Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Analysis technique for exceptional points in open quantum systems and QPT analogy for the appearance of irreversibility

We propose an analysis technique for the exceptional points (EPs) occurring in the discrete spectrum of open quantum systems (OQS), using a semi-infinite chain coupled to an endpoint impurity as a prototype. We outline our method to locate the EPs in OQS, further obtaining an eigenvalue expansion in the vicinity of the EPs that gives rise to characteristic exponents. We also report the precise number of EPs occurring in an OQS with a continuum described by a quadratic dispersion curve. In particular, the number of EPs occurring in a bare discrete Hamiltonian of dimension $n_\textrm{D}$ is given by $n_\textrm{D} (n_\textrm{D} - 1)$; if this discrete Hamiltonian is then coupled to continuum (or continua) to form an OQS, the interaction with the continuum generally produces an enlarged discrete solution space that includes a greater number of EPs, specifically $2^{n_\textrm{C}} (n_\textrm{C} + n_\textrm{D}) [2^{n_\textrm{C}} (n_\textrm{C} + n_\textrm{D}) - 1]$, in which $n_\textrm{C}$ is the number of (non-degenerate) continua to which the discrete sector is attached. Finally, we offer a heuristic quantum phase transition analogy for the emergence of the resonance (giving rise to irreversibility via exponential decay) in which the decay width plays the role of the order parameter; the associated critical exponent is then determined by the above eigenvalue expansion.Comment: 16 pages, 7 figure

Results from the First Science Run of the ZEPLIN-III Dark Matter Search Experiment

The ZEPLIN-III experiment in the Palmer Underground Laboratory at Boulby uses a 12kg two-phase xenon time projection chamber to search for the weakly interacting massive particles (WIMPs) that may account for the dark matter of our Galaxy. The detector measures both scintillation and ionisation produced by radiation interacting in the liquid to differentiate between the nuclear recoils expected from WIMPs and the electron recoil background signals down to ~10keV nuclear recoil energy. An analysis of 847kg.days of data acquired between February 27th 2008 and May 20th 2008 has excluded a WIMP-nucleon elastic scattering spin-independent cross-section above 8.1x10(-8)pb at 55GeV/c2 with a 90% confidence limit. It has also demonstrated that the two-phase xenon technique is capable of better discrimination between electron and nuclear recoils at low-energy than previously achieved by other xenon-based experiments.Comment: 12 pages, 17 figure

RBF-Neural network applied to the quality classification of tempered 100Cr6 steel cams by the multi-frequency nondestructive eddy current testing

Producción CientíficaThis article proposes a Radial Basis Function Artificial Neural Network (RBF-ANN) to classify tempered steel cams as correctly or incorrectly treated pieces by using multi-frequency nondestructive eddy current testing. Impedances at five frequencies between 10 kHz and 300 kHz were employed to perform the binary sorting. The ANalysis Of VAriance (ANOVA) test was employed to check the significance of the differences between the impedance samples for the two classification groups. Afterwards, eleven classifiers were implemented and compared with one RBF-ANN classifier: ten linear discriminant analysis classifiers and one Euclidean distance classifier. When employing the proposed RBF-ANN, the best performance was achieved with a precision of 95% and an area under the Receiver Operating Characteristic (ROC) curve of 0.98. The obtained results suggest RBF-ANN classifiers processing multi-frequency impedance data could be employed to classify tempered steel DIN 100Cr6 cams with a better performance than other classical classifiers

JPL bibliography 39-12 - Prerelease for December 1970

Bibliography of technical reports on scientific and engineering studies, December 197

Development of a Non-Invasive On-Chip Interconnect Health Sensing Method Based on Bit Error Rates

Electronic products and systems are widely used in industrial network systems, control devices, and data acquisition devices across many industry sectors. Failures of such electronic systems might lead to unexpected downtime, loss of productivity, additional work for repairs, and delay in product and service development. Thus, developing an appropriate sensing technique is necessary, because it is the first step in system fault diagnosis and prognosis. Many sensing techniques often require external and additional sensing devices, which might disturb system operation and consequently increase operating costs. In this study, we present an on-chip health sensing method for non-destructive and non-invasive interconnect degradation detection. Bit error rate (BER), which represents data integrity during digital signal transmission, was selected to sense interconnect health without connecting external sensing devices. To verify the health sensing performance, corrosion tests were conducted with in situ monitoring of the BER and direct current (DC) resistance. The eye size, extracted from the BER measurement, showed the highest separation between the intact and failed interconnect, as well as a gradual transition, compared with abrupt changes in the DC resistance, during interconnect degradation. These experimental results demonstrate the potential of the proposed sensing method for on-chip interconnect health monitoring applications without disturbing system operation

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection

Index to NASA Tech Briefs, January - June 1967

Technological innovations for January-June 1967, abstracts and subject inde

Quality 4.0 in action: Smart hybrid fault diagnosis system in plaster production

UIDB/00066/2020Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result shows the competency and eligibility of Quality 4.0 in action.publishersversionpublishe