改进的并行fp-growth算法在工业设备故障诊断中的应用研究

Application Research of Improved Parallel Fp-growth Algorithm in Fault Diagnosis of Industrial Equipment

如今,工业设备不断向智能化、大型化发展,伴随着设备故障日益复杂多样,如何快速、准确地诊断故障成为一个难题。通过研究,提出以大数据技术Hadoop为平台,基于兴趣属性列的改进的fp-growth算法作为数据挖掘方法,来实现工业设备的故障诊断。实验以工业齿轮箱为例,首先选取两部分数据分别作为训练数据和测试数据,在预处理阶段对训练数据进行空值处理、维度相关性分析以及抽样离散化数据;其次提出基于兴趣属性列的改进的并行fp-growth算法,从训练数据中挖掘出属性列与故障之间的关联规则;最后通过测试数据验证关联规则,证明了改进方法的可行性。实验结果表明,基于兴趣属性列改进的并行fp-growth算法能够在保证准确率的情况下进行快速故障诊断。

Nowadays,industrial equipment is becoming more and more intelligent and large-scale.Along with the increasingly complex and diverse equipment failures,how to diagnose faults quickly and accurately has become a challenge.Hence,taking big data technology Hadoop as platform,fp-growth is utilized as big data mining method to realize the fault diagnosis of industrial equipment.Taking the industrial gear box as example,firstly,the two parts of data are selected as the training data and the test data respectively.In preprocessing stage,the training data is processed by null value,the correlation analysis of dimension and discretization of data.Secondly,this paper put forward an improved parallel fp-growth algorithm based on interest to mine the association rules between attribute columns and faults by the training data.Finally,the association rules were verified by the test data to prove the feasibility of the improved method.Experiment results show that the proposed interest based improved parallel fp-growth algorithm can perform fault diagnosis efficiently with accuracy.