Abnormal Analysis of Generator Equipment Based on Time Series
To improve the level of maintenance and management of power generation equipment operation,a fault prediction technology for power generation equipment based on PCA-Informer method is proposed.Firstly,it uses Principal Component Analysis(PCA)algorithm to reduce the feature dimension of time series data.Secondly,the data is inputted into the Encoder in batches,and the Encoder performs distillation operations to extract Long-Range dependencies between long time series inputs.The important features are given higher weights through distillation operation,and a focused Self-Attention Feature Map is generated in the next layer.Finally,the Decoder generates a long sequence output by one-step reaction through a forward process.Experimental results show this method can effectively predict the faults of power generation equipment.
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