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Nuclear power plant is a large and complex system, when it fail, a fast and accurate fault diagnosis is needed to ensure a safe and efficient working of nuclear power plant. In this paper, the feasibility of exploiting fault diagnosis system based on fuzzy sets theory in nuclear plant was demonstrated. The genetic algorithm was introduced to optimize the rules of diagnosis, so as to achieve fast fault...
In this paper, a fuzzy model based on genetic programming (GPFM) is proposed to diagnose the fault types of insulation of power transformers. The proposed GPFM algorithm constructs the fuzzy relationship between input and output fuzzy variables by genetic programming algorithms. The parameters of memberships of fuzzy subsets and the fuzzy relationship of system are represented by the GP candidates...
The following topics are dealt with: neural networks; evolutionary computing and genetic algorithms; fuzzy systems and soft computing; particle swarm optimization; artificial life and artificial immune systems; systems biology and neurobiology; support vector machine; rough and fuzzy rough set; knowledge discovery and data mining; kernel methods; supervised & semi-supervised learning; hybrid system;...
A hybrid intelligent fault diagnosis method is presented for the diversity, uncertainty and complexity of device faults. This method integrates respective advantages of fault tree, fuzzy theory, neural networks and genetic algorithms to form a hybrid approach and is applied to fault diagnosis of fan. Experiments show that this method is simple and effective. It can also be applied to other fault diagnosis...
The fuzzy rule sets, which have been widely used in avionic fault diagnosis system, have considerable redundancy that leads to time-consuming faults location process. In this paper, to reduce the redundant rules, a multiple objective genetic algorithm, MOGAII, is used to optimize a fuzzy rule set. The optimization problem with two objectives, the maximization diagnostic capability of the system and...
This paper applies an intelligent technique based on fuzzy-genetic algorithm for automatically detecting failures in aircraft. The fuzzy-genetic algorithm constructs the automatic fault detection system for monitoring aircraft behaviors. Fuzzy-based classifier is employed to estimates the time of occurrence and types of actuator failure. Genetic algorithms are used to generate an optimal fuzzy rule...
The local best solution is often gotten by fuzzy c. But the whole best solution can be gotten by immune genetic algorithm (IGA) effectively. In this paper, a new method that integrates fuzzy c with IGA is put forward for fault diagnosis of power transformer. The method converts the problem about minimum for fuzzy c to the problem about maximum for IGA. From the practice, the new method can diagnose...
This paper combines rough set and genetic algorithm with fuzzy theory to diagnose faults in aluminum electrolysis to save energy. Firstly the author gets the simplest decision table by using the rough set to reduce the initial decision table which is made up of the original data. Because of one of important part in rough set being the reduction of condition attribute so a satisfied result can be got...
Early detection and diagnosis of incipient faults are desired for online condition monitoring and improved operational efficiency of induction motors. In this study, an artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm is developed to detect broken rotor bar and broken connector faults in induction motors. The proposed algorithm uses only one phase...
A genetic programming based fuzzy mapping functions (GPFMF) model is proposed in this paper to diagnose the insulation fault types of power transformers. The proposed GPFMF model constructs the fuzzy relationship between input and output fuzzy variables by genetic programming algorithms. The fuzzy relationship is represented as one of candidates which have the form of tree-like combinations of series...
The paper presents a genetic-based fuzzy clustering algorithm for fault diagnosis in satellite attitude determination system (ADS). The traditional fuzzy c-means(FCM) algorithm is local search techniques that search for the optimum by using a hill-climbing techniques. Thus, it often fail in the search for global optimum. Genetic algorithm is a stochastic global optimization algorithm, their combination...
A fuzzy model based on-line turn fault detection approach for induction motors is presented in this paper. Two T-S fuzzy models are employed to detect turn fault, one is used to estimate the fault severity, the other is used to determine the exact number of fault turns. During fuzzy modeling, a fuzzy clustering algorithm based on similarity assessing is proposed to determine the optimal structure...
Many fault detection algorithms deal with fault signatures that are manifested as step changes. While detection of these step changes can be difficult due to noise and other complicating factors, detecting slowly developing faults is usually even more complicated. Trade-offs between early detection and false positive avoidance are more difficult to establish. Often times, slow drift faults go completely...
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