Deep residual neural network for EMI event classification using bispectrum representation

This paper presents a novel method for condition monitoring of High Voltage (HV) power plant equipment through analysis of discharge signals. These discharge signals are measured using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to a Bispectrum image representations the problem can be approached as an image classification task. This allows for the novel application of a Deep Residual Neural Network (ResNet) to the classification of HV discharge signals. The network is trained on signals into 9 classes and achieves high classification accuracy in each category, improving upon our previous work on this task

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

A Machine Learning approach for damage detection and localisation in Wind Turbine Gearbox Bearings

Increasing demand for renewable sources requires more cost-effective solutions to mitigate the cost of maintenance and produce more energy. Preventive maintenance is the most normally adopted scheme in industry for maintenance but despite being well accepted has severe limitations. Its inability to intelligently schedule maintenance at the right time and prevent unexpected breakdowns are the main downsides of this approach and consequently leads to several problems such as unnecessary maintenances. This strategy does not justify the additional costs and thereby represents a negative aspect for renewable energy resource companies that try to generate cost-competitive energy. These challenges are progressively leading towards the predictive maintenance approach to overcome these aforementioned issues. Wind Turbine Gearbox Bearings have received a lot of attention due to the high incidence failure rates provoked by the harsh operational and environmental conditions. Current techniques only reach a level one of diagnostics commonly known as the Novelty Detection stage and normally requires the expertise of a skilled operator to interpret data and infer damage from it. A data-driven approach by using Machine Learning methods has been used to tackle the damage detection and location stage in bearing components. The damage location was performed by using non-destructive methods such as the Acoustic Emission technique — these measurements were used as features to locate damage around the bearing component once the damage was detected. The implementation of this stages also led to the exploration of damage generation due to overload defects and proposed a methodology to simulate these defects in bearings — the study of this concept was implemented in a scaled-down experiment where damage detection and localisation was performed. Due to the importance of the implementation of a damage location stage, damage in AE sensors was also explored in this work. Features extracted from impedance curves allowed to train Machine Learning methods to trigger a novelty when a bonding scenario occurred. This ultimately allowed the identification of unhealthy sensors in the network that could potentially generate spurious results in the damage predictions stage

Summary of Research 1994

The views expressed in this report are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.This report contains 359 summaries of research projects which were carried out under funding of the Naval Postgraduate School Research Program. A list of recent publications is also included which consists of conference presentations and publications, books, contributions to books, published journal papers, and technical reports. The research was conducted in the areas of Aeronautics and Astronautics, Computer Science, Electrical and Computer Engineering, Mathematics, Mechanical Engineering, Meteorology, National Security Affairs, Oceanography, Operations Research, Physics, and Systems Management. This also includes research by the Command, Control and Communications (C3) Academic Group, Electronic Warfare Academic Group, Space Systems Academic Group, and the Undersea Warfare Academic Group