An Approach for Detecting Fault Lines in a Small Current Grounding System using Fuzzy Reasoning Spiking Neural P Systems

This paper presents a novel approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural P systems. In this approach, six features of current/voltage signals in a small current grounding system are analyzed by considering transient and steady components, respectively; a fault measure is used to quantify the possibility that a line is faulty; information gain degree is discussed to weight the importance of each of the six features; rough set theory is applied to reduce the features; and finally a fuzzy reasoning spiking neural P system is used to construct fault line detection models. Six cases in a small current grounding system prove the effectiveness of the introduced approach

Information Volume of Mass Function

Given a probability distribution, its corresponding information volume is Shannon entropy. However, how to determine the information volume of a given mass function is still an open issue. Based on Deng entropy, the information volume of mass function is presented in this paper. Given a mass function, the corresponding information volume is larger than its uncertainty measured by Deng entropy. In addition, when the cardinal of the frame of discernment is identical, both the total uncertainty case and the BPA distribution of the maximum Deng entropy have the same information volume. Some numerical examples are illustrated to show the efficiency of the proposed information volume of mass function

Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

Although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. To address the issue, an improved fusing method with evidence distance and belief entropy is proposed. Generally, the goal is to obtain the appropriate weights assigning to different reports. Specifically, the distribution difference between two sensor reports is measured by belief entropy. The diversity degree is presented by the combination of evidence distance and the distribution difference. Then, the weight of each sensor report is determined based on the proposed diversity degree. Finally, we can use Dempster combination rule to make the decision. A real application in fault diagnosis and an example show the efficiency of the proposed method. Compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty

Performer selection in Human Reliability analysis: D numbers approach

Dependence assessment among human errors in human reliability analysis (HRA) is an significant issue. Many previous works discussed the factors influencing the dependence level but failed to discuss how these factors like "similarity of performers" determine the final result. In this paper, the influence of performers on HRA is focused, in addition, a new way of D numbers which is usually used to handle with the multiple criteria decision making (MCDM) problems is introduced as well to determine the optimal performer. Experimental result demonstrates the validity of proposed methods in choosing the best performers with lowest the conditional human error probability (CHEP) under the same circumstance

Fault Diagnosis and Condition Monitoring of Power Electronic Components Using Spread Spectrum Time Domain Reflectometry (SSTDR) and the Concept of Dynamic Safe Operating Area (SOA)

Title from PDF of title page viewed April 1, 2021Dissertation advisors: Faisal Khan and Yong ZengVitaIncludes bibliographical references ( page 117-132)Thesis (Ph.D.)--School of Computing and Engineering and Department of Mathematics and Statistics. University of Missouri--Kansas City, 2021Fault diagnosis and condition monitoring (CM) of power electronic components with a goal of improving system reliability and availability have been one of the major focus areas in the power electronics field in the last decades. Power semiconductor devices such as metal oxide semiconductor field-effect transistor (MOSFET) and insulated-gate bipolar transistor (IGBT) are considered to be the most fragile element of the power electronic systems and their reliability degrades with time due to mechanical and thermo-electrical stresses, which ultimately leads to a complete failure of the overall power conversion systems. Therefore, it is important to know the present state of health (SOH) of the power devices and the remaining useful life (RUL) of a power converter in order to perform preventive scheduled maintenance, which will eventually lead to increased system availability and reduced cost. In conventional practice, device aging and lifetime prediction techniques rely on the estimation of the meantime to failure (MTTF), a value that represents the expected lifespan of a device. MTTF predicts expected lifespan, but cannot adequately predict failures attributed to unusual circumstances or continuous overstress and premature degradation. This inability is due in large part to the fact that it considers the device safe operating area (SOA) or voltage and current ride-through capability to be independent of SOH. However, we experimentally proved that SOA of any semiconductor device goes down with the increased level of aging, and therefore, the probability of occurrence of over-voltage/current situation increases. As a result, the MTTF of the device as well as the overall converter reliability reduces with aging. That said, device degradation can be estimated by accomplishing an accurate online degradation monitoring tool that will determine the dynamic SOA. The correlation between aging and dynamic SOA gives us the useful remaining life of the device or the availability of a circuit. For this monitoring tool, spread spectrum time domain reflectometry (SSTDR) has been proposed and was successfully implemented in live power converters. In SSTDR, a high-frequency sine-modulated pseudo-noise sequence (SMPNS) is sent through the system, and reflections from age-related impedance discontinuities return to the test end where they are analyzed. In the past, SSTDR has been successfully used for device degradation detection in power converters while running at static conditions. However, the rapid variation in impedance throughout the entire live converter circuit caused by the fast-switching operation makes CM more challenging while using SSTDR. The algorithms and techniques developed in this project have overcome this challenge and demonstrated that the SSTDR test data are consistent with the aging of the power devices and do not affect the switching performance of the modulation process even the test signal is applied across the gate-source interface of the power MOSFET. This implies that the SSTDR technique can be integrated with the gate driver module, thereby creating a new platform for an intelligent gate-driver architecture (IGDA) that enables real-time health monitoring of power devices while performing features offered by a commercially available driver. Another application of SSTDR in power electronic systems is the ground fault prediction and detection technique for PV arrays. Protecting PV arrays from ground faults that lead to fire hazards and power loss is imperative to maintaining safe and effective solar power operations. Unlike many standard detection methods, SSTDR does not depend on fault current, therefore, can be implemented for testing ground faults at night or low illumination. However, wide variation in impedance throughout different materials and interconnections makes fault location more challenging than fault detection. This barrier was surmounted by the SSTDR-based fault detection algorithm developed in this project. The proposed algorithm was accounted for any variation in the number of strings, fault resistance, and the number of faults. In addition to its general utility for fault detection, the proposed algorithm can identify the location of multiple faults using only a single measurement point, thereby working as a preventative measure to protect the entire system at a reduced cost. Within the scope of the research work on SSTDR-based fault diagnosis and CM of power electronic components, a cell-level SOH measurement tool has been proposed that utilizes SSTDR to detect the location and aging of individual degraded cells in a large series-parallel connected Li-ion battery pack. This information of cell level SOH along with the respective cell location is critical to calculating the SOH of a battery pack and its remaining useful lifetime since the initial SOH of Li-ion cells varies under different manufacturing processes and operating conditions, causing them to perform inconsistently and thereby affect the performance of the entire battery pack in real-life applications. Unfortunately, today’s BMS considers the SOH of the entire battery pack/cell string as a single SOH and therefore, cannot monitor the SOH at the cell level. A healthy battery string has a specific impedance between the two terminals, and any aged cell in that string will change the impedance value. Since SSTDR can characterize the impedance change in its propagation path along with its location, it can successfully locate the degraded cell in a large battery pack and thereby, can prevent premature failure and catastrophic danger by performing scheduled maintenance.Introduction -- Background study and literature review -- Fundamentals of Spread Spectrum Time Domain Reflectometry (SSTDR): A new method for testing electronics live -- Accelerated aging test bench: design and implementation -- Condition monitoring of power switching in live power switching devices in live power electronic converters using SSTDR -- An irradiance-independent, robust ground-fault detection scheme for PV arrays based on SSTDR -- Detection of degraded/aged cell in a LI-Ion battery pack using SSTDR -- Dynamiv safe operating area (SOA) of power semiconductor devices -- Conclusion and future researc

