Real-time implementation of a sensor validation scheme for a heavy-duty diesel engine

With ultra-low exhaust emissions standards, heavy-duty diesel engines (HDDEs) are dependent upon a myriad of sensors to optimize power output and exhaust emissions. Apart from acquiring and processing sensor signals, engine control modules should also have capabilities to report and compensate for sensors that have failed. The global objective of this research was to develop strategies to enable HDDEs to maintain nominal in-use performance during periods of sensor failures. Specifically, the work explored the creation of a sensor validation scheme to detect, isolate, and accommodate sensor failures in HDDEs. The scheme not only offers onboard diagnostic (OBD) capabilities, but also control of engine performance in the event of sensor failures. The scheme, known as Sensor Failure Detection Isolation and Accommodation (SFDIA), depends on mathematical models for its functionality. Neural approximators served as the modeling tool featuring online adaptive capabilities. The significance of the SFDIA is that it can enhance an engine management system (EMS) capability to control performance under any operating conditions when sensors fail. The SFDIA scheme updates models during the lifetime of an engine under real world, in-use conditions. The central hypothesis for the work was that the SFDIA scheme would allow continuous normal operation of HDDEs under conditions of sensor failures. The SFDIA was tested using the boost pressure, coolant temperature, and fuel pressure sensors to evaluate its performance. The test engine was a 2004 MackRTM MP7-355E (11 L, 355 hp). Experimental work was conducted at the Engine and Emissions Research Laboratory (EERL) at West Virginia University (WVU). Failure modes modeled were abrupt, long-term drift and intermittent failures. During the accommodation phase, the SFDIA restored engine power up to 0.64% to nominal. In addition, oxides of nitrogen (NOx) emissions were maintained at up to 1.41% to nominal

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Modern Telemetry

Telemetry is based on knowledge of various disciplines like Electronics, Measurement, Control and Communication along with their combination. This fact leads to a need of studying and understanding of these principles before the usage of Telemetry on selected problem solving. Spending time is however many times returned in form of obtained data or knowledge which telemetry system can provide. Usage of telemetry can be found in many areas from military through biomedical to real medical applications. Modern way to create a wireless sensors remotely connected to central system with artificial intelligence provide many new, sometimes unusual ways to get a knowledge about remote objects behaviour. This book is intended to present some new up to date accesses to telemetry problems solving by use of new sensors conceptions, new wireless transfer or communication techniques, data collection or processing techniques as well as several real use case scenarios describing model examples. Most of book chapters deals with many real cases of telemetry issues which can be used as a cookbooks for your own telemetry related problems

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Contributions to the development of distributed sensors based on stimulated Brillouin scattering

RESUMEN: El objetivo principal de esta tesis es contribuir al desarrollo y la mejora del rendimiento de los sensores distribuidos basados en la dispersión Brillouin. Durante el desarrollo de este trabajo se han considerado diferentes áreas de mejora. En primer lugar, se han propuesto diversas configuraciones experimentales para superar algunas de las limitaciones típicas que tienen estos sensores, como son los efectos no locales en los sensores BOTDA o la aparición de sub-picos en el espectro de ganancia de Brillouin en sistemas basados en el dominio de frecuencia. Otro objetivo principal de este trabajo es aplicar diferentes enfoques de procesado para resolver problemáticas aún no resueltas, como la discriminación entre las medidas de temperatura y las de deformación obtenidas con los sensores Brillouin. Además, también se han estudiado algunos métodos alternativos al método tradicional basado en la aplicación de ajustes Lorentzianos para estimar el cambio de la frecuencia Brillouin. Finalmente, este trabajo también ha tratado de contribuir a la validación de los conocimientos adquiridos mediante la validación en escenarios reales, como aplicaciones de alta temperatura o detección de fugas en tuberías.ABSTRACT: The main objective of this thesis dissertation is to contribute to the development and improvement in the performance of distributed sensors based on Brillouin scattering. Different areas of improvement have been considered during the development of this work. First of all, various different experimental configurations have been proposed to overcome some traditional limitations of these sensors, such as non-local effects on Brillouin optical time domain analysis (BOTDA) sensors or appearance of sub-peaks on the Brillouin gain measured with systems based on the frequency domain. Another main objective of this work is applying different processing approaches in an attempt to solve open problems such as the discrimination between temperature and strain measurements obtained with Brillouin sensors. Additionally, it would be interesting to provide some faster and alternative methods to estimate the Brillouin shift in comparison to traditional method based on applying Lorentzian fittings. Finally, this work has also tried to contribute to the validation of the acquired knowledge by performing validations in real scenarios, such as high-temperature applications or leakage detection in pipelines.This work has been supported by the funding of the following entities and actions: • Universidad de Cantabria through the research grant Programa de Personal Investigador en Formación Predoctoral and research stays grants in Pamplona, Spain and in Aversa, Italy. • Agencia Estatal de Investigación through research project Sensores fotónicos para seguirdad y protección (TEC2016-76021-C2-2-R). • Ministerio de Economía y Competitividad through research project Sensores de fibra óptica para seguirdad y protección (TEC2013-47264-C2-1-R). • Gobierno de Cantabria through research project Detección de fugas en autovías del agua mediante sensores ópticos (FASO). • Fundación TTI through a research grant Patrocinio de actividades formativas en investigación científica y técnica. • Cost action td1001: Novel and reliable optical fibre sensor systems for future security and safety applications (OFSESA) through a research grant for a short term scientific mission to Aversa, Italy and through two grants for summer schools