Bearings are key components in mechanical equipment, which are widely used in various fields such as automobiles and airplanes. Aiming at the analysis of vibration signal processing under the variable speed condition of bearings, this paper proposes a new bearing fault diagnosis method, which firstly resamples the vibration signals in the angular domain, and then converts the resampled signals into images by the relative position matrix method, and finally uses the transfer learning to automatically extract the features and classify them. To verify the effectiveness of the method, it is tested on the Case Western Reserve University bearing fault dataset and University of Ottawa bearing fault dataset respectively. Compared with other time series to image methods (Recurrence Plot, etc) and other pre-trained models (GoogLeNet, etc), the proposed method has some advantages in terms of accuracy, image generation time, training time, and testing time. The accuracy of the proposed method in this paper reaches more than 90%, which suggests its potential effectiveness in the classification of bearing faults under variable speed working conditions.

As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed to solve the problem of insufficient discrimination ability of Poincaré plot distribution entropy (DE) in analyzing fractional Brownian motion time series with different Hurst indices. More specifically, firstly, the reasons for the failure of Poincaré plot DE in the analysis of fractional Brownian motion are analyzed; secondly, in view of the nonextensive of EEG, a nonextensive parameter, the distance between sector ring subintervals from the original point, is introduced to highlight the different roles of each sector ring subinterval in the system. To demonstrate the usefulness of this method, the simulated time series of the fractional Brownian motion with different Hurst indices were analyzed using Poincaré plot NDE, and the process of determining the relevant parameters was further explained. Furthermore, the published sleep EEG dataset was analyzed, and the results showed that the Poincaré plot NDE can effectively reflect different sleep stages. The obtained results for the two classes of time series demonstrate that the Poincaré plot NDE provides a prospective tool for single-channel EEG time series analysis.

This study aimed to improve the performance of single-channel steady-state visual evoked potential (SSVEP)-based visual acuity assessment by mode decomposition methods. Using the SSVEP dataset induced by the vertical sinusoidal gratings at six spatial frequency steps from 11 subjects, 3-40 Hz band-pass filtering and other four mode decomposition methods, i.e., empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and variational mode decomposition (VMD), were used to preprocess the single-channel SSVEP signals from Oz electrode. After comparing the SSVEP signal characteristics corresponding to each mode decomposition method, the visual acuity threshold estimation criterion was used to obtain the final visual acuity results. The agreement between subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for band-pass filtering (-0.095 logMAR), EMD (-0.112 logMAR), EEMD (-0.098 logMAR), ICEEMDAN (-0.093 logMAR), and VMD (-0.090 logMAR) was all pretty good, with an acceptable difference between FrACT and SSVEP acuity for band-pass filtering (0.129 logMAR), EMD (0.083 logMAR), EEMD (0.120 logMAR), ICEEMDAN (0.103 logMAR), and VMD (0.108 logMAR), finding that the visual acuity obtained by these four mode decompositions had a lower limit of agreement and a lower or close difference compared to the traditional band-pass filtering method. This study proved that the mode decomposition methods can enhance the performance of single-channel SSVEP-based visual acuity assessment, and also recommended ICEEEMDAN as the mode decomposition method for single-channel electroencephalography (EEG) signal denoising in the SSVEP visual acuity assessment.

Planetary gearboxes are the key components of large equipment, such as wind turbines, shield machines, etc. The operating state of the planetary gearbox is related to the safety of the equipment as a whole, and its feature extraction technology is essential. In assessing the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under a tacholess condition, a three-phase current, variable-speed tacholess envelope order analysis method is proposed. Firstly, a tacholess rotation speed estimation is completed by extracting the trend term of the instantaneous frequency of the asynchronous motor’s three-phase currents. The motor slip rate is assumed to be constant. Then, the envelope order analysis signal is obtained by re-sampling in the angular domain. Finally, the features of the envelope order signal are extracted, and a linear discriminant analysis (LDA) algorithm is used to fuse multiple indexes to generate a comprehensive feature reflecting the operating status of the planetary gearbox. The results of the simulation analysis and experimental verification show that the proposed method is effective in evaluating the operating state of the planetary gearbox under variable speed conditions. Compared with the traditional time–frequency ridge extraction method, the tacholess speed estimation method can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order completes the planetary gearbox state identification process, and a 95% classification accuracy rate is achieved.