Envelope analysis is one of the most useful methods in localized fault diagnosis of rolling element bearings. However, there is a challenge in selecting the optimal resonance band. In this paper, a novel method based on kurtogram and frequency domain correlated kurtosis is proposed. To obtain the correct relationship between the node and frequency band in wavelet packet transform, a vital process named frequency ordering is conducted to solve the frequency folding problem due to down sampling. Correlated kurtosis of envelope spectrum instead of correlated kurtosis of envelope signal or kurtosis of envelope spectrum is utilized to generate the kurtogram, in which the maximum value can indicate the optimal band for envelope analysis. Several cases of experimental bearing fault signals are used to evaluate the immunity of the proposed method to strong noise interference. The improved performance has also been compared with two previous developed methods. The results demonstrate the effectiveness and robustness of the method in fault diagnosis of rolling element bearings.

With the rapid development of deep learning (DL) techniques, data-driven models have been increasingly used in remaining useful life (RUL) prediction, in which convolution neural network (CNN)-based RUL prognostics models have received special attention. However, there are still two main issues that need to be addressed: 1) traditional CNN is not suitable to extract the time-sequence characteristics from the long-term historical signals and 2) the receptive field range of convolution operation is fixed, thus only learning the feature information at a specific scale, which is insufficient for complex feature extraction. To address these two issues, a multiscale temporal convolutional network (MsTCN) that has powerful time-sequence characteristics is proposed for RUL prediction in this article. The MsTCN adopts the temporal convolutional network (TCN) framework, which is good at extracting time-sequence information. Based on this, a new multiscale dilated causal convolution residual block (MsDCCRB) is developed to constitute the RUL prognostics model, where multiple dilated convolutions (DCs) are based on different dilation factors that are put on each layer in parallel. Furthermore, the squeeze-and-excitation (SE) unit is embedded into the MsDCCRB to adaptively recalibrate the sequence feature responses and enhance the representation learning ability. Through stacking multiple MsDCCRBs, the historical condition monitoring data can be fed directly into the proposed model to realize the high-level representations of RUL estimation. Finally, the proposed approach is validated with the accelerated whole-life degradation dataset of rolling element bearings (REBs). The experimental results exhibit that the proposed MsTCN achieves a higher RUL prediction accuracy, which is superior to some state-of-the-art data-driven prognostics methods.

This paper presents a method of ultrasonic-assisted laser metal deposition of Al-12Si alloy. The effects of the ultrasonic power and remelting treatment on the development of porosity, microstructural evolution, and tensile properties of the deposits were investigated. The results suggested that a combination of an ultrasonic vibration and remelting treatment could prolong the existence of the molten pool and the effect of the ultrasound. Therefore, the density of the samples increased from 95.4% to 99.1% compared to the as-prepared samples. The ultrasonic action in the molten pool could not only increase the density of the samples but also refine the grains and improve the tensile properties of the samples. Metallographic observation showed that the maximum size of the primary α-Al dendrites were refined from 277.5 µm to 87.5 µm. The ultimate tensile strength and elongation of the remelting treatment samples with ultrasonic vibration were ~227 ± 3 MPa and 12.2% ± 1.4%, respectively, which were approximately 1.17 and 1.53 times those of the as-prepared samples, respectively. According to the tensile properties and fracture analysis, the density increase dominated the improvement of the mechanical properties.