Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Neural network fault diagnosis system for a diesel-electric locomotive's closed loop excitation control system

In closed loop control systems fault isolation is extremely difficult due to the fact that if feedbacks are corrupted or actuators can’t produce a desired output, a system reacts due to an increase in error between the measured variable and the set input variable, which can cause oscillations. The goal of this project is to develop a fault detection and isolation system for the isolation of faults, which cause oscillatory conditions on a GE Diesel-Electric Locomotive’s excitation control system. The proposed system will illustrate the use of artificial neural networks as a replacement to classical analytical models. The artificial neural network model’s design will be based on model-based dedicated observer theory to isolate sensor, as well as component faults, where observer theory will be utilised to effectively select input-output data configurations for detection of sensor and component faults causing oscillations. Owing to the nature of the locomotive’s data acquisition abilities, the model-based observer design will utilise historical data to design an effective model of the system which will be used to perform offline sampled fault detection. This method is proposed as an alternative to trend checking, data mining, etc. Faults are thus detected through the use of an offline model-based dedicated observer residual generator. With the use of a neural network a number of parameters affect the accuracy of the network where the primary source of ensuring an accurate model is training. The project highlights and experiments with these parameters to ensure an accurate model is trained with the use of the gradient descent training algorithm. The parameters which are considered are learning rate, hidden layer neurons, momentum and data preparation. The project will also provide a literature review on residual evaluation techniques used in practice and describe and evaluate the proposed method to perform residual evaluation for this specific application. The proposed method for residual evaluation was based on two principles, namely the moving average, as well as the simple thresholding techniques. The developed FDI system’s performance was measured against known faults and produced 100% accuracy for the detection and isolation of sensor and components causing oscillatory conditions on the locomotive’s excitation system

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice