VP16-06 Modeling Non-Linear and Non-Stationary Magnetic Signals: An Enhanced Signal Processing Strategy for Wind Time Series Analysis
Creators: Akhyurna Swain
Imraan Hossain
Chunhua Liu
Philip W.T. Pong
Publisher: name: IEEE
Year: 2025

Magnetic-flux-based condition monitoring techniques are becoming increasingly popular due to their advantages such as non-invasiveness, low-costs, and ease of sensor installation. However, developing diagnostic- and prognostic-based condition monitoring sytems become challenging as magnetic signals based wind time series exhibit nonlinear and non-stationary characteristics due to being subjected to diverse combination of dynamic system behavior [1]. Present data-driven-based feature extraction techniques and knowledge and parameter estimator models are successful in detecting and quantifying the nonlinear and nonstationary behaviour in wind speed time series and wind vibration time series [2],[3]. Further, short-time Fourier transform and discrete wavelet transform have proven suboptimal for analyzing nonlinear magnetic flux signals [4]. This research bridges the gap by investigating a new methodology of feature extraction to adeplty process the nonlinear and nonstationary behaviour in magnetic signals based wind time series. This methodology is unique in nature since it utilizes magnetic signature-based condition indicators derived from distributed TMR sensors placed on a model that accounts for the electromagnetic nature of the drive train coupling as shown in Fig. 1. Further, the signal processing technique employed here takes into account various non-linearities that arises from magnetic saturation, hysteresis and eddy currents in the generator's magnetic circuit and the non-stationarity that arises from variations in wind speed, mechanical vibrations and environmental factors such as changes in temperature. Next, these bivatiate features are decomposed into its first, second, and higher order of moments in time frequency domain to efficiently extract the underlying fault features. Finally, faults on both mechanical and electrical components of the drive train are identified and successfully classified using a machine learning based multi class classification algorithm. The proposed methodology is validated using distributed sensors on a drive train emulator.References: [1] M. S. Kan, A. C. C. Tan, and J. Mathew, "A review on prognostic techniques for non-stationary and non-linear rotating systems," Mechanical Systems and Signal Processing, vol. 62-63, pp. 1-20, 2015. [2] S. Solari and M. Á. Losada, "Simulation of non-stationary wind speed and direction time series," Journal of Wind Engineering and Industrial Aerodynamics, vol. 149, pp. 48-58, 2016. [3] R. Uma Maheswari and R. Umamaheswari, "Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train – A contemporary survey," Mechanical Systems and Signal Processing, vol. 85, pp. 296-311, 2017. [4] I. Zamudio-Ramirez, R. A. Osornio-Rios and J. A. Antonino-Daviu, "Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review," IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 2895-2908, 2022.
Magnetic-flux-based condition monitoring techniques are becoming increasingly popular due to their advantages such as non-invasiveness, low-costs, and ease of sensor installation. However, developing diagnostic- and prognostic-based condition monitoring sytems become challenging as magnetic signals based wind time series exhibit nonlinear and non-stationary characteristics due to being subjected to diverse combination of dynamic system behavior [1]. Present data-driven-based feature extraction techniques and knowledge and parameter estimator models are successful in detecting and quantifying the nonlinear and nonstationary behaviour in wind speed time series and wind vibration time series [2],[3]. Further, short-time Fourier transform and discrete wavelet transform have proven suboptimal for analyzing nonlinear magnetic flux signals [4]. This research bridges the gap by investigating a new methodology of feature extraction to adeplty process the nonlinear and nonstationary behaviour in magnetic signals based wind time series. This methodology is unique in nature since it utilizes magnetic signature-based condition indicators derived from distributed TMR sensors placed on a model that accounts for the electromagnetic nature of the drive train coupling as shown in Fig. 1. Further, the signal processing technique employed here takes into account various non-linearities that arises from magnetic saturation, hysteresis and eddy currents in the generator's magnetic circuit and the non-stationarity that arises from variations in wind speed, mechanical vibrations and environmental factors such as changes in temperature. Next, these bivatiate features are decomposed into its first, second, and higher order of moments in time frequency domain to efficiently extract the underlying fault features. Finally, faults on both mechanical and electrical components of the drive train are identified and successfully classified using a machine learning based multi class classification algorithm. The proposed methodology is validated using distributed sensors on a drive train emulator.References: [1] M. S. Kan, A. C. C. Tan, and J. Mathew, "A review on prognostic techniques for non-stationary and non-linear rotating systems," Mechanical Systems and Signal Processing, vol. 62-63, pp. 1-20, 2015. [2] S. Solari and M. Á. Losada, "Simulation of non-stationary wind speed and direction time series," Journal of Wind Engineering and Industrial Aerodynamics, vol. 149, pp. 48-58, 2016. [3] R. Uma Maheswari and R. Umamaheswari, "Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train – A contemporary survey," Mechanical Systems and Signal Processing, vol. 85, pp. 296-311, 2017. [4] I. Zamudio-Ramirez, R. A. Osornio-Rios and J. A. Antonino-Daviu, "Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review," IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 2895-2908, 2022.