On the usage of active learning for SHM

The key element of this work is to demonstrate a strategy for using pattern recognition algorithms to investigate correlations between feature variables for Structural Health Monitoring (SHM). The task will take advantage of data from a bridge. An informative chain of artificial intelligence tools will allow an active learning interaction between the unfolded shapes of the manifold of online data by characterising the physical shape between variables. In many data mining and machine learning applications, there is a significant supply of unlabelled data but an important undersupply of labelled data. Semi-supervised active learning, which combines both labelled and unlabelled data can offer serious access to useful information and may be the crucial element in successful decision making, regarding the health of structures

Robust methods for outlier detection and regression for SHM applications.

In this paper, robust statistical methods are presented for the data-based approach to structural health monitoring (SHM). The discussion initially focuses on the high level removal of the ‘masking effect’ of inclusive outliers. Multiple outliers commonly occur when novelty detection in the form of unsupervised learning is utilised as a means of damage diagnosis; then benign variations in the operating or environmental conditions of the structure must be handled very carefully, as it is possible that they can lead to false alarms. It is shown that recent developments in the field of robust regression can provide a means of exploring and visualising SHM data as a tool for exploring the different characteristics of outliers, and removing the effects of benign variations. The paper is not, in any sense, a survey; it is an overview and summary of recent work by the authors

Performance monitoring of a wind turbine using extreme function theory

A power curve relates the power produced by a wind turbine to the wind speed. Usually, such curves are unique to the various types of wind turbines, so that by monitoring the power curves, one may monitor the performance of the turbine itself. Most approaches to monitoring a system or a structure at a basic level, generally aim at differentiating between a normal and an abnormal state. Typically, the normal state is represented by a model, and then abnormal, or extreme data points are identified when they are compared to that model. This comparison is very often done pointwise on scalars in the univariate case, or on vectors, if multivariate features are available. Depending on the actual application, the pointwise approach may be limited, or highly prone to false identifications. This paper presents the use of extreme functions for the performance monitoring of wind turbines. Power curves from an actual wind turbine, are assessed as whole functions, and not individual datapoints, with the help of Gaussian process regression and extreme value distributions, with the ultimate aim of the performance monitoring of the wind turbine at a weekly resolution. The approach is compared to the more conventional pointwise method, and approaches which make use of multivariate features, and is shown to be superior in terms of the number of false identifications, with a significantly lower number of false-positives without sacrificing the sensitivity of the approach

Experimental evaluation of environmental effects on a polymer-coated aluminium structure: a time-series analysis and pattern recognition approach

Temperature variation is an important issue that needs to be considered when trying to develop a reliable Structural Health Monitoring (SHM) strategy. In the case that a data-based approach is chosen for damage detection, environmental fluctuations could be erroneously regarded as an abnormal condition of the structure and could mask the presence of damage. One of the objectives of the current work is to examine a statistical pattern recognition approach for novelty detection under different temperature conditions. A second important issue that could hinder the reliability of a SHM strategy is any kind of nonlinear behaviour, not associated with damage, in a system. For the purposes of this paper, the dynamic behaviour of a polymer-coated aluminium structure with ribs fixed with bolts is examined. The autoregressive parameters are the damage sensitive features and later, it is performed Principal Component Analysis (PCA) for robust novelty detection that takes into account the temperature variation

A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm

The use of offshore wind farms has been growing in recent years. Europe is presenting a geometrically growing interest in exploring and investing in such offshore power plants as the continent's water sites offer impressive wind conditions. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore, their reliability is crucial if they ever need to be fully integrated into the energy arena. This paper presents an analysis of supervisory control and data acquisition (SCADA) extracts from the Lillgrund offshore wind farm for the purposes of monitoring. An advanced and robust machine-learning approach is applied, in order to produce individual and population-based power curves and then predict measurements of the power produced from each wind turbine (WT) from the measurements of the other WTs in the farm. Control charts with robust thresholds calculated from extreme value statistics are successfully applied for the monitoring of the turbines

Effect of intravenous clarithromycin in patients with sepsis, respiratory and multiple organ dysfunction syndrome: a randomized clinical trial.

