The Implementation Scheme of DCS Redundancy Fault-Tolerant in the Process Industry Control

2012 ◽  
Vol 546-547 ◽  
pp. 840-844
Author(s):  
Ping Li Wu
Keyword(s):  
Fault Tolerant ◽  
Process Industry ◽  
Hardware System ◽  
Implementation Scheme ◽  
Redundant Fault ◽  
Very High

Process industry control put very high demands on the reliability of DCS. Application of Redundancy Fault-tolerant Technology is one of the most effective way to improve the reliability of DCS. In this paper, we elaborated on the implementation scheme of DCS redundancy fault-tolerant in process industry control, which includes the configuration of DCS hardware system redundancy, the redundant fault-tolerant realization method of I/O module, etc.

scholarly journals Fault Analysis and Non-Redundant Fault Tolerance in 3-Level Double Conversion UPS Systems Using Finite-Control-Set Model Predictive Control

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2210
Author(s):  
Luís Caseiro ◽  
André Mendes
Keyword(s):  
Fault Tolerance ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Fault Tolerant ◽  
Finite Control ◽  
Control Set ◽  
Corrective Actions ◽  
Uninterruptible Power ◽  
Hardware Reconfiguration ◽  
Redundant Fault

Fault-tolerance is critical in power electronics, especially in Uninterruptible Power Supplies, given their role in protecting critical loads. Hence, it is crucial to develop fault-tolerant techniques to improve the resilience of these systems. This paper proposes a non-redundant fault-tolerant double conversion uninterruptible power supply based on 3-level converters. The proposed solution can correct open-circuit faults in all semiconductors (IGBTs and diodes) of all converters of the system (including the DC-DC converter), ensuring full-rated post-fault operation. This technique leverages the versatility of Finite-Control-Set Model Predictive Control to implement highly specific fault correction. This type of control enables a conditional exclusion of the switching states affected by each fault, allowing the converter to avoid these states when the fault compromises their output but still use them in all other conditions. Three main types of corrective actions are used: predictive controller adaptations, hardware reconfiguration, and DC bus voltage adjustment. However, highly differentiated corrective actions are taken depending on the fault type and location, maximizing post-fault performance in each case. Faults can be corrected simultaneously in all converters, as well as some combinations of multiple faults in the same converter. Experimental results are presented demonstrating the performance of the proposed solution.

Artificial Intelligence Techniques for Modern Energy Applications

2010 ◽  
pp. 1-39 ◽  
Author(s):  
Soteris Kalogirou ◽  
Kostas Metaxiotis ◽  
Adel Mellit
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
High Speed ◽  
Fault Tolerant ◽  
System Modeling ◽  
Social Psychological ◽  
Energy Applications ◽  
Manufacturing Optimization ◽  
Linear Problems ◽  
Very High

Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and nowadays are very popular. They are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and once trained can perform prediction and generalization at very high speed. AI-based systems are being developed and deployed worldwide in a wide variety of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. This chapter presents a review of the main AI techniques such as expert systems, artificial neural networks, genetic algorithms, fuzzy logic and hybrid systems, which combine two or more techniques. It also outlines some applications in the energy sector.

A Fault-Tolerant Deflection Routing for Network-on-Chip

2016 ◽  
Vol 26 (03) ◽  
pp. 1750037 ◽  
Author(s):  
Xiaofeng Zhou ◽  
Lu Liu ◽  
Zhangming Zhu
Keyword(s):  
Fault Tolerant ◽  
Network On Chip ◽  
Deflection Routing ◽  
Core System ◽  
Routing Schemes ◽  
Routing Performance ◽  
Layout Area ◽  
On Chip ◽  
Many Core ◽  
Redundant Fault

Network-on-Chip (NoC) has become a promising design methodology for the modern on-chip communication infrastructure of many-core system. To guarantee the reliability of traffic, effective fault-tolerant scheme is critical to NoC systems. In this paper, we propose a fault-tolerant deflection routing (FTDR) to address faults on links and router by redundancy technique. The proposed FTDR employs backup links and a redundant fault-tolerant unit (FTU) at router-level to sustain the traffic reliability of NoC. Experimental results show that the proposed FTDR yields an improvement of routing performance and fault-tolerant capability over the reported fault-tolerant routing schemes in average flit deflection rate, average packet latency, saturation throughput and reliability by up to 13.5%, 9.8%, 10.6% and 17.5%, respectively. The layout area and power consumption are increased merely 3.5% and 2.6%.

