The invention discloses a massive monitoring data based substation equipment fault diagnosis method. The method includes: structuring a process memory matrix D according to related monitoring information of equipment; performing normalization processing on measuring values of measure points according to extreme values of the measuring values; structuring a fault diagnosis model, and determining whether faults occur to the substation equipment or not according to residual errors of input and output monitoring amounts. Compared with an equipment fault diagnosis system based on the neural network technique, the massive monitoring data based substation equipment fault diagnosis method has the advantages that shortcomings that the neural network is directly used for learning when more variables are inputted and long time and massive learning samples are needed resulting in the fact that learning convergence cannot be guaranteed are overcome; meanwhile, the method is a parameter-free modeling method, no functions are assumed for model, and accuracy and rapidness in implementation are achieved.