Clarithromycin may act as immune-regulating treatment in sepsis and acute respiratory dysfunction syndrome. However, clinical evidence remains inconclusive. We aimed to evaluate whether clarithromycin improves 28-day mortality among patients with sepsis, respiratory and multiple organ dysfunction syndrome. We conducted a multicenter, randomized, clinical trial in patients with sepsis. Participants with ratio of partial oxygen pressure to fraction of inspired oxygen less than 200 and more than 3 SOFA points from systems other than the respiratory function were enrolled between December 2017 and September 2019. Patients were randomized to receive 1 gr of clarithromycin or placebo intravenously once daily for 4 consecutive days. The primary endpoint was 28-day all-cause mortality. Secondary outcomes were 90-day mortality; sepsis response (defined as at least 25% decrease in SOFA score by day 7); sepsis recurrence; and differences in peripheral blood cell populations and leukocyte transcriptomics. Fifty-five patients were allocated to each arm. By day 28, 27 (49.1%) patients in the clarithromycin and 25 (45.5%) in the placebo group died (risk difference 3.6% [95% confidence interval (CI) - 15.7 to 22.7]; P = 0.703, adjusted OR 1.03 [95%CI 0.35-3.06]; P = 0.959). There were no statistical differences in 90-day mortality and sepsis response. Clarithromycin was associated with lower incidence of sepsis recurrence (OR 0.21 [95%CI 0.06-0.68]; P = 0.012); significant increase in monocyte HLA-DR expression; expansion of non-classical monocytes; and upregulation of genes involved in cholesterol homeostasis. Serious and non-serious adverse events were equally distributed. Clarithromycin did not reduce mortality among patients with sepsis with respiratory and multiple organ dysfunction. Clarithromycin was associated with lower sepsis recurrence, possibly through a mechanism of immune restoration. Clinical trial registration clinicaltrials.gov identifier NCT03345992 registered 17 November 2017; EudraCT 2017-001056-55

CD4 cell count and the risk of AIDS or death in HIV-Infected adults on combination antiretroviral therapy with a suppressed viral load: a longitudinal cohort study from COHERE.

BACKGROUND: Most adults infected with HIV achieve viral suppression within a year of starting combination antiretroviral therapy (cART). It is important to understand the risk of AIDS events or death for patients with a suppressed viral load. METHODS AND FINDINGS: Using data from the Collaboration of Observational HIV Epidemiological Research Europe (2010 merger), we assessed the risk of a new AIDS-defining event or death in successfully treated patients. We accumulated episodes of viral suppression for each patient while on cART, each episode beginning with the second of two consecutive plasma viral load measurements 500 copies/µl, the first of two consecutive measurements between 50-500 copies/µl, cART interruption or administrative censoring. We used stratified multivariate Cox models to estimate the association between time updated CD4 cell count and a new AIDS event or death or death alone. 75,336 patients contributed 104,265 suppression episodes and were suppressed while on cART for a median 2.7 years. The mortality rate was 4.8 per 1,000 years of viral suppression. A higher CD4 cell count was always associated with a reduced risk of a new AIDS event or death; with a hazard ratio per 100 cells/µl (95% CI) of: 0.35 (0.30-0.40) for counts <200 cells/µl, 0.81 (0.71-0.92) for counts 200 to <350 cells/µl, 0.74 (0.66-0.83) for counts 350 to <500 cells/µl, and 0.96 (0.92-0.99) for counts ≥500 cells/µl. A higher CD4 cell count became even more beneficial over time for patients with CD4 cell counts <200 cells/µl. CONCLUSIONS: Despite the low mortality rate, the risk of a new AIDS event or death follows a CD4 cell count gradient in patients with viral suppression. A higher CD4 cell count was associated with the greatest benefit for patients with a CD4 cell count <200 cells/µl but still some slight benefit for those with a CD4 cell count ≥500 cells/µl

Use of IFN gamma/IL10 Ratio for Stratification of Hydrocortisone Therapy in Patients With Septic Shock

Large clinical trials testing hydrocortisone therapy in septic shock have produced conflicting results. Subgroups may benefit of hydrocortisone treatment depending on their individual immune response. We performed an exploratory analysis of the database from the international randomized controlled clinical trial Corticosteroid Therapy of Septic Shock (CORTICUS) employing machine learning to a panel of 137 variables collected from the Berlin subcohort comprising 83 patients including demographic and clinical measures, organ failure scores, leukocyte counts and levels of circulating cytokines. The identified theranostic marker was validated against data from a cohort of the Hellenic Sepsis Study Group (HSSG) (n = 246), patients enrolled in the clinical trial of Sodium Selenite and Procalcitonin Guided Antimicrobial Therapy in Severe Sepsis (SISPCT, n = 118), and another, smaller clinical trial (Crossover study, n = 20). In addition, in vitro blood culture experiments and in vivo experiments in mouse models were performed to assess biological plausibility. A low serum IFNγ/IL10 ratio predicted increased survival in the hydrocortisone group whereas a high ratio predicted better survival in the placebo group. Using this marker for a decision rule, we applied it to three validation sets and observed the same trend. Experimental studies in vitro revealed that IFNγ/IL10 was negatively associated with the load of (heat inactivated) pathogens in spiked human blood and in septic mouse models. Accordingly, an in silico analysis of published IFNγ and IL10 values in bacteremic and non-bacteremic patients with the Systemic Inflammatory Response Syndrome supported this association between the ratio and pathogen burden. We propose IFNγ/IL10 as a molecular marker supporting the decision to administer hydrocortisone to patients in septic shock. Prospective clinical studies are necessary and standard operating procedures need to be implemented, particularly to define a generic threshold. If confirmed, IFNγ/IL10 may become a suitable theranostic marker for an urging clinical need