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access icon free Diagnosis of non-linear mixed multiple faults based on underdetermined blind source separation for wind turbine gearbox: simulation, testbed and realistic scenarios

© The Institution of Engineering and Technology
Received 10/12/2016, Accepted 13/06/2017, Revised 06/05/2017, Published 15/06/2017

The diagnosis of multi-fault in wind turbine gearbox based on vibration signal processing is considered challenging as the collected measurements from acceleration transducers are often a non-linear mixture of signals induced from an unknown number of sources, i.e. an underdetermined blind source separation (UBSS) problem. In this study, a novel UBSS-based algorithmic solution is proposed to address this technical challenge from two aspects: source number estimation and source signal recovery. The former is realised based on the empirical mode decomposition and singular value decomposition (SVD) joint approach; and for the latter, the observed vibration signals are transformed to the time-frequency domain using short-time Fourier transform to obtain the sparse representation of the signals. The fuzzy C-means clustering and l 1 norm decomposition are carried out to estimate the mixing matrix and recover the source signals, respectively. The performance of proposed solution is extensively assessed through experiments based on simulation, testbed and realistic wind farm measurements for a range of fault scenarios for both linear and non-linear scenarios. The numerical result clearly confirms the effectiveness of the proposed algorithmic solution for non-linear multi-fault diagnosis of wind turbines.

Inspec keywords: fault diagnosis; Fourier transforms; power generation faults; wind turbines; singular value decomposition; blind source separation; power system measurement; time-frequency analysis; wind power plants; matrix algebra; gears; signal representation; fuzzy set theory

Other keywords: source signal recovery; wind farm measurement; wind turbine gearbox; vibration signal processing; vibration signal transformation; l1 norm decomposition; singular value decomposition; sparse signal representation; underdetermined blind source separation; nonlinear mixed multiple fault diagnosis; mixing matrix estimation; time-frequency domain analysis; fuzzy C-means clustering; empirical mode decomposition; nonlinear signal mixture; source number estimation; acceleration transducer; short-time Fourier transform; SVD; UBSS problem

Subjects: Integral transforms; Signal processing and detection; Wind power plants; Power system measurement and metering; Algebra; Combinatorial mathematics

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