Data-driven prognostics for critical electronic assemblies and electromechanical components

The industrial digitalisation enables the adoption of robust, data-driven maintenance strategies that increase safety and reliability of critical assets such as electronics. And yet, an implementation of data-driven methods which primarily address the industrialisation of diagnostic and prognostic strategies is opposed by various, application specific challenges. This thesis collates such restricting factors encountered within the oil and gas industry, in particular for the critical electrical systems and components in upstream deep drilling tools. A fleet-level, tuned machine learning approach is presented that classifies the operational state (no-failure/ failure) of downhole tool printed circuit board assemblies. It supports maintenance decision making under varying levels of failure costs and fleet reliability scenarios. Applied within a maintenance scheme it has the potential to minimise non-productive time while increasing operational reliability. Likewise, a tailored and efficient deep learning data pipeline is proposed for a component-level forecast of the end of life of electromagnetic relays. It is evaluated using high resolution life-cycle data which has been collected as a part of this thesis. In combination with a failure analysis, the proposed method improves the prognostics capabilities compared to traditional methods which have been proposed so far in order to assess the operational health of electromagnetic relays. Two case studies underpin the need for tailored prognostic methods in order to provide viable solutions that can de-risk deep drilling operations. In consequence, the proposed approaches alleviate the pressure on current maintenance strategies which can no longer meet the stringent reliability requirements of upstream assets

AI-driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability

Depressive disorders are associated with increased peripheral blood cell deformability: a cross-sectional case-control study (Mood-Morph)

Pathophysiological landmarks of depressive disorders are chronic low-grade inflammation and elevated glucocorticoid output. Both can potentially interfere with cytoskeleton organization, cell membrane bending and cell function, suggesting altered cell morpho-rheological properties like cell deformability and other cell mechanical features in depressive disorders. We performed a cross-sectional case-control study using the image-based morpho-rheological characterization of unmanipulated blood samples facilitating real-time deformability cytometry (RT-DC). Sixty-nine pre-screened individuals at high risk for depressive disorders and 70 matched healthy controls were included and clinically evaluated by Composite International Diagnostic Interview leading to lifetime and 12-month diagnoses. Facilitating deep learning on blood cell images, major blood cell types were classified and morpho-rheological parameters such as cell size and cell deformability of every individual cell was quantified. We found peripheral blood cells to be more deformable in patients with depressive disorders compared to controls, while cell size was not affected. Lifetime persistent depressive disorder was associated with increased cell deformability in monocytes and neutrophils, while in 12-month persistent depressive disorder erythrocytes deformed more. Lymphocytes were more deformable in 12-month major depressive disorder, while for lifetime major depressive disorder no differences could be identified. After correction for multiple testing, only associations for lifetime persistent depressive disorder remained significant. This is the first study analyzing morpho-rheological properties of entire blood cells and highlighting depressive disorders and in particular persistent depressive disorders to be associated with increased blood cell deformability. While all major blood cells tend to be more deformable, lymphocytes, monocytes, and neutrophils are mostly affected. This indicates that immune cell mechanical changes occur in depressive disorders, which might be predictive of persistent immune response

Hematological analysis of Micropogonias Furnieri, Desmarest, 1823, Scianidae, from two estuaries of Baixada Santista, São paulo Brazil

Alterações hematológicas em peixes são consideradas uma importante ferramenta para avaliar processos patológicos decorrentes da exposição a poluentes ambientais. Micropogonias furnieri (Desmarest, 1823) (corvina) é comumente encontrada em regiões estuarinas e eventualmente está exposta a inúmeros contaminantes. No presente estudo foi avaliado o quadro hematológico de indivíduos de M. furnieri coletados na Baixada Santista: o Sistema Estuarino de Santos, considerado poluído, e o estuário do Rio Itanhaém (controle). Foram avaliados o número de Eritrócitos (Er), o Hematócrito (Ht), a taxa de Hemoglobina (Hb), o Volume Corpuscular Médio (VCM) e a Concentração de Hemoglobina Corpuscular Média (CHCM). Nos peixes coletados no Sistema Estuarino de Santos, os níveis de Ht foram significativamente menores, enquanto os níveis de CHCM e Hb foram significativamente mais altos, indicando que os prováveis efeitos estejam atribuídos aos diferentes níveis de contaminação encontrados nos estuários.Hematological alterations in fish are considered a useful tool to evaluate pathological processes resulting from the exposure to environmental pollutants. The whitemouth croaker Micropogonias furnieri is a common species in estuarine areas and potentially exposed to many contaminants. In the present study, the hematological characteristics of fish collected at two sites in Baixada Santista (Santos Estuarine System - SES, a polluted site; and the Estuary of Itanhaém River - EIR, unpolluted site) del was analysed. The following blood descriptors were analyzed: number of Erythrocytes (Er), Hematocrit (Ht), Hemoglobin (Hb), Mean Corpuscular Volume (MCV) and Mean Corpuscular Hemoglobin concentration (MCHC). Fish from SES exhibited significant lower levels of Ht and increase on MCHC and Hb. Such differences are likely related to the different contamination levels found in these estuaries

