朴素贝叶斯分类在仪表故障判断上的应用

Application of Naive Bayesian Classification in Instruments Fault Judgment

为了探讨朴素贝叶斯分类在仪表故障判断领域的应用价值,通过将某核电厂压力表故障的历史信息进行分类汇总,将故障的判断转换成文本分类任务,结合朴素贝叶斯分类算法和自然语言处理建立故障的分类模型,实现对新增故障的准确判断。通过验证,朴素贝叶斯分类模型能够对新增故障进行判断分类。测试中需要进行校验类故障准确率能够达到95%以上,其他类故障准确率高于70%。传统故障判断一般是由人来完成,通过贝叶斯分类模型实现对故障的判断,可减轻人员劳动强度,提高工厂维修自动化水平。

In order to investigate the application value of Naive Bayesian classification in the field of instruments fault judgment.By classifying and summarizing the historical maintenance information of pressure gauge fault in a nuclear power plant,the fault judgment is converted into a text classification task.After that the naive Bayesian classification algorithm and natural language processing are used to establish a diagnosis model to achieve accurate judgment of the new faults.It is proven the model can realize the fault judgment function,the accuracy rate which needs calibration can reach above 95%,while the others are above 70%.Traditional faults judgment is generally completed by human beings,while the bayesian classification model can reduce the labor intensity of personnel and improve the level of maintenance automation.