Variational mode decomposition is a powerful signal processing technique that can adaptively decompose a multi-component signal into a number of modes, via solving an optimization problem. The optimal performance of this method in signal decomposition and avoiding of the mode mixing problem strictly relies on the true selection of decomposition parameters, that is, the number of extracted modes (K) and the mode frequency bandwidth control parameter (α). In the literature, the optimal values of these parameters are achieved by evaluating fault-related indices like kurtosis, but such an index is inefficient in judging the decomposition of healthy (without fault-related components), low-defective, and high-noise signals. In this research, a novel method called fine-tuned variational mode decomposition is proposed to determine the optimal values of decomposition parameters K and α, by judging the adaptive indices. In this proposed method, the optimal values of these parameters are obtained by minimizing the mean bandwidth of the extracted modes. In order to achieve these optimal values, the mean correlation coefficients between the adjacent modes and the energy loss coefficient between the original signal and the reconstructed signal, should not exceed of defined thresholds for optimal values. The proposed method is applied to the simulation signal and experimental ones collected from the automobile gearbox system. Comparing this method with those in the literature exhibits its higher effectiveness in the true decomposition of signals with different natures. It is also shown that using the proposed method for signal decomposition is able to correctly classify the healthy and defective states of the gearbox system alongside the principal component analysis method and support vector machine classifier.

This paper investigates fault detection in offshore wind permanent-magnet synchronous generators (PMSGs) for demagnetization and eccentricity faults (both static and dynamic) at various severity levels. The study utilizes a high-speed PMSG model on the National Renewable Energy Laboratory (NREL) 5 MW reference offshore wind turbine at the rated wind speed to simulate healthy and faulty conditions. An unsupervised convolutional autoencoder (CAE) model, trained on simulated signals from the generator in its healthy state, serves for anomaly detection. The main aim of the paper is to evaluate the possibility of fault detection by means of high-resolution electrical and electromagnetic signals, given that the typically low-resolution standard measurements used in supervisory control and data acquisition (SCADA) systems of wind turbines often impede the early detection of incipient failures. Signals analyzed include three-phase currents, induced shaft voltage, electromagnetic torque, and magnetic flux (air gap and stray) from different directions and positions. The performance of CAE models is compared across time and frequency domains. Results show that in the time domain, stator three-phase currents effectively detect faults. In the frequency domain, stray flux measurements, positioned at the top, bottom, and sides of the outside of the stator housing, demonstrate superior performance in fault detection and sensitivity to fault severity levels. In particular, radial components of stray flux can successfully distinguish between eccentricity and demagnetization.

Structural health monitoring is essential for ensuring the structural safety performance during the service life. The process is of paramount importance in the case of the Pelamis wave energy converter due to the structural parts being subjected to the marine environmental risky conditions and consequently, power take-off system damage. In this paper, a fuzzy-based damage detection method using the dynamic response of the Pelamis type P1 is proposed. Accurate modeling of a dynamic system with simple dynamic equations is one of the important steps of damage detection process; for this purpose, the equations of the motion of the Pelamis system is derived using the Lagrangian method for creating a discrete model of the Pelamis in the first step. For validating the obtained model, scaled experimental model of the Pelamis is provided, and modal analysis is carried out. Then, the features in the frequency spectrum of the structure response in each degree of freedom as a result of power take-off damaged conditions are considered as inputs to the fuzzy system. Also, the fuzzy system is trained based on calibrating the membership functions by defining the damage classes appropriately. For validating the proposed method, noise with different signal to noise ratios is inserted to the measured features and the success rate of the damage detection is calculated. The results show that the proposed method is able to identify the damage classes with acceptable success rate.