TY - GEN

T1 - Noise-Robust Condition Monitoring Using Variational Mode Decomposition and Neural Networks

AU - Макшанов, Андрей

AU - Мусаев, Александр Азерович

AU - Григорьев, Дмитрий Алексеевич

PY - 2026/3/23

Y1 - 2026/3/23

N2 - Reliable condition assessment of complex technical systems based on vibroacoustic signals is challenging due to strong non-stationarity, high noise levels, and overlapping spectral components. This paper proposes an integrated signal processing and machine learning framework for automated technical condition prediction. The approach is based on adaptive time-frequency decomposition using the Hilbert-Huang methodology and Variational Mode Decomposition (VMD), which enables the extraction of physically meaningful intrinsic mode functions while suppressing noise-induced distortions. The decomposed components are subsequently used as informative inputs to neural network classifiers. Special attention is given to a hybrid architecture combining VMD-based preprocessing with a Kolmogorov-Arnold Network and a Long Short-Term Memory model (VMD-KAN-LSTM), designed to capture both nonlinear feature interactions and temporal dependencies. The proposed methodology is evaluated using monitoring data obtained from an SKA60/200 reciprocating compressor under normal operation and three characteristic fault conditions. Comparative experiments involving LSTM, deep autoencoder, KAN, and their VMD-enhanced counterparts are conducted using confusion-matrix-based performance analysis. The results demonstrate that VMD significantly improves class separability in noisy operating conditions, while the proposed VMD-KAN-LSTM architecture achieves the highest diagnostic accuracy and robustness among the considered models. The findings confirm the effectiveness of decomposition-informed learning for condition monitoring of complex technical systems.

AB - Reliable condition assessment of complex technical systems based on vibroacoustic signals is challenging due to strong non-stationarity, high noise levels, and overlapping spectral components. This paper proposes an integrated signal processing and machine learning framework for automated technical condition prediction. The approach is based on adaptive time-frequency decomposition using the Hilbert-Huang methodology and Variational Mode Decomposition (VMD), which enables the extraction of physically meaningful intrinsic mode functions while suppressing noise-induced distortions. The decomposed components are subsequently used as informative inputs to neural network classifiers. Special attention is given to a hybrid architecture combining VMD-based preprocessing with a Kolmogorov-Arnold Network and a Long Short-Term Memory model (VMD-KAN-LSTM), designed to capture both nonlinear feature interactions and temporal dependencies. The proposed methodology is evaluated using monitoring data obtained from an SKA60/200 reciprocating compressor under normal operation and three characteristic fault conditions. Comparative experiments involving LSTM, deep autoencoder, KAN, and their VMD-enhanced counterparts are conducted using confusion-matrix-based performance analysis. The results demonstrate that VMD significantly improves class separability in noisy operating conditions, while the proposed VMD-KAN-LSTM architecture achieves the highest diagnostic accuracy and robustness among the considered models. The findings confirm the effectiveness of decomposition-informed learning for condition monitoring of complex technical systems.

KW - Hilbert-Huang transform

KW - condition monitoring

KW - empirical mode decomposition

KW - fault diagnosis

KW - variational mode decomposition

KW - vibroacoustic signal analysis

UR - https://www.mendeley.com/catalogue/7367b99f-1c5a-39ef-b968-d05a4adb6651/

U2 - 10.1109/smartindustrycon68821.2026.11492929

DO - 10.1109/smartindustrycon68821.2026.11492929

M3 - Conference contribution

SN - 979-8-3315-8054-4

SP - 108

EP - 114

BT - 2026 International Russian Smart Industry Conference (SmartIndustryCon)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2026 International Russian Smart Industry Conference

Y2 - 22 March 2026 through 28 March 2026

ER -