Trauma, Attempted Suicide, and Morning Cortisol in a Community Sample of Adolescents

Individuals exposed to trauma or who have attempted suicide may show abnormal cortisol profiles; those exposed to significant trauma show reduced, while those who attempt suicide show increased cortisol output, although the evidence is inconsistent. This study explores the associations between morning cortisol, trauma, and suicide attempts or ideation among young people. In a community-based sample of 501 15-year-olds, using data from a DSM-IV-compatible interview on suicidal-behavior/ideation, trauma, and morning cortisol, we found no association between these factors and morning cortisol. A significant gender interaction was found for those threatened with a weapon—men showing a negative and women a positive association, suggesting that any cortisol/trauma association may be partially explained by coexisting behavioral problems and gender

AI-Driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability

Associations between DSM-IV diagnosis, psychiatric symptoms and morning cortisol levels in a community sample of adolescents

Purpose. Dysfunction of the hypothalamic-pituitary-adrenocortical axis (HPA-axis) is implicated in a variety of psychiatric and emotional disorders. In this study, we explore the association between HPA-axis functioning, as measured by morning cortisol, and common psychiatric disorders and symptoms among a community sample of adolescents. Method. Data from a cross-sectional school-based survey of 501 school pupils, aged 15, were used to establish the strength of association between salivary morning cortisol and both diagnosis of psychiatric disorders and a number of psychiatric symptoms, as measured via a computerised psychiatric interview. Analysis, conducted separately by gender, used multiple regressions, adjusting for relevant confounders. Results-á-áWith one exception (a positive association between conduct disorder symptoms and cortisol among females) there was no association between morning cortisol and psychiatric diagnosis or symptoms. However, there was a significant two-way interaction between gender and conduct symptoms, with females showing a positive and males a negative association between cortisol and conduct symptoms. A further three-way interaction showed that while the association between cortisol and conduct symptoms was negative among males with a few mood disorder symptoms, among females with many mood symptoms it was positive. Conclusions. Except in relation to conduct symptoms, dysregulation of morning cortisol levels seems unrelated to any psychiatric disorder or symptoms. However, the relationship between cortisol and conduct symptoms is moderated by both gender and mood symptoms. Findings are compatible with the recent work suggesting research should concentrate on the moderated associations between gender, internalising and externalising symptoms and cortisol, rather than any simple relationship

The Novel Deacetylase Inhibitor AR-42 Demonstrates Pre-Clinical Activity in B-Cell Malignancies In Vitro and In Vivo

While deacetylase (DAC) inhibitors show promise for the treatment of B-cell malignancies, those introduced to date are weak inhibitors of class I and II DACs or potent inhibitors of class I DAC only, and have shown suboptimal activity or unacceptable toxicities. We therefore investigated the novel DAC inhibitor AR-42 to determine its efficacy in B-cell malignancies.In mantle cell lymphoma (JeKo-1), Burkitt's lymphoma (Raji), and acute lymphoblastic leukemia (697) cell lines, the 48-hr IC(50) (50% growth inhibitory concentration) of AR-42 is 0.61 microM or less. In chronic lymphocytic leukemia (CLL) patient cells, the 48-hr LC(50) (concentration lethal to 50%) of AR-42 is 0.76 microM. AR-42 produces dose- and time-dependent acetylation both of histones and tubulin, and induces caspase-dependent apoptosis that is not reduced in the presence of stromal cells. AR-42 also sensitizes CLL cells to TNF-Related Apoptosis Inducing Ligand (TRAIL), potentially through reduction of c-FLIP. AR-42 significantly reduced leukocyte counts and/or prolonged survival in three separate mouse models of B-cell malignancy without evidence of toxicity.Together, these data demonstrate that AR-42 has in vitro and in vivo efficacy at tolerable doses. These results strongly support upcoming phase I testing of AR-42 in B-cell malignancies

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem\u27s carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.</p