To solve the problem of data redundancy in the process of state parameter data acquisition of power equipment

and accurately obtain the operation state of power equipment and fault information during operation

the compressed sensing technology is introduced into the state perception of power equipment. In this paper

a sparse dictionary building method with adaptive atomic number is proposed

an establishment method of traditional observation matrix is improved

the sampling at an equal time interval with maximum sparsity is realized

and a method of establishing physiological health curve of equipment state by using sparse sampling data is proposed. Moreover

the method for realizing the fault diagnosis of equipment through pattern recognition and state timing analysis of physiological health curve is discussed. Finally

the application results of a 500 kV substation reactor oil temperature data show that the compressed sensing technology can keep equipment in the complete status under the premise of parametric variation characteristics

and effectively reduce the electric power equipment state parameter data acquisition time ’intensive’. Meanwhile

the data collected under ’time sparsity’ can well identify potential equipment malfunction. The research of this paper gives scientific guidance for the time interval of power equipment status data acquisition

avoids the waste of system resources and cost caused by the blind pursuit of collection time density in practice. Besides

it provides a new technical idea for how to obtain the complete information of equipment state and fault in the absence of data acquisition of equipment state parameters.