Fault early warning based on gas turbine sensor networks is critical for intelligent predictive maintenance and operational safety in modern power systems. While data-driven techniques are prevalent, balancing computational efficiency with long-term modeling of measurement transients remains challenging under complex temporal drifts. This paper proposes a novel Multi-Scale Temporal Convolutional Network (MS-TCN) specifically designed for the long-term time series forecasting (LTSF) task in industrial environments. The proposed architecture introduces a mathematical decomposition framework featuring: 1) an autocorrelation-guided detrending module to mitigate non-stationary distribution shifts by extracting inherent periodic trends directly from multi-sensor streams; and 2) a dual-pooling mechanism that decomposes measurement sequences for highly efficient cross-scale dependency learning. Additionally, a full-history convolution layer provides the architectural capacity to integrate broader historical context. Rigorous evaluations on Long-Term Time Series Forecasting (LTSF) benchmarks demonstrate that the MS-TCN architecture reduces estimation errors by up to 43. 2% (with improvements ranging from 18. 5% to 43. 2% depending on the specific dataset and forecasting horizon) compared to state-of-the-art computational models. Furthermore, to translate these LTSF predictions into practical industrial applications, a adaptive residual-weighted multi-level warning framework is established using adaptive weights to quantify decaying prediction confidence. A robust four-level alert system based on 3 thresholds is introduced to explicitly account for sensing uncertainties and helps reduce false positives. Validation on real-world gas turbine operational data confirms that the proposed computing framework triggers warnings 4–6 hours earlier than conventional baselines, offering a practical and effective tool for industrial predictive maintenance.
Liu et al. (Thu,) studied this question.