Abstract: 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 paral-lel 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.

Key words: Fault diagnosis, Fp-growth, Hadoop