Health Condition Monitoring and Fault-Tolerant Operation of Adjustable Speed Drives

Adjustable speed drives (ASDs) have been extensively used in industrial applications over the past few decades because of their benefits of energy saving and control flexibilities. However, the wider penetration of ASD systems into industrial applications is hindered by the lack of health monitoring and fault-tolerant operation techniques, especially in safety-critical applications. In this dissertation, a comprehensive portfolio of health condition monitoring and fault-tolerant operation strategies is developed and implemented for multilevel neutral-point-clamped (NPC) power converters in ASDs. Simulations and experiments show that these techniques can improve power cycling lifetime of power transistors, on-line diagnosis of switch faults, and fault-tolerant capabilities.The first contribution of this dissertation is the development of a lifetime improvement Pulse Width Modulation (PWM) method which can significantly extend the power cycling lifetime of Insulated Gate Bipolar Transistors (IGBTs) in NPC inverters operating at low frequencies. This PWM method is achieved by injecting a zero-sequence signal with a frequency higher than that of the IGBT junction-to-case thermal time constants. This, in turn, lowers IGBT junction temperatures at low output frequencies. Thermal models, simulation and experimental verifications are carried out to confirm the effectiveness of this PWM method. As a second contribution of this dissertation, a novel on-line diagnostic method is developed for electronic switch faults in power converters. Targeted at three-level NPC converters, this diagnostic method can diagnose any IGBT faults by utilizing the information on the dc-bus neutral-point current and switching states. This diagnostic method only requires one additional current sensor for sensing the neutral-point current. Simulation and experimental results verified the efficacy of this diagnostic method.The third contribution consists of the development and implementation of a fault-tolerant topology for T-Type NPC power converters. In this fault-tolerant topology, one additional phase leg is added to the original T-Type NPC converter. In addition to providing a fault-tolerant solution to certain switch faults in the converter, this fault-tolerant topology can share the overload current with the original phase legs, thus increasing the overload capabilities of the power converters. A lab-scale 30-kVA ASD based on this proposed topology is implemented and the experimental results verified its benefits

Novel pattern recognition methods for classification and detection in remote sensing and power generation applications

Novel pattern recognition methods for classification and detection in remote sensing and power generation application

Private Graph Data Release: A Survey

The application of graph analytics to various domains have yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect private information in graph databases, especially in light of the many privacy breaches in real-world graph data that was supposed to preserve sensitive information. This paper provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific focus on provably private mechanisms. Many of these mechanisms fall under natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that can deal with the limitations of Differential Privacy. A wide-ranging survey of the applications of private graph data release mechanisms to social networks, finance, supply chain, health and energy is also provided. This survey paper and the taxonomy it provides should benefit practitioners and researchers alike in the increasingly important area of private graph data release and analysis

Responsive Economic Model Predictive Control for Next-Generation Manufacturing

There is an increasing push to make automated systems capable of carrying out tasks which humans perform, such as driving, speech recognition, and anomaly detection. Automated systems, therefore, are increasingly required to respond to unexpected conditions. Two types of unexpected conditions of relevance in the chemical process industries are anomalous conditions and the responses of operators and engineers to controller behavior. Enhancing responsiveness of an advanced control design known as economic model predictive control (EMPC) (which uses predictions of future process behavior to determine an economically optimal manner in which to operate a process) to unexpected conditions of these types would advance the move toward artificial intelligence properties for this controller beyond those which it has today and would provide new thoughts on interpretability and verification for the controller. This work provides theoretical studies which relate nonlinear systems considerations for EMPC to these higher-level concepts using two ideas for EMPC formulations motivated by specific situations related to self-modification of a control design after human perceptions of the process response are received and to controller handling of anomalies

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace