Condition monitoring of fibre ropes using machine learning

The application of fibre ropes in offshore lifting operations has significant potential for further development. With minimum breaking loads (MBL) equivalent to steel wire at similar diameters and almost neutral buoyancy in water, it is in theory possible to reach depths exceeding 3000 m with smaller cranes and vessels, representing substantial savings in not only potential operation costs. However, with fibre ropes there are different requirements and standards to consider with regards to condition monitoring, maintenance and retirement criteria. Safe and reliable operations are paramount in the offshore sector and any incidents that occur during offshore lifting would not be only significantly damaging financially but could potentially lead to loss of life. Current standards for fibre rope condition monitoring originate in mooring applications, and are based on manual inspection for retirement and re-certification. There is significant room for developments in methods that can aid the inspection process. To address this problem, computer vision and thermal monitoring methods for fibre ropes are developed and experimentally investigated at the Mechatronics Innovation Lab in Grimstad, Norway. The methods are used to monitor changes in fibre rope condition during cyclic-bend-over-sheave testing and to find relevant condition indicators that give more information regarding the condition and remaining useful life of the fibre rope. In addition, the data recorded is used to form machine learning models that both classify rope condition and predict the remaining life of fibre ropes during CBOS testing. The expected outcome is to use physics-based machine learning methods to improve both condition classification and remaining useful life estimation of  bre ropes used in offshore lifting operations. In the appended papers at the end of this thesis, the proposed methods have been experimentally investigated and validated through cyclic-bend-over-sheave experiments performed at the Mechatronics Innovation Lab and further data analysis performed at the University of Agder, Norway and at divis in Dortmund, Germany.publishedVersio

Computer vision and thermal monitoring of HMPE fibre rope condition during CBOS testing

Fibre rope usage in deep sea lifting operations is gaining more prominence in recent times. With rope minimum break loads (MBL) comparable to that of their steel wire counterparts, the use of high modulus polyethylene (HMPE) ropes is seen as a viable option for use in subsea construction cranes. The ropes are worn out during use and visual inspection remains one of the main methods of determining whether a fibre rope is to be retired from use, therefore a natural extension is condition monitoring through computer vision. Creep and temperature are constraining with HMPE ropes and should be monitored continuously, particularly when the rope is cyclically bent over sheaves. Additionally, interpreting the thermal history of the rope during use could give insight into deterioration. In this paper, a condition monitoring system based on combined computer vision and thermal monitoring is used during cyclic bend over sheave tests performed on 560 kN break load of 12 strand braided HMPE ropes. New monitoring features such as local length and width through computer vision algorithms combined with surface thermal monitoring and global elongation are presented and their effectiveness as condition monitoring features is assessed.publishedVersio

Condition Monitoring Technologies for Synthetic Fiber Ropes - a Review

This paper presents a review of different condition monitoring technologies for fiber ropes. Specifically, it presents an overview of the articles and patents on the subject, ranging from the early 70’s up until today with the state of the art. Experimental results are also included and discussed in a conditionmonitoring context,where failuremechanisms and changes in physical parameters give improved insight into the degradation process of fiber ropes. From this review, it is found that automatic width measurement has received surprisingly little attention, and might be a future direction for the development of a continuous condition monitoring system for synthetic fiber ropes

A Comparison of Flare Forecasting Methods. III. Systematic Behaviors of Operational Solar Flare Forecasting Systems

A workshop was recently held at Nagoya University (31 October – 02 November 2017), sponsored by the Center for International Collaborative Research, at the Institute for Space-Earth Environmental Research, Nagoya University, Japan, to quantitatively compare the performance of today’s operational solar flare forecasting facilities. Building upon Paper I of this series (Barnes et al. 2016), in Paper II (Leka et al. 2019) we described the participating methods for this latest comparison effort, the evaluation methodology, and presented quantitative comparisons. In this paper we focus on the behavior and performance of the methods when evaluated in the context of broad implementation differences. Acknowledging the short testing interval available and the small number of methods available, we do find that forecast performance: 1) appears to improve by including persistence or prior flare activity, region evolution, and a human “forecaster in the loop”; 2) is hurt by restricting data to disk-center observations; 3) may benefit from long-term statistics, but mostly when then combined with modern data sources and statistical approaches. These trends are arguably weak and must be viewed with numerous caveats, as discussed both here and in Paper II. Following this present work, we present in Paper IV a novel analysis method to evaluate temporal patterns of forecasting errors of both types (i.e., misses and false alarms; Park et al. 2019). Hence, most importantly, with this series of papers we demonstrate the techniques for facilitating comparisons in the interest of establishing performance-positive methodologies

Solar flare prediction using advanced feature extraction, machine learning and feature selection

