Massive power device condition monitoring data feature extraction and clustering analysis using MapReduce and graph model

Abstract

Effective storage, processing and analyzing of power device condition monitoring data faces enormous challenges. A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform. First, power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform. Then, Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis. Finally, performance tests are performed to compare the execution time between serial program and parallel program. Performance is analyzed from CPU cores consumption, memory utilization and parallel granularity. Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.

Keywords

• clustering analysis
• graph
• feature extraction
• mapreduce
• maxcompute
• power device condition monitoring