Abstract: An ectuipment condition monitoring algorithm based on massive data mining was proposed. Industrial equipment has massive historical data and has a lot of multidimensional real-time running data. The proposed algorithm makes adaptive cluster analysis with massive historical healthy data to establish the mathematical models of equipment.The algorithm combines these models and real-time running data to achieve predication data. This algorithm can automatically determine the count of clusters by fully considering actual requests from industrial applications,which solves the problem that traditional clustering algorithms have much spending and low efficiency, and it also guarantees the efficiency in the procedure of regression. Simulation results show that the algorithm can effectively deal with massive data and get real-time predicted values,which realizes equipment condition monitoring.

Key words: Equipment condition, Massive data, Data mining, Pre-Alarm