To address the challenges of high concealability and difficulty in identifying minor damage in transmission lines, as well as low fault diagnosis accuracy under strong noise conditions, a novel fault diagnosis method for transmission lines is proposed. A data processing method based on adaptive modal filtering is proposed by combining a variational constraint model with an adaptive frequency band extraction strategy. Subsequently, by leveraging the concept of the generalized Fourier transform, pseudopeak effects near modal frequencies are suppressed, achieving thorough noise signal filtering without altering the intrinsic state characteristics of the transmission lines. For fault diagnosis, a convolutional neural network enhanced with an attention module is constructed, and a fault diagnosis model integrated with bidirectional long short-term memory (BiLSTM) is proposed. By embedding a convolutional block attention module, network weights are dynamically adjusted to enhance feature representation in both channel and spatial dimensions. Additionally, the introduction of BiLSTM strengthens the model’s ability to process time series data. Finally, the proposed method is validated on a conductor vibration test platform, demonstrating its high diagnostic accuracy and superior performance in noisy environments compared with other models.
ZhangTZhengWXieY, et al. A case study of rupture in 110 kV overhead conductor repaired by full-tension splice. Eng Fail Anal2020; 108: 104349.
2.
SmyrakB.Analysis of the quality of aluminum overhead conductors after 30 years of operation. Eng Fail Anal2023; 154: 107600.
3.
JinYQuanMYanS, et al. Analysis of overhead transmission lines fusing failure due to poor contact between conductors and clamps. Eng Fail Anal2020; 118: 104858.
4.
WangFSongGMaoJ, et al. Internal defect detection of overhead aluminum conductor composite core transmission lines with an inspection robot and computer vision. IEEE Trans Instrum Meas2023; 72: 3512516.
5.
ZhaoLZhangTHuangX, et al. A structural health monitoring system of the overhead transmission line conductor. IET Sci Meas Technol2022; 16: 28–39.
6.
WuHWangJWuZ, et al. Multi-parameter optimized triboelectric nanogenerator based self-powered sensor network for broadband aeolian vibration online-monitoring of transmission lines. Adv Energy Mater2022; 12: 2103654.
7.
RochaPHCLangloisSLalondeS, et al. A general life estimation method for overhead conductors based on fretting fatigue behavior of wire. Theor Appl Fract Mech2022; 121: 103443.
8.
BealeCNiezreckiCInalpolatM.An adaptive wavelet packet denoising algorithm for enhanced active acoustic damage detection from wind turbine blades. Mech Syst Signal Process2020; 142: 106754.
9.
KimSLeeSHKimS.Pointwise multiclass vibration classification for cable-supported bridges using a signal-segmentation deep network. Eng Struct2023; 279: 115599.
10.
LeiWWangGWanB, et al. High voltage shunt reactor acoustic signal denoising based on the combination of VMD parameters optimized by coati optimization algorithm and wavelet threshold. Measurement2024; 224: 113854.
11.
LuJJiaBLiS, et al. A noise reduction method of rolling bearing based on empirical wavelet transform and adaptive time frequency peak filtering. Meas Sci Technol2023; 34: 125146.
12.
Ch’ienLWangYShiA, et al. Noise suppression: empirical modal decomposition in non-dispersive infrared gas detection systems. Infrared Phys Techn2020; 108: 103335.
13.
ShiJTehJ.Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion. Appl Energy2024; 353: 122146.
14.
WeiHQiTFengG, et al. Comparative research on noise reduction of transient electromagnetic signals based on empirical mode decomposition and variational mode decomposition. Radio Sci2021; 56: 1–19.
15.
FengXCZhangSSXuH, et al. Variational mode decomposition-enhanced hybrid deep learning for accurate wave height prediction. Phys Fluids2025; 37: 7.
16.
DengHZSiRJHuXY, et al. Wind tunnel study on wind-induced vibration responses of a UHV transmission tower-line system. Adv Struct Eng2013; 16.7: 1175–1185.
17.
SaïdSBKobayashiTØrstedB.Generalized Fourier transforms Fk,a. C R Math2009; 347: 1119–1124.
18.
ZhuZLeiYQiG, et al. A review of the application of deep learning in intelligent fault diagnosis of rotating machinery. Measurement2023; 206: 112346.
19.
MianTChoudharyAFatimaS.Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning. Nondestr Test Eval2023; 38: 275–296.
20.
