Multi-Scale Temporal Convolutional Network for the Long-Term Fault Early Warning of Gas Turbines

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 <tex-math notation="LaTeX">3σ </tex-math> 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.