, birth weight, associated anomalies, operative procedures, morbidity and mortality. RESULTS: Of 349 patients with OA across the 22-year period, 9 ELBW patients were identified (<1000 g). Six males and three females. Gestational age ranged from 23 to 34 weeks and median birth weight was 815 g ranging from 630 to 950 g. Overall survival was 56 % (5/9). There were double the numbers of ELBW OA/TOF patients seen in the second half of the study period presumably the result of improving neonatal care. Seven patients had type C OA with TOF and underwent emergency TOF ligation, two had concomitant oesophageal repair. One of these patients died from NEC; the other survived. Of the five who had isolated TOF ligation three died-two from cardiac disease and one from prematurity. Both type A patients survived and after initial gastrostomy placement one had a primary delayed repair, the other a gastric transposition. All three babies under 800 g died-one from cardiac disease the others from conditions indicative of their prematurity-necrotising enterocolitis and intraventricular haemorrhage. CONCLUSIONS: 50 % survival is achievable in OA/TOF under 1 kg and the Spitz classification is still applicable in this group as a whole. However, none of the current classification systems are applicable in infants <800 g who in our study all had poor outcomes. We suggest these should be considered as separate group when predicting outcomes

A brief introduction to recent developments in population-based structural health monitoring

This is the final version. Available from the publisher via the DOI in this record.One of the main problems in data-based Structural Health Monitoring (SHM), is the scarcity of measured data corresponding to damage states in the structures of interest. One approach to solving this problem is to develop methods of transferring health inferences and information between structures in an identified population—Population-based SHM (PBSHM). In the case of homogenous populations (sets of nominally-identical structures, like in a wind farm), the idea of the form has been proposed which encodes information about the ideal or typical structure together with information about variations across the population. In the case of sets of disparate structures—heterogeneous populations—transfer learning appears to be a powerful tool for sharing inferences, and is also applicable in the homogenous case. In order to assess the likelihood of transference being meaningful, it has proved useful to develop an abstract representation framework for spaces of structures, so that similarities between structures can formally be assessed; this framework exploits tools from graph theory. The current paper discusses all of these very recent developments and provides illustrative examplesEngineering and Physical Sciences Research Council (EPSRC

Aspects of structural health and condition monitoring of offshore wind turbines

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector

Characterisation and expression of SPLUNC2, the human orthologue of rodent parotid secretory protein

We recently described the Palate Lung Nasal Clone (PLUNC) family of proteins as an extended group of proteins expressed in the upper airways, nose and mouth. Little is known about these proteins, but they are secreted into the airway and nasal lining fluids and saliva where, due to their structural similarity with lipopolysaccharide-binding protein and bactericidal/permeability-increasing protein, they may play a role in the innate immune defence. We now describe the generation and characterisation of novel affinity-purified antibodies to SPLUNC2, and use them to determine the expression of this, the major salivary gland PLUNC. Western blotting showed that the antibodies identified a number of distinct protein bands in saliva, whilst immunohistochemical analysis demonstrated protein expression in serous cells of the major salivary glands and in the ductal lumens as well as in cells of minor mucosal glands. Antibodies directed against distinct epitopes of the protein yielded different staining patterns in both minor and major salivary glands. Using RT-PCR of tissues from the oral cavity, coupled with EST analysis, we showed that the gene undergoes alternative splicing using two 5' non-coding exons, suggesting that the gene is regulated by alternative promoters. Comprehensive RACE analysis using salivary gland RNA as template failed to identify any additional exons. Analysis of saliva showed that SPLUNC2 is subject to N-glycosylation. Thus, our study shows that multiple SPLUNC2 isoforms are found in the oral cavity and suggest that these proteins may be differentially regulated in distinct tissues where they may function in the innate immune response

Continuation for thin film hydrodynamics and related scalar problems

This chapter illustrates how to apply continuation techniques in the analysis of a particular class of nonlinear kinetic equations that describe the time evolution through transport equations for a single scalar field like a densities or interface profiles of various types. We first systematically introduce these equations as gradient dynamics combining mass-conserving and nonmass-conserving fluxes followed by a discussion of nonvariational amendmends and a brief introduction to their analysis by numerical continuation. The approach is first applied to a number of common examples of variational equations, namely, Allen-Cahn- and Cahn-Hilliard-type equations including certain thin-film equations for partially wetting liquids on homogeneous and heterogeneous substrates as well as Swift-Hohenberg and Phase-Field-Crystal equations. Second we consider nonvariational examples as the Kuramoto-Sivashinsky equation, convective Allen-Cahn and Cahn-Hilliard equations and thin-film equations describing stationary sliding drops and a transversal front instability in a dip-coating. Through the different examples we illustrate how to employ the numerical tools provided by the packages auto07p and pde2path to determine steady, stationary and time-periodic solutions in one and two dimensions and the resulting bifurcation diagrams. The incorporation of boundary conditions and integral side conditions is also discussed as well as problem-specific implementation issues