Data-Driven Event Identification Using Deep Graph Neural Network and PMU Data

Phasor measurement units (PMUs) are being widely installed on power transmission systems, which provides a unique opportunity to enhance wide-area situational awareness. One essential application is to utilize PMU data for real-time event identification. However, taking full advantage of all PMU data in event identification is still an open problem. Hence, we propose a novel event identification method using multiple PMU measurements and deep graph neural network techniques. Unlike the previous models that rely on data from single PMU and ignore the interactive relationships between different PMUs or use multiple PMUs but determine the functional connectivity manually, our method performs interactive relationship inference in a data-driven manner. To ensure the optimality of the interactive inference procedure, the proposed method learns the interactive graph jointly with the event identification model. Moreover, instead of generating a single statistical graph to represent pair-wise relationships among PMUs during different events, our approach produces different event identification-specific graphs for different power system events, which handles the uncertainty of event location. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. The numerical results validate that our method has higher identification accuracy compared to the existing methods

Autoencoder for clinical data analysis and classification : data imputation, dimensional reduction, and pattern recognition

Over the last decade, research has focused on machine learning and data mining to develop frameworks that can improve data analysis and output performance; to build accurate decision support systems that benefit from real-life datasets. This leads to the field of clinical data analysis, which has attracted a significant amount of interest in the computing, information systems, and medical fields. To create and develop models by machine learning algorithms, there is a need for a particular type of data for the existing algorithms to build an efficient model. Clinical datasets pose several issues that can affect the classification of the dataset: missing values, high dimensionality, and class imbalance. In order to build a framework for mining the data, it is necessary first to preprocess data, by eliminating patients’ records that have too many missing values, imputing missing values, addressing high dimensionality, and classifying the data for decision support.This thesis investigates a real clinical dataset to solve their challenges. Autoencoder is employed as a tool that can compress data mining methodology, by extracting features and classifying data in one model. The first step in data mining methodology is to impute missing values, so several imputation methods are analysed and employed. Then high dimensionality is demonstrated and used to discard irrelevant and redundant features, in order to improve prediction accuracy and reduce computational complexity. Class imbalance is manipulated to investigate the effect on feature selection algorithms and classification algorithms.The first stage of analysis is to investigate the role of the missing values. Results found that techniques based on class separation will outperform other techniques in predictive ability. The next stage is to investigate the high dimensionality and a class imbalance. However it was found a small set of features that can improve the classification performance, the balancing class does not affect the performance as much as imbalance class

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning

NASA Tech Briefs, October 2000

Topics include: special coverage sections on CAD, CAE, and PDM, and, Composites and Plastics, and sections on electronic components and systems, software, test and measurement, mechanics, manufacturing/fabrication, physical sciences, information sciences, book and reports, and special sections of Electronics Tech Briefs and Motion Control Tech Brief

Voyager spacecraft phase B, task D. Volume 7 - Preliminary OSE and MDE Final report

Systems analysis and preliminary requirements for mission dependent and operational support equipment for Voyager Mars spacecraf

Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

Autonomous support for microorganism research in space

A preliminary design for performing on orbit, autonomous research on microorganisms and cultured cells/tissues is presented. An understanding of gravity and its effects on cells is crucial for space exploration as well as for terrestrial applications. The payload is designed to be compatible with the Commercial Experiment Transporter (COMET) launch vehicle, an orbiter middeck locker interface, and with Space Station Freedom. Uplink/downlink capabilities and sample return through controlled reentry are available for all carriers. Autonomous testing activities are preprogrammed with in-flight reprogrammability. Sensors for monitoring temperature, pH, light, gravity levels, vibrations, and radiation are provided for environmental regulation and experimental data collection. Additional experimental data acquisition includes optical density measurement, microscopy, video, and film photography. On-board full data storage capabilities are provided. A fluid transfer mechanism is utilized for inoculation, sampling, and nutrient replenishment of experiment cultures. In addition to payload design, representative experiments were developed to ensure scientific objectives remained compatible with hardware capabilities. The project is defined to provide biological data pertinent to extended duration crewed space flight including crew health issues and development of a Controlled Ecological Life Support System (CELSS). In addition, opportunities are opened for investigations leading to commercial applications of space, such as pharmaceutical development, modeling of terrestrial diseases, and material processing