The invention discloses a
contactor operation state on-line identification method based on image features and
deep learning. The method comprises the following steps: firstly, collecting vibration and sound signals of a
contactor in different operation states in an action process; then, calculating the characteristic frequency of the
signal in the collision process of the seriously worn contact, and taking the characteristic frequency as an additional frequency component; performing CEEMDAN
decomposition on the vibration and sound signals to obtain a plurality of IMF components; the main frequency of each IMF component is calculated, and IMF components containing additional frequency components are removed; calculating a
correlation coefficient and an energy value between each remaining IMF component and the original
signal; keeping the IMF components of which the correlation coefficients and the energy values are greater than the average value to obtain effective
modal components of the vibration and sound signals; thirdly, optimizing a time interval parameter and an angle
amplification factor of the SDP conversion technology by utilizing an intelligent optimization
algorithm, and generating SDP images by utilizing effective
modal components of the vibration signals and the sound signals according to the optimized parameters, so as to obtain a plurality of SDP images in different operation states; and finally, a
state recognition model is constructed and trained, and the trained model is used for
state recognition. According to the method, the influence of interference components on the effective mode is avoided, the feature expression effect of the SDP image is improved, and the recognition precision is improved.