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Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF cables experience faults caused by insulation degradation, thermal expansion, and environmental stressors, which, due to their subtle and gradual nature, complicate incipient fault detection and subsequent fault localization. This study presents a novel, proactive, and retrofit-friendly predictive condition monitoring method. It leverages distributed accelerometer sensors non-intrusively mounted on the HPFF steel pipe within existing manholes to continuously monitor vibration signals in real time. A physics-enhanced convolutional neural network–long short-term memory (CNN–LSTM) deep learning architecture analyzes these signals to detect incipient faults before they evolve into critical failures. The CNN–LSTM model captures temporal dependencies in acoustic data streams, applying time-series analysis techniques tailored for the predictive condition monitoring of HPFF cables. Experimental validation uses vibration data from a scaled-down HPFF laboratory test setup, comparing normal operation to incipient fault events. The model reliably identifies subtle changes in sequential acoustic patterns indicative of incipient faults. Laboratory experimental results demonstrate a high accuracy of the physics-enhanced CNN–LSTM architecture for incipient fault detection with effective data feature extraction. This approach aims to support enhanced operational resilience and faster response times without intrusive infrastructure modifications, facilitating early intervention to mitigate service disruptions.
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This content will become publicly available on October 14, 2026
Echo-AE: A Physics-Informed Seq2Seq-Attention-Autoencoder for Predictive Maintenance of Underground Power Cable Infrastructure
Predictive maintenance for underground high-pressure fluid-filled (HPFF) power cables remains a critical challenge due to the weak and intermittent nature of fault-induced signals and the limited accessibility of buried infrastructure. This paper proposes a physics-informed Seq2Seq-attentionautoencoder acoustic monitoring (Echo-AE) model for predictive maintenance in underground HPFF cable systems. The Echo-AE model is developed based on a physics-informed loss function that incorporates both physics-based constraints and prediction errors. A controlled experimental setup of underground HPFF cable systems was used to capture continuous acoustic monitoring data, where three fault severity levels were generated, resulting in 4 million acoustic samples spanning normal operations and 15 fault events, and producing an imbalanced dataset with a 117:1 normal-tofault ratio to simulate real-world scenarios in which early-stage faults are rare. Results demonstrated Echo-AE’s superior early-stage fault detection capability compared with traditional models, with an F1-score of 0.8313, precision of 0.7864, recall of 0.8816, and an accuracy of 0.9936. The model exhibits fast convergence (20 epochs) and an area under the receiver operating characteristic curve of 0.998. Threshold sensitivity analysis revealed an optimal operation point that balances false positives and false negatives.
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- PAR ID:
- 10652867
- Publisher / Repository:
- ELSEVIER
- Date Published:
- Journal Name:
- Reliability engineering systems safety
- ISSN:
- 1879-0836
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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