The fast kurtogram (FK), as a fast and effective method for fault diagnosis, is well accepted by many experts and scholars. However, the FK can only estimate the bandwidth and central frequency which come from resonance modulation of the signal. Sometimes useful information (containing faults) may be lost due to the inaccuracy of the estimated center frequency or bandwidth. In this paper, a novel method named empirical scanning spectrum kurtosis (ESSK), based on empirical wavelet transform (EWT), is proposed. Constructed by the principle of EWT, a set of filters with varying bandwidth scan and filter the whole frequency domain from low to high and a series of empirical modal components are obtained. Then, the spectral kurtosis (SK) of these components is calculated. The center frequency and bandwidth corresponding to the component which has the maximum SK are selected as the optimal center frequency and bandwidth. This method can adaptively and accurately find the frequency band containing rich fault feature information, and extract the corresponding component. Multiple simulation signals and experimental signals are used to verify the effectiveness of the proposed method. The results show that the method can maximally extract the components which contain the periodic pulse information and accurately diagnose the faults of the rolling bearing. In addition, comparisons with three popular signal processing methods, including the sparsogram, firbased FK and shorttime Fourier transform (STFT) based FK are conducted to highlight the superiority of the proposed method.

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As essential but easily damaged parts of rotating machinery, rolling bearings have been deeply researched and widely used in mechanical processes. The real-time detection of bearing state and simple, rapid, and accurate diagnosis of bearing fault are indispensable to the industrial system. The bearing’s inner ring and outer ring vibration acceleration can be measured by high-precision sensors, and the running state of the bearing can be effectively extracted. The empirical wavelet transform (EWT) can adaptively decompose the vibration acceleration signal into a series of empirical modes. However, this method not only runs slowly, but also causes inexplicable empirical modes due to the unreasonable boundaries of the frequency domain division. In this paper, a new method is proposed to improve the empirical wavelet transform by dividing the boundaries from the spectrum, named the fast empirical wavelet transform (FEWT). The proposed method chooses different points in the Fourier transform of the spectrum (key function) to reconstruct the trend component of the spectrum. The minimum points in the trend component divide the spectrum into a series of bands. A more reasonable set of boundaries can be found by choosing appropriate trend components to obtain effective empirical modes. The simulation results show that the proposed method is effective and that the acquired empirical mode is more reasonable than the EWT method. Combining kurtosis with fault feature extraction of inner and outer rings of bearings, the method is successfully applied to the fault diagnosis of rolling bearings.

Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors. For statistical machine translation (SMT), forestbased methods have been proven to be effective for solving this problem, while for NMT this kind of approach has not been attempted. This paper proposes a forest-based NMT method that translates a linearized packed forest under a simple sequence-to-sequence framework (i.e., a forest-to-string NMT model). The BLEU score of the proposed method is higher than that of the string-to-string NMT, treebased NMT, and forest-based SMT systems.