ZhangKTangBDengL, et al. A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox. Measurement2021; 179: 109491.
21.
ZhangLFanQLinJ, et al. A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions. Eng Appl Artif Intell2023; 119: 105735.
22.
LiCLiSWangH, et al. Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis. Knowledge Based Syst2023; 264: 110345.
23.
LiGHuJShanD, et al. A CNN model based on innovative expansion operation improving the fault diagnosis accuracy of drilling pump fluid end. Mech Syst Signal Process2023; 187: 109974.
24.
ZhangXWangHWuB, et al. A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data. J Intell Manuf2023; 34: 2449–2462.
25.
KhorramAKhalooeiMRezghiM.End-to-end CNN+ LSTM deep learning approach for bearing fault diagnosis. Appl Intell2021; 51: 736–751.
26.
MehmoodILiHQaroutY, et al. Deep learning-based construction equipment operators’ mental fatigue classification using wearable EEG sensor data. Adv Eng Inf2023; 56: 101978.
27.
YanXYanWXuY, et al. Machinery multi-sensor fault diagnosis based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network. Mech Syst Signal Process2023; 202: 110664.
28.
YangSChouinardLELangloisS.Hourly wind data for aeolian vibration analysis of overhead transmission line conductors. J Wind Eng Ind Aerodyn2022; 230: 105184.
29.
LiMJiaDWuZ, et al. Structural damage identification using strain mode differences by the iFEM based on the convolutional neural network (CNN). Mech Syst Signal Process2022; 165: 108289.
30.
HeJH.A tutorial and heuristic review on Lagrange multiplier for optimal problems. Science2016; 8: 121–148.
31.
MartinJBaylisCCohenL, et al. A peak-search algorithm for load–pull optimization of power-added efficiency and adjacent-channel power ratio. IEEE Trans Microwave Theory Tech2014; 62: 1772–1783.
32.
TianZGaiM.Football team training algorithm: a novel sport-inspired meta-heuristic optimization algorithm for global optimization. Expert Syst Appl2024; 245: 123088.
33.
BakırH.Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem. Expert Syst Appl2024; 240: 122460.
34.
MiaoYWangJZhangB, et al. Practical framework of Gini index in the application of machinery fault feature extraction. Mech Syst Signal Process2022; 165: 108333.
35.
LvYWuHYuanR, et al. Generalized synchro-extracting-based stepwise demodulation transform and its application to fault diagnosis of rotating machinery. IEEE Sens J2023; 23: 5045–5060.
36.
ZhangZWangM. Convolutional neural network with convolutional block attention module for finger vein recognition. arXiv. Preprint. arXiv 2202.06673, 2022.
37.
LuoXJOyedeleLO.Forecasting building energy consumption: adaptive long-short term memory neural networks driven by genetic algorithm. Adv Eng Inf2021; 50: 101357.
38.
PanZXuBChenW, et al. Improved complete ensemble empirical mode decomposition with adaptive noise and its application in noise reduction for fiber optic sensing. J Lightwave Technol2024; 43: 2466–2474.
39.
LuHZhangWChenZ, et al. Rotating machinery early fault detection integrating variational mode decomposition and multiscale singular value decomposition. Meas Sci Technol2024; 35: 126128.
40.
LiYXuF.Structural damage monitoring for metallic panels based on acoustic emission and adaptive improvement variational mode decomposition–wavelet packet transform. Struct Health Monit2022; 21: 710–730.
41.
ArtsLPAvan den BroekEL. The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis. Nat Comput Sci2022; 2: 47–58.
42.
KarabaySErtürkATZerenM, et al. Failure analysis of wire-breaks in aluminum conductor production and investigation of early failure reasons for transmission lines. Eng Fail Anal2018; 83: 47–56.
43.
PanZZhangZZhaoP, et al. Multi-sensor signal fusion method for rolling bearing based on the standard relative mean–variance value and random weighting algorithm. Measurement2024; 235: 114823.
44.
ShiHMiaoKRenX.Short-term load forecasting based on CNN-BiLSTM with Bayesian optimization and attention mechanism. Concurrency Comput Pract Exper2023; 35: e6676.
45.
SongBLiuYFangJ, et al. An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples. Neurocomputing2024; 574: 127284.
46.
KangJZhuXShenL, et al. Fault diagnosis of a wave energy converter gearbox based on an Adam optimized CNN-LSTM algorithm. Renew Energ2024; 231: 121022.
