A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures.

Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.

Nurses' Attitudes Toward Internet-Based Home Care: A Survey Study.

Internet-based home care has emerged as a way to relieve the burden of hospitals and meet patients' need for home care. This study aims to explore nurses' attitudes toward Internet-based home care. A cross-sectional online survey was conducted in Ningbo City in China. A self-designed Internet-based home care attitudes questionnaire for nurses (23 items) was used. There were 2039 nurses from 13 hospitals who participated in this online survey. Results reveal that, 1369 nurses (67.1%) were willing to provide Internet-based home care. However, there were significant differences in the attitudes of nurses with different ages (H = 11.86, P = .001), years of work experience (H = 24.257, P = .000), positions (H = 8.850, P = .031), and types of phones (H = 13.096, P = .001). More than 80% of nurses were willing to provide hypodermic and intramuscular injection. But there was a significant difference in the attitudes toward hypodermic injection, intramuscular injection, and pressure ulcer care in nurses with different ages (H = 13.039, P = .005; H = 9.178, P = .027; H = 10.997, P = .012) and a significant difference in the attitudes toward pressure ulcer care in nurses with different years of work experience (H = 15.259, P = .002). Results also indicated that most nurses were worried about their own safety and personal rights protection during Internet-based home care.

Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings.

Bearing fault features are presented as repetitive transient impulses in vibration signals. Narrowband demodulation methods have been widely used to extract the repetitive transients in bearing fault diagnosis, for which the key factor is to accurately locate the optimal band. A multitude of criteria have been constructed to determine the optimal frequency band for demodulation. However, these criteria can only describe the strength of transient impulses, and cannot differentiate fault-related impulses and interference impulses that are cyclically generated in the signals. Additionally, these criteria are easily affected by the independent transitions and background noise in industrial locales. Therefore, the large values of the criteria may not appear in the optimal frequency band. To overcome these problems, a new method, referred to as multiband envelope spectra extraction (MESE), is proposed in this paper, which can extract all repetitive transient features in the signals. The novelty of MESE lies in the following aspects: (1) it can fuse envelope spectra at multiple narrow bands. The potential bands are selected based on Jarque-Bera statistics of narrowband envelope spectra; and (2) fast independent component analysis (fastICA) is introduced to extract the independent source spectra, which are fault- or interference-related. The multi-band strategy will preserve all impulse features and make the method more robust. Meanwhile, as a blind source separation technique, the fastICA can suppress some in-band noise and make the diagnosis more accurate. Several simulated and experimental signals are used to validate the efficiency of the proposed method. The results show that MESE is effective for enhanced fault diagnosis of rolling element bearings. Bearing faults can be detected even in a harsh environment.

Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings.

This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results. The features based on wavelet packet decomposition are derived from the original signals. Then the intrinsic geometric features extracted by WKECA are fed into the support vector machine (SVM) classifier to recognize different operating conditions of bearings, and we obtain the overall accuracy (97%) for the experimental samples. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

Effectiveness of Physical Activity Interventions on Cognition, Neuropsychiatric Symptoms, and Quality of Life of Alzheimer's Disease: An Update of a Systematic Review and Meta-Analysis.

The topic of physical activity interventions for the treatment of Alzheimer's disease (AD) has been discussed for decades, but there are still inconsistent views on the effect of its intervention in different studies. With the increase in randomized controlled trials (RCTs), it is necessary to update newly published studies and systematically evaluate the effects of physical activity interventions. Scientific citation databases (e.g., PubMed, EMBASE, etc.) and registration databases (e.g., ISRCTN, CHICTR, etc.) were checked to screen RCTs and systematic reviews of physical activity interventions in AD. Then extract and review the intervention methods and their evaluation results in the included studies. Spearman correlation method was used to test the association between the mean difference (MD) of intervention results and activity time. The Hedges'g method was used to combine continuous data to analyze the standard MD (SMD) of different intervention types or time subgroups. The overall results show that physical activity intervention can improve the cognition, neuropsychiatric symptoms and quality of life (Qol) of AD patients, but the duration of the intervention significantly affected the outcome of the assessment. Subgroup analysis results showed that an intervention duration of 2-5 months had a significant advantage: cognitive function (Minimum Mental State Examination: SMD = 0.47, 95% CI = 0.33 ∼ 0.61, P < 0.01), neuropsychiatric symptoms (Neuropsychiatric Inventory: SMD = -0.48, 95% CI = -0.85 ∼-0.11, P < 0.01), and quality of life (Qol-AD: SMD = 0.47, 95% CI = 0.23 ∼ 0.71, P < 0.01). The systematic review and analysis results of updated RCTs suggested that short-term (2-5 months) physical activity interventions were more beneficial in improving cognitive function, neuropsychiatric symptoms and Qol in patients with AD. And there was no evidence of differences in the effectiveness of different physical activity interventions.

Towards a Stable and High-Performance Hindered Phenol/Polymer-Based Damping Material Through Structure Optimization and Damping Mechanism Revelation.

Although hindered phenol/polymer-based hybrid damping materials, with excellent damping performance, attract more and more attention, the poor stability of hindered phenol limits the application of such promising materials. To solve this problem, a linear hindered phenol with amorphous state and low polarity was synthesized and related polyurethane-based hybrid materials were prepared in this study. The structure and state of the hindered phenol were confirmed by nuclear magnetic resonance spectrum, Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD). The existence of intermolecular hydrogen bonds (HBs) between hindered phenol and polyurethane was confirmed by FT-IR, and the amorphous state of the hybrids was confirmed by XRD. Moreover, by a combination of molecular dynamics simulation and dynamic mechanical analysis, the relationship between the structure optimization of the hindered phenol and the high damping performance of the hybrids was quantitatively revealed. By constructing the synthetic hindered phenol, the intramolecular HBs between hindered phenols were restricted, while the strength and concentration of the intermolecular HBs increased by increasing the amount of hindered phenol. Thus, intermolecular interactions were enhanced, which lead to the compact chain packing of polyurethane, extended chain packing of hindered phenol, and good dispersion of hindered phenol in polyurethane. Therefore, the stability of the hindered phenol and the damping properties of the hybrids were both improved. The experiment results are expected to provide some useful information for the design and fabrication of high-performance polymeric damping materials.