White Paper 11: Artificial intelligence, robotics & data science

198 p. : 17 cmSIC white paper on Artificial Intelligence, Robotics and Data Science sketches a preliminary roadmap for addressing current R&D challenges associated with automated and autonomous machines. More than 50 research challenges investigated all over Spain by more than 150 experts within CSIC are presented in eight chapters. Chapter One introduces key concepts and tackles the issue of the integration of knowledge (representation), reasoning and learning in the design of artificial entities. Chapter Two analyses challenges associated with the development of theories –and supporting technologies– for modelling the behaviour of autonomous agents. Specifically, it pays attention to the interplay between elements at micro level (individual autonomous agent interactions) with the macro world (the properties we seek in large and complex societies). While Chapter Three discusses the variety of data science applications currently used in all fields of science, paying particular attention to Machine Learning (ML) techniques, Chapter Four presents current development in various areas of robotics. Chapter Five explores the challenges associated with computational cognitive models. Chapter Six pays attention to the ethical, legal, economic and social challenges coming alongside the development of smart systems. Chapter Seven engages with the problem of the environmental sustainability of deploying intelligent systems at large scale. Finally, Chapter Eight deals with the complexity of ensuring the security, safety, resilience and privacy-protection of smart systems against cyber threats.18 EXECUTIVE SUMMARY ARTIFICIAL INTELLIGENCE, ROBOTICS AND DATA SCIENCE Topic Coordinators Sara Degli Esposti ( IPP-CCHS, CSIC ) and Carles Sierra ( IIIA, CSIC ) 18 CHALLENGE 1 INTEGRATING KNOWLEDGE, REASONING AND LEARNING Challenge Coordinators Felip Manyà ( IIIA, CSIC ) and Adrià Colomé ( IRI, CSIC – UPC ) 38 CHALLENGE 2 MULTIAGENT SYSTEMS Challenge Coordinators N. Osman ( IIIA, CSIC ) and D. López ( IFS, CSIC ) 54 CHALLENGE 3 MACHINE LEARNING AND DATA SCIENCE Challenge Coordinators J. J. Ramasco Sukia ( IFISC ) and L. Lloret Iglesias ( IFCA, CSIC ) 80 CHALLENGE 4 INTELLIGENT ROBOTICS Topic Coordinators G. Alenyà ( IRI, CSIC – UPC ) and J. Villagra ( CAR, CSIC ) 100 CHALLENGE 5 COMPUTATIONAL COGNITIVE MODELS Challenge Coordinators M. D. del Castillo ( CAR, CSIC) and M. Schorlemmer ( IIIA, CSIC ) 120 CHALLENGE 6 ETHICAL, LEGAL, ECONOMIC, AND SOCIAL IMPLICATIONS Challenge Coordinators P. Noriega ( IIIA, CSIC ) and T. Ausín ( IFS, CSIC ) 142 CHALLENGE 7 LOW-POWER SUSTAINABLE HARDWARE FOR AI Challenge Coordinators T. Serrano ( IMSE-CNM, CSIC – US ) and A. Oyanguren ( IFIC, CSIC - UV ) 160 CHALLENGE 8 SMART CYBERSECURITY Challenge Coordinators D. Arroyo Guardeño ( ITEFI, CSIC ) and P. Brox Jiménez ( IMSE-CNM, CSIC – US )Peer reviewe

How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression