9,506 research outputs found
A genetic algorithm approach to designing and modelling of a multi-functional fractal manufacturing layout
A dynamic and optimal shop floor design, modelling and implementation is key to achieving successful Fractal Manufacturing System (FrMS). To build adaptive and fault-tolerant fractal layout, attention is paid to issues of shop floor planning, function layout, determination of capacity level, cell composition planning and flow distances of products. A full fledged FrMS. layout is multi-functional and is capable of producing a variety of products with minimal reconfiguration. This paper is part and a progression of an on-going project whereby Genetic Algorithm (GA) is adopted to design and model a flexible and multi-functional FrMS floor layout. GA is used in the project for modeling and simulation. The design implementation is done using MATLAB. The result is a fault tolerant configuration that self-regulates and adapts to unpredictable changes in the manufacturing environment arising from lead time reduction pressure, inventories, product customization and other challenges of a dynamic and volatile operational environment
Optimal design of a three-phase AFPM for in-wheel electrical traction
Sinusoidally fed permanent magnet synchronous motors (PMSM) fulfill the special features required for traction
motors to be applied in electric vehicles (EV). Among them, axial flux permanent magnet (AFPM) synchronous motors are
especially suited for in-wheel applications. Electric motors used
in such applications must meet two main requirements, i.e. high power density and fault tolerance. This paper deals with the
optimal design of an AFPM for in-wheel applications used to drive an electrical scooter. The single-objective optimization
process carried out in this paper is based on designing the AFPM to obtain an optimized power density while ensuring appropriate fault tolerance requirements. For this purpose a set of analytical
equations are applied to obtain the geometrical, electric and mechanical parameters of the optimized AFPM and several design restrictions are applied to ensure fault tolerance capability. The optimization process is based on a genetic
algorithm and two more constrained nonlinear optimization algorithms in which the objective function is the power density.
Comparisons with available data found in the technical bibliography show the appropriateness of the approach
developed in this work.Postprint (published version
Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system
For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system
A synthesis of logic and bio-inspired techniques in the design of dependable systems
Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
GA-Based fault diagnosis algorithms for distributed systems
Distributed Systems are becoming very popular day-by-day due to their applications in various fields such as electronic automotives, remote environment control like underwater sensor network, K-connected networks. Faults may aect the nodes of the system at any time. So diagnosing the faulty nodes in the distributed system is an worst necessity to make the system more reliable and ecient. This thesis describes about dierent types of faults, system and fault model, those are already in literature. As the evolutionary approaches give optimum outcome than probabilistic approaches, we have developed Genetic algorithm based fault diagnosis algorithm which provides better result than other fault diagnosis algorithms. The GA-based fault diagnosis algorithm has worked upon dierent types of faults like permanent as well as intermittent faults in a K-connected system. Simulation results demonstrate that the proposed Genetic Algorithm Based Permanent Fault Diagnosis Algorithm(GAPFDA) and Genetic Algorithm Based Intermittent Fault Diagnosis Algorithm (GAIFDA) decreases the number of messages transferred and the time needed to diagnose the faulty nodes in a K-connected distributed system. The decrease in CPU time and number of steps are due to the application of supervised mutation in the fault diagnosis algorithms. The
time complexity and message complexity of GAPFDA are analyzed as O(n*P*K*ng) and O(n*K) respectively. The time complexity and message complexity of GAIFDA are O(r*n*P*K*ng) and O(r*n*K) respectively, where ’n’ is the number of nodes, ’P’ is the population size, ’K’ is the connectivity of the network, ’ng’ is the number of generations (steps), ’r’ is the number of rounds. Along with the design of fault diagnosis algorithm of O(r*k) for diagnosing the transient-leading-to-permanent faults in the actuators of a k-fault tolerant Fly-by-wire(FBW) system, an ecient scheduling algorithm has been developed to schedule dierent tasks of a FBW system, here ’r’ denotes the number of rounds. The proposed algorithm for scheduling the task graphs of a multi-rate FBW system demonstrates that, maximization in microcontroller’s execution period reduces
the number of microcontrollers needed for performing diagnosis
A novel genetic algorithm for evolvable hardware
Evolutionary algorithms are used for solving search and optimization problems. A new field in which they are also applied is evolvable hardware, which refers to a self-configurable electronic system. However, evolvable hardware is not widely recognized as a tool for solving real-world applications, because of the scalability problem, which limits the size of the system that may be evolved. In this paper a new genetic algorithm, particularly designed for evolving logic circuits, is presented and tested for its scalability. The proposed algorithm designs and optimizes logic circuits based on a Programmable Logic Array (PLA) structure. Furthermore it allows the evolution of large logic circuits, without the use of any decomposition techniques. The experimental results, based on the evolution of several logic circuits taken from three different benchmarks, prove that the proposed algorithm is very fast, as only a few generations are required to fully evolve the logic circuits. In addition it optimizes the evolved circuits better than the optimization offered by other evolutionary algorithms based on a PLA and FPGA structures
Restart-Based Fault-Tolerance: System Design and Schedulability Analysis
Embedded systems in safety-critical environments are continuously required to
deliver more performance and functionality, while expected to provide verified
safety guarantees. Nonetheless, platform-wide software verification (required
for safety) is often expensive. Therefore, design methods that enable
utilization of components such as real-time operating systems (RTOS), without
requiring their correctness to guarantee safety, is necessary.
In this paper, we propose a design approach to deploy safe-by-design embedded
systems. To attain this goal, we rely on a small core of verified software to
handle faults in applications and RTOS and recover from them while ensuring
that timing constraints of safety-critical tasks are always satisfied. Faults
are detected by monitoring the application timing and fault-recovery is
achieved via full platform restart and software reload, enabled by the short
restart time of embedded systems. Schedulability analysis is used to ensure
that the timing constraints of critical plant control tasks are always
satisfied in spite of faults and consequent restarts. We derive schedulability
results for four restart-tolerant task models. We use a simulator to evaluate
and compare the performance of the considered scheduling models
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