scholarly journals Detecting changes in real-time data: a user’s guide to optimal detection

2017 ◽  
Vol 375 (2100) ◽  
pp. 20160298 ◽  
Author(s):  
P. Johnson ◽  
J. Moriarty ◽  
G. Peskir
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Systems Engineering ◽  
Sequential Analysis ◽  
Signal To Noise Ratio ◽  
Detection Theory ◽  
Change Point Detection ◽  
Optimal Detection ◽  
Time Data ◽  
Detection Delay

The real-time detection of changes in a noisily observed signal is an important problem in applied science and engineering. The study of parametric optimal detection theory began in the 1930s, motivated by applications in production and defence. Today this theory, which aims to minimize a given measure of detection delay under accuracy constraints, finds applications in domains including radar, sonar, seismic activity, global positioning, psychological testing, quality control, communications and power systems engineering. This paper reviews developments in optimal detection theory and sequential analysis, including sequential hypothesis testing and change-point detection, in both Bayesian and classical (non-Bayesian) settings. For clarity of exposition, we work in discrete time and provide a brief discussion of the continuous time setting, including recent developments using stochastic calculus. Different measures of detection delay are presented, together with the corresponding optimal solutions. We emphasize the important role of the signal-to-noise ratio and discuss both the underlying assumptions and some typical applications for each formulation. This article is part of the themed issue ‘Energy management: flexibility, risk and optimization’.

scholarly journals Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data

2022 ◽  
Vol 12 (2) ◽  
pp. 532
Author(s):  
Jonathan Singh ◽  
Katherine Tant ◽  
Anthony Mulholland ◽  
Charles MacLeod
Keyword(s):  
Real Time ◽  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Scattered Wave ◽  
Training Data ◽  
Grain Structure ◽  
Time Data ◽  
Grain Orientations ◽  
Weld Properties ◽  
Crystallographic Orientations

The ability to reliably detect and characterise defects embedded in austenitic steel welds depends on prior knowledge of microstructural descriptors, such as the orientations of the weld’s locally anisotropic grain structure. These orientations are usually unknown but it has been shown recently that they can be estimated from ultrasonic scattered wave data. However, conventional algorithms used for solving this inverse problem incur a significant computational cost. In this paper, we propose a framework which uses deep neural networks (DNNs) to reconstruct crystallographic orientations in a welded material from ultrasonic travel time data, in real-time. Acquiring the large amount of training data required for DNNs experimentally is practically infeasible for this problem, therefore a model based training approach is investigated instead, where a simple and efficient analytical method for modelling ultrasonic wave travel times through given weld geometries is implemented. The proposed method is validated by testing the trained networks on data arising from sophisticated finite element simulations of wave propagation through weld microstructures. The trained deep neural network predicts grain orientations to within 3° and in near real-time (0.04 s), presenting a significant step towards realising real-time, accurate characterisation of weld microstructures from ultrasonic non-destructive measurements. The subsequent improvement in defect imaging is then demonstrated via use of the DNN predicted crystallographic orientations to correct the delay laws on which the total focusing method imaging algorithm is based. An improvement of up to 5.3 dB in the signal-to-noise ratio is achieved.

Aircraft engines: a proud heritage and an exciting future

2016 ◽  
Vol 120 (1223) ◽  
pp. 131-169 ◽  
Author(s):  
R. Parker ◽  
G. Fedder
Keyword(s):  
Power Systems ◽  
Real Time ◽  
New Products ◽  
Aircraft Engines ◽  
Next Generation ◽  
Time Data ◽  
British Industry ◽  
Real Time Data ◽  
Global Player ◽  
Fierce Competition

SUMMARYThe 150th anniversary of the Royal Aeronautical Society has seen Rolls-Royce become a global player in aerospace and a champion of British industry. Its products vary from the nimble RR300, powering two-seater helicopters, all the way to the 97,000-pound thrust Trent XWB, powering future variants of the Airbus A350, and the MT30, which provides the propulsion for the Royal Navy's new Queen Elizabeth class aircraft carriers. It has built this range of products derived from the vision and innovation of its talented engineers, spurred on by the guiding principles provided by Henry Royce. This has seen it through times of war, hardship, bankruptcy and fierce competition to emerge as the leading manufacturer of aircraft engines and a provider of power across land and sea. Alongside its products, it has developed pioneering services to support its customers, analysing real-time data to improve the reliability and efficiency of its engines. In keeping with its tradition of innovation, the company is continuing to develop new products and services for the next generation of power systems for land, sea and air.

Development and Realization of a Computer Monitoring System Based on a Power Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Qin Qin
Keyword(s):  
Power Systems ◽  
Data Acquisition ◽  
Electric Power ◽  
Real Time ◽  
Large Scale ◽  
Time Data ◽  
Power Enterprise ◽  
Reliable Performance ◽  
On Line ◽  
State Index

Background:: To solve an electric power enterprise for a safe operation of power systems. Methods:: A data acquisition technology based on multi-thread mechanism of data collection technology is proposed; its application may provide data acquisition rate of 1832/min and update cycle of approximately 30 s to ensure efficient and reliable performance on a large-scale electric power real-time data acquisition. Results:: A power state index diagnosis algorithm is designed. Conclusion:: The on-line real-time diagnosis of the current operating state for power system is realized, and its importance is given.

