Fault area detection is one of the important contents of circuit board fault diagnosis. In recent years, a lot of research work has been devoted to explore circuit fault diagnosis methods through theoretical simulation, but the difference between simulation conditions and actual measurement environment reduces the feasibility of such methods in practical applications. Combined with the characteristics of the measured circuit board data, this paper proposes a circuit board fault area detection method based on near-field scanning and time series similarity measurement. This method obtains the electromagnetic radiation data of the circuit board under normal and fault conditions through near-field scanning, and uses the variational mode decomposition (VMD) method to reduce the noise of the original data. After that, the data in the two states are regarded as two types of time series, the improved time series similarity measurement algorithm is used to calculate the distance value of the two types of time series, and the fault area of the circuit board is determined according to the distance value. According to the experimental results of data sets, the similarity measurement algorithm in this paper shows better measurement ability in processing time series than other algorithms, and the classification accuracy of distance value is also 6. 3%, 8. 4% and 4. 2% higher than the three comparison algorithms. At the same time, the consistency between the experimental results of the measured data and the theoretical simulation results verifies the reliability and practicability of the method in this paper. This method provides a new way to realize the circuit board fault diagnosis.