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An algorithm based on ant colony algorithm for health condition monitoring of aero-engine was put forward. The algorithm conversed the health status classification of aero-engine into solving the clustering-based optimization problem with constrain. Ant colony algorithm based on colony collaboration and learning could solve this clustering problem. The proposed algorithm was applied to monitor health...
Failure of rectifier circuit has the characteristics of latency and complexity, which leads to the difficulty to fault diagnosis for rectifier circuit. A new method of optimizing support vector machine (SVM) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the paper. The experimental object is provided and the six ACO-SVM classifiers are developed...
To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system, a new optimization method for f the fault diagnosis model is proposed based on support vector regression (SVR) and principal components analysis. Utilizing the character that principal components analysis algorithm can keep the discernability of original dataset after reduction, the reduces of the original...
A fault identification with fuzzy C-Mean clustering algorithm based on improved ant colony algorithm (ACA) is presented to avoid local optimization in iterative process of fuzzy C-Mean (FCM) clustering algorithm and the difficulty in fault classification. In the algorithm, the problem of fault identification is translated to a constrained optimized clustering problem. Using heuristic search of colony...
Support vector machine (SVM) which overcomes the drawbacks of neural networks has been widely used for pattern recognition in recent years. A new optimization method for the fault diagnosis model is proposed. To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system, an integrated fault diagnosis model based on support vector regression and principal components...
Failure of power transformer is very complex, so that it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on ant colony-support vector machine (AC-SVM) approach is presented for fault diagnosis of power transformer. The AC-SVM selects kernel function parameter and soft margin constant C penalty parameter of support vector machine...
The objective of this paper is to propose a new system for fault diagnosis of train bearings using PCA and ACO. On the base of the analysis of time and frequency domain statistical features extracted from the vibration signals collected from the bearings, twenty features which were the most sensitive to different working states were chosen as the object of follow-on process. After zero-average and...
Dissolved gas analysis in transformer oil (DGA) is an important method for power transformer insulating diagnosis. Aiming at the problem that fuzzy C-means (FCM) clustering algorithm is likely to fall into local minimum point when being used for dissolved gas analysis, dynamic tunneling algorithm was introduced for its high global optimization performance. Then a FCM clustering algorithm was presented...
The iterative optimization algorithm is a traditional classification method of the pattern recognition. In the iterative optimization algorithm, the primary center of classes is selected by random method. This choice method causes the iterative time increase greatly in the optimization at anaphase. It also has some serious defects which are the selected samples blindly, the presented a local extremum...
Diagnostic ambiguity caused by limited observability of sensors is a significant challenge in real-world diagnostic applications, such as gas turbine engines. Traditional data-driven clustering, classification and fusion techniques based on single fault (class) assumption result in large diagnostic errors. Thus, we solve this problem by diagnosing the inherent ambiguity as multiple faults. The proposed...
With the development of the industry, the machine system is becoming more and more complicated, and more and more difficult to detect the gear faults of such a large and complicated system. The wavelet neural network approach is developed for gear faults diagnosis. The wavelet neural work is trained by the gradient descent optimization algorithm in this paper. The wavelet neural network based on the...
A novel transformer insulation fault diagnosis method is proposed based on a decision tree in this paper. In terms of history samples library of transformer faults, the method applies entropy-based information gain as heuristic information to select test attributes, and uses ID3 algorithm to generate the decision tree. Then, pruning in the tree to eliminate noises, and distilling classification rules...
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