## 1 Introduction  
Electric power, oil, natural gas, medical and other fields have extremely stringent requirements on the reliability of power supply systems [1]. The diode-based parallel redundant power supply system without current sharing control has a simple structure, high reliability, low cost, and easy expansion, so it is a mainstream method to improve the reliability of power supply systems. However, the current sharing state significantly impacts the reliability and remaining useful life of these systems. According to the research of Phoenix company, in the case of uneven current, the temperature of a heavy-load power supply will be more than 10 degrees higher than that of a light-load power supply, and the average lifespan will be about 50% lower [2]. On the other hand, the non-current sharing state of the parallel power supply will also affect the power supply parameters of the power supply input terminal, increase the harmonics of the system, and cause the system performance to decline [3]. Therefore, online monitoring of the current sharing status of redundant power supplies can timely find out the unbalanced system load caused by various factors such as installation, temperature drift, and component parameter changes [2]. It plays a significant role in maintaining the redundant power system [4].  
At present, the current sharing state detection methods mainly depend on each branch's current measurement [5], which is based on the resistor or inductor connected in series in the branch [6; 7; 8]. These methods require multiple current sensors and a complex connecting structure to evaluate the detection results. These disadvantages make the current state detection methods challenging to implement. In order to realize the current sharing state estimation under single-point detection, the researchers pioneered research on ripple spectrum analysis of the load terminal voltage [9; 10; 11]. However, this method requires changing the operating mode of the SMPS to encode the current sharing information in the ripple of the output voltage and sometimes even requires that the SMPSwork in discontinuous conduction mode. This change inevitably deteriorates the power supply's output characteristics and design complexity.  
Theoretical studies show that the harmonics of SMPS are related to the conversion efficiency [12; 13]. The conversion efficiency is also different under different load conditions, so the harmonics of the power supply are related to the output power of the power supply [14]. Spectral fingerprints can be formed based on the unique inherent spectral characteristics caused by individual differences in the SMPSs. In parallel mode, the spectrum of each power supply is superimposed on each other, and the spectrum change of the ripple signal at the load end must contain the information of the current of each branch. Therefore, this paper designs a spectral fingerprinting algorithm based on LSTM recurrent neural network to effectively extract the branch's current information in redundant SMPS systems. Experiments on the three-power redundant experimental platform show that this method's estimated error of the branch power supply is less than 10%.