دانلود رایگان مقاله انگلیسی یک دستگاه یادگیری شبکه بیزی برای خطاهای شارژ کولر سیستم تهویه - الزویر 2018

abstract

An intelligent fault diagnosis network for variable refrigerant flow air conditioning system is proposed in this study. The network is developed under the foundation of bayesian belief network theory, which comprises two main elements: the structure and parameters. The structure obtained by machine learning and experts’ experiences illustrates the relationships among faults and physical variables from the qualitative prospective, and its parameters (including prior probability distribution and conditional distribution) describe the uncertainty between them quantitatively. Once the structure and parameters are determined, the posterior probability distribution which can be used to complete fault diagnosis and isolation will be calculated by some algorithms. In comparison with other fault diagnosis approaches, the proposed approach can make full use of performance information. Moreover, it is more reasonable and precise to express the relationship between faults and variables rather than Boolean variables. Evaluation was conducted on a variable refrigerant flow air conditioning system, which demonstrated that this strategy is effective and efficient.

5. Conclusions

This paper has proposed an intelligent fault diagnosis network for VRF systems using bayesian belief network. Because of its special attribute, the BBN accounts for relationships between variables with probability and distributions. Thus the most important and significant thing for BBN is to obtain the suitable network structure and probability distributions. The subject in this study is VRF system which has seldom been studied. As for its BBN structure, manual setting based on experience and experts’ knowledge and data mining combining feature selection and machine learning are united, which is different from previous researches. On the other hand, the probability distributions are obtained mainly by statistical analysis. Prior probability distributions are often on frequencies in training data set, while the conditional probability tables are calculated by algorithms under some independence assumptions. Besides, the constructed network has been evaluated by test data set. The diagnosis results has evaluated using the part of experiment data, which demonstrates that such a BBN is an excellent tool for fault diagnosis. It is noticeable that it is difficult to separate refrigerant overcharge from normal condition especially when the fault level is not heavy. It is because of the attribute of VRF system. Because there is an accumulator in the system which can accumulate extra refrigerant so that it does not affect performance. But when the surplus exceeds the capacity of accumulator,the fault can be diagnosed. Furthermore, during bayesian inference, the order of input evidence have an impact on fault isolation efficiency in spite of the final posterior probabilities are same.