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Research on intelligent diagnosis method for large-scale ship engine fault in non-deterministic environment

Feng D., Li Y.

Polish Maritime Research

2017

S 3

200--206

Aiming at the problem of inaccurate and time-consuming of the fault diagnosis method for large-scale ship engine, an intelligent diagnosis method for large-scale ship engine fault in non-deterministic environment based on neural network is proposed. First, the possible fault of the engine was analyzed, and the downtime fault of large-scale ship engine and the main fault mode were identified. On this basis, the fault diagnosis model for large-scale ship engine based on neural network is established, and the intelligent diagnosis of engine fault is completed. The experiment proved that the proposed method has high diagnostic accuracy, engine fault diagnosis takes only about 3s, with a higher use value.

Analysis of Markov Boundary Induction in Bayesian Networks: A New View From Matroid Theory

Feng D., Chen F., Xu W.

Fundamenta Informaticae

2011

Vol. 107, nr 4

415-434

Learning Markov boundaries from data without having to learn a Bayesian network first can be viewed as a feature subset selection problem and has received much attention due to its significance in the wide applications of AI techniques. Popular constraint based methods suffer from high computational complexity and are usually unstable in spaces of high dimensionality. We propose a new perspective from matroid theory towards the discovery of Markov boundaries of random variable in the domain, and develop a learning algorithm which guarantees to recover the true Markov boundaries by a greedy learning algorithm. Then we use the precision matrix of the original distribution as a measure of independence to make our algorithm feasible in large scale problems, which is essentially an approximation of the probabilistic relations with Gaussians and can find possible variables in Markov boundaries with low computational complexity. Experimental results on standard Bayesian networks show that our analysis and approximation can efficiently and accurately identify Markov boundaries in complex networks from data.