The invention discloses an LSTM-COF-based
power equipment data
anomaly detection method and
system, and relates to the technical field of
power equipment state monitoring, and the method comprises the following steps: collecting historical data, and constructing a three-dimensional
data matrix; predicting equipment parameters at the
extreme temperature through an LSTM model; compressing the features, quantifying the
covariance deviation degree between the features in combination with a correlation abnormal factor
algorithm, and detecting abnormal points; the
system integrates a data collection module, a data preprocessing module, a
data prediction module, a detection model generation module and a
visualization module. According to the method, the multi-dimensional historical operation data of the
power equipment is collected, the equipment parameters in the
extreme temperature environment are predicted by using the LSTM model, the
principal component analysis dimensionality reduction and correlation abnormal factor algorithms are combined, insulation degradation type and
electrical connection type faults can be dynamically identified, the data distribution change is adapted through the
incremental learning mechanism, and the fault diagnosis accuracy is improved. The problems of low high-dimensional
data processing efficiency and poor
anomaly detection adaptability due to manual experience dependence in a traditional method are solved.