DST-SGR: a Mamba-inspired dual-stream spectro-temporal network for remaining useful life prediction of rotating machinery

Abstract

Introduction Predicting the Remaining Useful Life (RUL) of rolling bearings is essential in predictive maintenance. High-frequency vibration signals present a dual-domain challenge, since impulses are most visible in the time domain while harmonics are most visible in time-frequency analysis. Existing approaches are limited by vanishing gradients in LSTM networks and by the quadratic O(L2) cost of transformer models. Methods We introduce DST-SGR, a Mamba-inspired dual-stream spectro-temporal network in which selective temporal modeling is realized through optimized gated recurrent (GRU-based) state interactions with linear O(L) computational cost in a fully cross-platform PyTorch implementation. A temporal branch detects impulses from raw time-series data while a spectral branch learns harmonic signatures from an STFT spectrogram, and three Spectro-Temporal Interaction Blocks (STIBs) couple the streams through bidirectional cross-gated fusion. Results On the NASA IMS bearing dataset, under a leakage-free, file-level evaluation protocol with piecewise RUL labels, DST-SGR attains a test-set MAE of 0.152 and RMSE of 0.191, with a five-fold file-level cross-validation MAE of 0.128 ± 0.004, and generalizes to IMS Tests 2 and 3 under changed fault locations. Discussion The model contains 131,137 trainable parameters and trains in approximately 0.97 h on an NVIDIA RTX 4060 GPU, showing that competitive prognostic accuracy is achievable with modest computational resources.