Using a high fidelity CCGT simulator for building prognostic systems

Pressure to reduce maintenance costs in power utilities has resulted in growing interest in prognostic monitoring systems. Accurate prediction of the occurrence of faults and failures would result not only in improved system maintenance schedules but also in improved availability and system efficiency. The desire for such a system has driven research into the emerging field of prognostics for complex systems. At the same time there is a general move towards implementing high fidelity simulators of complex systems especially within the power generation field, with the nuclear power industry taking the lead. Whilst the simulators mainly function in a training capacity, the high fidelity of the simulations can also allow representative data to be gathered. Using simulators in this way enables systems and components to be damaged, run to failure and reset all without cost or danger to personnel as well as allowing fault scenarios to be run faster than real time. Consequently, this allows failure data to be gathered which is normally otherwise unavailable or limited, enabling analysis and research of fault progression in critical and high value systems. This paper presents a case study of utilising a high fidelity industrial Combined Cycle Gas Turbine (CCGT) simulator to generate fault data, and shows how this can be employed to build a prognostic system. Advantages and disadvantages of this approach are discussed

Establishing Drosophila as a model to study the functional relevance of conserved heart genes

Background/Aims. Understanding the fundamental mechanisms underlying the development of congenital heart disease and cardiomyopathies is a goal of researchers worldwide, with the ultimate goal being the establishment of effective therapeutics for the amelioration of cardiac dysfunction. Unfortunately these disorders are often polygenic in aetiology, making it difficult for researchers to probe complex interactions that may contribute to the severity of the disease. Over the last decade, the adult fruit fly (Drosophila melanogaster) has emerged as an invaluable tool with which to study the genetic and molecular mechanisms underlying heart function. The aim of my thesis research was to establish the adult fruit fly as a model of human heart function, and to exploit this powerful genetic system to screen for conserved genes affecting the development and function of its cardiac syncytium. Methodology/Results Baseline measures of heart function and other factors contributing to variability in heart function (i.e. age, temperature, and the time of day) were assessed to establish the adult Drosophila heart model. I then performed an a priori RNAi screen, knocking down expression of individual conserved genes via cardiomyocyte-specific overexpression utilising the yeast GAL4/UAS system. Heart-specific ablation of Fermitin 1 and Fermitin 2 (Fit1, Fit2), the two Drosophila orthologs of Kindlin 2 (Kind2, a gene thought to be important for cardiomyocyte-cardiomyocyte junction integrity in human myocardium), caused severe cardiomyopathy characterised by the failure of cardiomyocytes to develop as a functional syncytium and loss of synchrony between cardiomyocytes. I generated a null allele of Fit1 via P-element mobilisation, but this had no impact on heart development or function. Similarly, the silencing of Fit2 failed to affect heart development or function. In contrast, the silencing of Fit2 in the cardiomyocytes of Fit1-null flies disrupted syncytium development, leading to severe cardiomyopathy. Temperature-sensitive cardiac-specific GAL4/GAL80ts lines were also generated, and knockdown of Fit (Fit1 and Fit2) function at different developmental stages was assessed. I observed the strongest effects of Fit knockdown on adult cardiac morphology during stages of heart development and remodelling, with significant cardiomyocyte decoupling. After 3-weeks of Fit knockdown during adulthood, cardiomyocytes were significantly decoupled, and these hearts were significantly arrhythmic compared to control animals. Conclusions/Discussion. My data provide clarity about the role of Kind2 by demonstrating a cell autonomous role for this family in the development of a functional cardiac syncytium in Drosophila. My findings also show that the Fermitins can functionally compensate for each other in order to control syncytium development. Therefore, my thesis demonstrates the power of the fruit fly as a model of human cardiac physiology, and supports the concept that abnormalities in cardiomyocyte KIND2 expression or function may contribute to cardiomyopathies in humans

Selecting appropriate machine learning classifiers for DGA diagnosis

Dissolved gas analysis (DGA) is a common method of assessing transformer health. There are a number of machine learning classifiers reported to give a high accuracy on specific datasets, such as Artificial Neural Networks or Support Vector Machines. When these methods reach the same conclusion about the type of fault present, this can give an increased confidence in the veracity of the diagnosis. However, it is critical to analyze and quantify the strength of these classifiers in the presence of conflicting data to test their practicality for usage in the field. This paper investigates the adequacy of different machine learning based DGA diagnosis models in the presence of conflicting data. The proposed method will aid engineers with the selection of machine learning models so as to maximize the usability and accuracy in the presence of conflicting data

Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence

Transformers are critical assets for the reliable and cost-effective operation of the power grid. Transformers may fail if condition monitoring does not identify degraded conditions in time. Dissolved Gas Analysis (DGA) focuses on the examination of the dissolved gasses in the transformer oil and there exist different methods for transformer fault diagnosis based on different analyses of the gassing levels. However, these methods can give conflicting results, and it is not always clear which model is most accurate in a given situation. This paper presents a novel evidence combination framework for DGA based on Bayesian networks. Bayesian network models embed expert knowledge along with learned data patterns and evidence combination which aids in the consistency of analysis. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset with a maximum diagnosis accuracy of 88.3%

Determining appropriate data analytics for transformer health monitoring

Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring

Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%

Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

Transformers are critical assets for the reliable operation of the power grid. Transformers may fail in service if monitoring models do not identify degraded conditions in time. Dissolved gas analysis (DGA) focuses on the examination of dissolved gasses in transformer oil to diagnose the state of a transformer. Fusion of black-box (BB) classifiers, also known as an ensemble of diagnostics models, have been used to improve the accuracy of diagnostics models across many fields. When independent classifiers diagnose the same fault, this method can increase the veracity of the diagnostics. However, if these methods give conflicting results, it is not always clear which model is most accurate due to their BB nature. In this context, the use of white-box (WB) models can help resolve conflicted samples effectively by incorporating uncertainty information and improve the classification accuracy. This paper presents an uncertainty-aware fusion method to combine BB and WB diagnostics methods. The effectiveness of the proposed approach is validated using two publicly available DGA datasets

Adaptive power transformer lifetime predictions through machine learning and uncertainty modelling in nuclear power plants

The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the non-linearities of the thermal model and improves the prediction accuracy amongst a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a Particle Filtering framework to improve thermal modelling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant

Protein retention in the endoplasmic reticulum rescues Aβ toxicity in Drosophila

Amyloid β (Aβ) accumulation is a hallmark of Alzheimer's disease. In adult Drosophila brains, human Aβ overexpression harms climbing and lifespan. It's uncertain whether Aβ is intrinsically toxic or activates downstream neurodegeneration pathways. Our study uncovers a novel protective role against Aβ toxicity: intra-endoplasmic reticulum (ER) protein accumulation with a focus on laminin and collagen subunits. Despite high Aβ, laminin B1 (LanB1) overexpression robustly counters toxicity, suggesting a potential Aβ resistance mechanism. Other laminin subunits and collagen IV also alleviate Aβ toxicity; combining them with LanB1 augments the effect. Imaging reveals ER retention of LanB1 without altering Aβ secretion. LanB1's rescue function operates independently of the IRE1α/XBP1 ER stress response. ER-targeted GFP overexpression also mitigates Aβ toxicity, highlighting broader ER protein retention advantages. Proof-of-principle tests in murine hippocampal slices using mouse Lamb1 demonstrate ER retention in transduced cells, indicating a conserved mechanism. Though ER protein retention generally harms, it could paradoxically counter neuronal Aβ toxicity, offering a new therapeutic avenue for Alzheimer's disease

p-tau Ser356 is associated with Alzheimer's disease pathology and is lowered in brain slice cultures using the NUAK inhibitor WZ4003

Tau hyperphosphorylation and aggregation is a common feature of many dementia-causing neurodegenerative diseases. Tau can be phosphorylated at up to 85 different sites, and there is increasing interest in whether tau phosphorylation at specific epitopes, by specific kinases, plays an important role in disease progression. The AMP-activated protein kinase (AMPK)-related enzyme NUAK1 has been identified as a potential mediator of tau pathology, whereby NUAK1-mediated phosphorylation of tau at Ser356 prevents the degradation of tau by the proteasome, further exacerbating tau hyperphosphorylation and accumulation. This study provides a detailed characterisation of the association of p-tau Ser356 with progression of Alzheimer's disease pathology, identifying a Braak stage-dependent increase in p-tau Ser356 protein levels and an almost ubiquitous presence in neurofibrillary tangles. We also demonstrate, using sub-diffraction-limit resolution array tomography imaging, that p-tau Ser356 co-localises with synapses in AD postmortem brain tissue, increasing evidence that this form of tau may play important roles in AD progression. To assess the potential impacts of pharmacological NUAK inhibition in an ex vivo system that retains multiple cell types and brain-relevant neuronal architecture, we treated postnatal mouse organotypic brain slice cultures from wildtype or APP/PS1 littermates with the commercially available NUAK1/2 inhibitor WZ4003. Whilst there were no genotype-specific effects, we found that WZ4003 results in a culture-phase-dependent loss of total tau and p-tau Ser356, which corresponds with a reduction in neuronal and synaptic proteins. By contrast, application of WZ4003 to live human brain slice cultures results in a specific lowering of p-tau Ser356, alongside increased neuronal tubulin protein. This work identifies differential responses of postnatal mouse organotypic brain slice cultures and adult human brain slice cultures to NUAK1 inhibition that will be important to consider in future work developing tau-targeting therapeutics for human disease.</p