YesNovel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties

Protocol for investigating genetic determinants of posttraumatic stress disorder in women from the Nurses' Health Study II

<p>Abstract</p> <p>Background</p> <p>One in nine American women will meet criteria for the diagnosis of posttraumatic stress disorder (PTSD) in their lifetime. Although twin studies suggest genetic influences account for substantial variance in PTSD risk, little progress has been made in identifying variants in specific genes that influence liability to this common, debilitating disorder.</p> <p>Methods and design</p> <p>We are using the unique resource of the Nurses Health Study II, a prospective epidemiologic cohort of 68,518 women, to conduct what promises to be the largest candidate gene association study of PTSD to date. The entire cohort will be screened for trauma exposure and PTSD; 3,000 women will be selected for PTSD diagnostic interviews based on the screening data. Our nested case-control study will genotype1000 women who developed PTSD following a history of trauma exposure; 1000 controls will be selected from women who experienced similar traumas but did not develop PTSD.</p> <p>The primary aim of this study is to detect genetic variants that predict the development of PTSD following trauma. We posit inherited vulnerability to PTSD is mediated by genetic variation in three specific neurobiological systems whose alterations are implicated in PTSD etiology: the hypothalamic-pituitary-adrenal axis, the locus coeruleus/noradrenergic system, and the limbic-frontal neuro-circuitry of fear. The secondary, exploratory aim of this study is to dissect genetic influences on PTSD in the broader genetic and environmental context for the candidate genes that show significant association with PTSD in detection analyses. This will involve: conducting conditional tests to identify the causal genetic variant among multiple correlated signals; testing whether the effect of PTSD genetic risk variants is moderated by age of first trauma, trauma type, and trauma severity; and exploring gene-gene interactions using a novel gene-based statistical approach.</p> <p>Discussion</p> <p>Identification of liability genes for PTSD would represent a major advance in understanding the pathophysiology of the disorder. Such understanding could advance the development of new pharmacological agents for PTSD treatment and prevention. Moreover, the addition of PTSD assessment data will make the NHSII cohort an unparalleled resource for future genetic studies of PTSD as well as provide the unique opportunity for the prospective examination of PTSD-disease associations.</p

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Condition monitoring of fibre ropes using machine learning

The application of fibre ropes in offshore lifting operations has significant potential for further development. With minimum breaking loads (MBL) equivalent to steel wire at similar diameters and almost neutral buoyancy in water, it is in theory possible to reach depths exceeding 3000 m with smaller cranes and vessels, representing substantial savings in not only potential operation costs. However, with fibre ropes there are different requirements and standards to consider with regards to condition monitoring, maintenance and retirement criteria. Safe and reliable operations are paramount in the offshore sector and any incidents that occur during offshore lifting would not be only significantly damaging financially but could potentially lead to loss of life. Current standards for fibre rope condition monitoring originate in mooring applications, and are based on manual inspection for retirement and re-certification. There is significant room for developments in methods that can aid the inspection process. To address this problem, computer vision and thermal monitoring methods for fibre ropes are developed and experimentally investigated at the Mechatronics Innovation Lab in Grimstad, Norway. The methods are used to monitor changes in fibre rope condition during cyclic-bend-over-sheave testing and to find relevant condition indicators that give more information regarding the condition and remaining useful life of the fibre rope. In addition, the data recorded is used to form machine learning models that both classify rope condition and predict the remaining life of fibre ropes during CBOS testing. The expected outcome is to use physics-based machine learning methods to improve both condition classification and remaining useful life estimation of  bre ropes used in offshore lifting operations. In the appended papers at the end of this thesis, the proposed methods have been experimentally investigated and validated through cyclic-bend-over-sheave experiments performed at the Mechatronics Innovation Lab and further data analysis performed at the University of Agder, Norway and at divis in Dortmund, Germany

Remaining useful life estimation of HMPE rope during CBOS testing through machine learning

Fibre rope used in cranes for offshore deployment and recovery has significant potential to perform lifts with smaller cranes and vessels to reach depths limited by weight of steel wire rope. Current condition monitoring methods based on manual inspection and time-based and reactive maintenance have significant potential for improvement coupled with more accurate remaining useful life (RUL) prediction. Machine learning has found use as a condition monitoring approach, coupled with vast improvements in data acquisition methods. This paper details data-driven RUL prediction methods based on machine learning algorithms applied on cyclic-bend-over-sheave (CBOS) tests performed on two fibre rope types until failure. Data extracted through computer vision and thermal monitoring is used to predict RUL through neural networks, support vector machines and random forest. Random forest and neural networks methods are shown to be particularly adept at predicting RUL compared to support vector machines . Additionally, improved RUL predictions can be achieved by combining data from distinct rope types subject to different test conditions