A Study on Developing Cardiac Signals Recording Framework (CARDIF) Using a Reconfigurable Real-Time Embedded Processor

2019 ◽  
Vol 8 (2) ◽  
pp. 31-44
Author(s):  
Uma Arun ◽  
Natarajan Sriraam
Keyword(s):  
Real Time ◽  
Signal To Noise Ratio ◽  
Physiological State ◽  
Parameters Estimation ◽  
Signal Acquisition ◽  
Virtual Instrumentation ◽  
Time Data ◽  
Embedded Processor ◽  
Fidelity Measures ◽  
The Time Domain

Due to recent developments in technology, there is a significant growth in healthcare monitoring systems. The most widely monitored human physiological parameters is electrocardiogram (ECG) which is useful for inferring the physiological state of humans. Most of the existing multi-channel ECG acquisition systems were not accessible in resource-constrained settings. This research study proposes a cardiac signal recording framework (CARDIF) using a reconfigurable input-output real-time embedded processor by employing a virtual instrumentation platform. The signal acquisition was configured using Lab VIEW virtual instrumentation block sets. A graphical user interface (GUI) was developed for real-time data acquisition and visualization. The time domain heart rate variability (HRV) statistics were calculated using CARDIF, and the same were compared with a clinical grade 12-channel ECG system. The quality of the acquired signals obtained from the proposed and standard systems was measured and compared by calculating signal-to-noise ratio (SNR). The proposed CARDIF was evaluated qualitatively by visual inspection by a clinician and quantitatively by fidelity measures and vital parameters estimation. The results are quite promising and can be extended for clinical validations.

scholarly journals Instantaneous Disturbance Index for Power Distribution Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1348
Author(s):  
María Dolores Borrás-Talavera ◽  
Juan Carlos Bravo ◽  
César Álvarez-Arroyo
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Power Quality ◽  
Power Distribution ◽  
Distribution Networks ◽  
Accurate Estimation ◽  
Time Data ◽  
Power Distribution Networks ◽  
Disturbance Index ◽  
The Impact

The stability of power systems is very sensitive to voltage or current variations caused by the discontinuous supply of renewable power feeders. Moreover, the impact of these anomalies varies depending on the sensitivity/resilience of customer and transmission system equipment to those deviations. From any of these points of view, an instantaneous characterization of power quality (PQ) aspects becomes an important task. For this purpose, a wavelet-based power quality indices (PQIs) are introduced in this paper. An instantaneous disturbance index (ITD(t)) and a Global Disturbance Ratio index (GDR) are defined to integrally reflect the PQ level in Power Distribution Networks (PDN) under steady-state and/or transient conditions. With only these two indices it is possible to quantify the effects of non-stationary disturbances with high resolution and precision. These PQIs offer an advantage over other similar because of the suitable choice of mother wavelet function that permits to minimize leakage errors between wavelet levels. The wavelet-based algorithms which give rise to these PQIs can be implemented in smart sensors and used for monitoring purposes in PDN. The applicability of the proposed indices is validated by using a real-time experimental platform. In this emulated power system, signals are generated and real-time data are analyzed by a specifically designed software. The effectiveness of this method of detection and identification of disturbances has been proven by comparing the proposed PQIs with classical indices. The results confirm that the proposed method efficiently extracts the characteristics of each component from the multi-event test signals and thus clearly indicates the combined effect of these events through an accurate estimation of the PQIs.

scholarly journals Data Fusion: A Project Update & Pathway Forward

2021 ◽  
Author(s):  
Salvatore Della Villa ◽  
Robert Steele ◽  
Dongwon Shin ◽  
Sangkeun (Matt) Lee ◽  
Travis Johnston ◽  
...  
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Data Science ◽  
Model Building ◽  
Predictive Analytics ◽  
National Laboratory ◽  
Critical Systems ◽  
Time Data ◽  
Design Data ◽  
Oak Ridge

Abstract At the Turbo Expo 2018: Turbomachinery Conference & Expedition, in Oslo, Norway, an innovative approach for assessing operating and near real-time data from power generating assets with meaningful predictive analytics was presented and discussed. GT2018-75030, entitled; Energy Innovation: A Focus on Power Generation Data Capture & Analytics in a Competitive Market established a challenging objective for the industry: “To advance the notion that the fusion of total plant data, from three primary sources, with the ability to transform, analyze, and act based on integrating subject matter expertise is essential for effectively managing assets for optimum performance and profitability; executing and delivering on the promise of “Big Data” and advanced analytics.” Throughout 2019 and 2020, a team comprised of members from Strategic Power Systems, Inc. ® (SPS), Turbine Logic (TL), and two National Labs; National Energy Technology Laboratory (NETL) and Oak Ridge National Laboratory (ORNL), collaborated on the paper’s hypothesis. The team worked with the support of funding from DOE’s Fossil Energy Program through its HPC4 Materials Program, which provided access to the High-Performance Computing assets at both laboratories. The team brought unique skills, strengths, and capabilities that would serve as the basis for an effective, open, and challenging collaboration. The engineering and data science disciplines that converged on this project provided the back-bone for the unbiased analysis and model building that took place; relying on a unique and up-to-date source of plant operating and design data essential for performing the engineering scope of work. A key objective was to use the data and the modeling to be predictive; to characterize remaining life, expended life, and to determine the “next failure” for critical systems and components. Proof-of-concepts were tested for longer term, data-driven reliability prediction for fleets of power generating assets, near real-time prediction of power plant faults which could lead to imminent failure, and physics-based model prediction of life consumption of critical parts. Each of these pilot scale projects is summarized with key results presented.

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