Operational fault-tolerant microcomputer for very high reliability

1983 ◽  
Vol 130 (3) ◽  
pp. 90 ◽  
Author(s):  
Mashkuri Yaacob ◽  
M.G. Hartley ◽  
P.G. Depledge
Keyword(s):  
Fault Tolerant ◽  
High Reliability ◽  
Very High

scholarly journals Design Process of a Triple Redundant Fault Tolerant PMA SynRM

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 76241-76249 ◽  
Author(s):  
Bo Wang ◽  
Jiapeng Hu ◽  
Wei Hua
Keyword(s):  
Design Process ◽  
Fault Tolerant ◽  
Redundant Fault

Design and Realization of High Reliability and Extensible Automatic Power Distribution System for General Aviation Aircraft

2013 ◽  
Vol 677 ◽  
pp. 466-471
Author(s):  
Qin Ling Zhang ◽  
Lei Liu ◽  
Yang Liu
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Fault Tolerant ◽  
High Reliability ◽  
General Aviation ◽  
Power Distribution System ◽  
Implementation Scheme ◽  
Composition Structure ◽  
Independent Distribution ◽  
General Aviation Aircraft

Taking small-sized plan as a representative of general aviation aircraft, a kind of airborne automatic power distribution system architecture based on DSP, FPGA and SSPC is proposed, which can meet the requirements of high reliability and expansibility in airborne platform. Based on the research of distribution function, composition structure and software and hardware design in airborne automatic power distribution system, this paper puts forward corresponding implementation scheme in view of independent distribution control and fault tolerant design, and develops a principle prototype. The experimental results verify that the designed system has achieved expected functional demand.

scholarly journals Bitstream-Based Neural Network for Scalable, Efficient, and Accurate Deep Learning Hardware

2020 ◽  
Vol 14 ◽  
Author(s):  
Hyeonuk Sim ◽  
Jongeun Lee
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Fault Tolerant ◽  
State Of The Art ◽  
The Other ◽  
Stochastic Computing ◽  
Hardware Implementations ◽  
Neuromorphic Hardware ◽  
Very High

While convolutional neural networks (CNNs) continue to renew state-of-the-art performance across many fields of machine learning, their hardware implementations tend to be very costly and inflexible. Neuromorphic hardware, on the other hand, targets higher efficiency but their inference accuracy lags far behind that of CNNs. To bridge the gap between deep learning and neuromorphic computing, we present bitstream-based neural network, which is both efficient and accurate as well as being flexible in terms of arithmetic precision and hardware size. Our bitstream-based neural network (called SC-CNN) is built on top of CNN but inspired by stochastic computing (SC), which uses bitstreams to represent numbers. Being based on CNN, our SC-CNN can be trained with backpropagation, ensuring very high inference accuracy. At the same time our SC-CNN is deterministic, hence repeatable, and is highly accurate and scalable even to large networks. Our experimental results demonstrate that our SC-CNN is highly accurate up to ImageNet-targeting CNNs, and improves efficiency over conventional digital designs ranging through 50–100% in operations-per-area depending on the CNN and the application scenario, while losing <1% in recognition accuracy. In addition, our SC-CNN implementations can be much more fault-tolerant than conventional digital implementations.

Toward Hardware-Redundant, Fault-Tolerant Logic for Nanoelectronics

2005 ◽  
Vol 22 (4) ◽  
pp. 328-339 ◽  
Author(s):  
Jie Han ◽  
Jianbo Gao ◽  
Yan Qi ◽  
P. Jonker ◽  
J.A.B. Fortes
Keyword(s):  
Fault Tolerant ◽  
Redundant Fault
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