This paper proposes an improved LSTM-based method for asynchronous motor fault diagnosis using the RAdam optimizer. It combines double-peak spectral interpolation and sliding window techniques for harmonic extraction and time-series prediction, enabling more accurate fault detection. Experimental results using real-world motor data demonstrate improved fault detection performance compared to traditional neural networks.