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An integrated approach for real‐time oscillatory stability assessment (OSA) based on mutual information theory is proposed in this study. An advanced maximum‐relevance minimum‐redundancy (MRMR) ensemble scheme is designed to explore the internal relations between operation variables and the oscillatory stability margin (OSM). Multiple MRMR procedures are generated in parallel to select multiple different feature subsets, in which each feature presents a relevant and complementary description of OSM. The functional expression of the relationships is obtained by curve fitting. The 21‐bus system and 1648‐bus system are implemented to test the performance of the proposed approach. A compared investigation is made with some other data mining methods. The impacts of the number of feature sets, size of feature sets, size of training set, invalid data and computation time are studied. Experimental results reveal that the proposed approach provides faster and more accurate assessment results and is a real‐time adaptive approach for OSA. [ABSTRACT FROM AUTHOR]

• A methodology is developed to mine the dynamics of vibrations signals for wind turbine drivetrain system fault diagnosis. • An effective visualization technique namely distribution recurrence plots is originally designed to reveal intrinsic dynamic changes of vibration signal. • A new quantifier pattern entropy is proposed for feature extraction. • The proposed method can be extended to identify various wind turbine drivetrain system faults. Extracting sensitive information from vibration signal has become a frequently adopted way in fault diagnosis. However, most previous methods fragmented the relationship between quantification and visualization analysis, which affects the interpretability, accuracy and comprehensiveness of the extracted information. To this end, this paper proposes distribution recurrence plots (DRP) and measures (DRM) to realize the unity of visualization and quantification analysis of the signals. Specifically, DRP is a novel feature graphical representation method following the thought of symbolic dynamics. Derived from DRP, DRM is developed containing four quantifiers for extracting comprehensive fault features that allows a multiclass support vector machine (SVM) to identify the fault types of wind turbine drivetrain system (WTDS). Specially in DRM, pattern entropy is a newly designed quantifier by considering pattern distribution to obtained more accurate quantitative representation of the signals. Using simulated data, DRP and DRM are validated to reveal the intrinsic structural changes for different dynamic systems and robustness to noise. Applications on wind turbine gearbox illustrate that the proposed method has favorable diagnosis performance and stability compared with other competitors. This approach is easy to interpret, is robust to noise, and has a low computational burden, becoming viable for WTDS fault diagnosis. [ABSTRACT FROM AUTHOR]

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method. [ABSTRACT FROM AUTHOR]