A Novel Method for Intelligent Single Fault Detection of Bearings Using SAE and Improved D–S Evidence Theory

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster–Shafer evidence theory (D–S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors’ data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D–S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases; when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types’ fault detection accuracy—reached to 99.12%, 99.33% and 98.46% by the improved Dempster–Shafer evidence theory (IDS) to fuse the sensors’ results—is respectively 0.38%, 2.06% and 0.76% higher than the traditional D–S evidence theory. That indicated the effectiveness of improving the D–S evidence theory by evidence weight calculation of PCC

Modeling the effects of augmentation strategies on the control of Dengue fever with an impulsive differential equation

Dengue fever has rapidly become the world’s most common vector-borne viral disease. Use of endosymbiotic Wolbachia is an innovative technology to prevent vector mosquitoes from reproducing and so break the cycle of dengue transmission. However, strategies such as population eradication and replacement will only succeed if appropriate augmentations with Wolbachia-infected mosquitoes that take account of a variety of factors are carried out. Here, we describe the spread of Wolbachia in mosquito populations using an impulsive differential system with four state variables, incorporating the effects of cytoplasmic incompatibility and the augmentation of Wolbachia-infected mosquitoes with different sex ratios.We then evaluated (a) how each parameter value contributes to the success of population replacement; (b) how different release quantities of infected mosquitoes with different sex ratios affect the success of population suppression or replacement; and (c) how the success of these two strategies can be realized to block the transmission of dengue fever. Analysis of the system’s stability, bifurcations and sensitivity reveals the existence of forward and backward bifurcations, multiple attractors and the contribution of each parameter to the success of the strategies. The results indicate that the initial density of mosquitoes, the quantities of mosquitoes released in augmentations and their sex ratios have impacts on whether or not the strategies of population suppression or replacement can be achieved. Therefore, successful strategies rely on selecting suitable strains of Wolbachia and carefully designing the mosquito augmentation program

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions

The use of selected bacteria and yeasts to control vibrio spp. in live food

Vibrio species are a significant causative of mass mortality in mariculture worldwide, which can quickly accumulate in live food and transmit into the larval gut. With restrictions on the use of antibiotics in aquaculture, finding a proper solution to reduce the risk of Vibriosis is vital. This study aimed to evaluate the susceptibility of Vibrio harveyi, V. campbellii, V. anguillarum, and V. parahaemolyticus to twenty-six bacterial and yeast strains and use the beneficial ones to enrich live food (Branchiopod, Artemia franciscana, rotifer, Brachionus plicatilis and copepod, Tigriopus japonicus). Thus, a modified disk diffusion method was applied. After a susceptibility assay, the bacteria and yeast beneficial in suppressing the Vibrio species were labeled by fluorescent stain and used to measure the accumulation potential in different live foods. Also, the beneficial bacteria and yeast were used to enrich live foods, and then the count of loaded Vibrio was estimated after 5, 10, 15, and 20 h by the serial dilution method. From the total bacteria and yeast strains that were used, Candida parapsilosis, Pseudoalteromonas flavipulchra, Lactobacillus sakei, Bacillus natto, and B. amyloliquefaciens inhibited all four Vibrio species. The results of microbial labeling showed that L. sakei in Artemia, C. parapsilosis in rotifers, and V. harveyi in copepods had the highest accumulation rate. The results of the estimation of loaded Vibrio in different live foods also showed that the use of beneficial bacteria and yeast each significantly reduced the count of Vibrio. Application of bacteria and yeast to suppress pathogenic Vibrio maybe a sustainable method for preventing this pathogen from harmfully invading aquaculture and may also aid in reducing the chances of antibiotic resistance in pathogenic Vibrio

Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring:A Review

Condition monitoring can reduce machine breakdown losses, increase productivity and operation safety, and therefore deliver significant benefits to many industries. The emergence of wireless sensor networks (WSNs) with smart processing ability play an ever-growing role in online condition monitoring of machines. WSNs are cost-effective networking systems for machine condition monitoring. It avoids cable usage and eases system deployment in industry, which leads to significant savings. Powering the nodes is one of the major challenges for a true WSN system, especially when positioned at inaccessible or dangerous locations and in harsh environments. Promising energy harvesting technologies have attracted the attention of engineers because they convert microwatt or milliwatt level power from the environment to implement maintenance-free machine condition monitoring systems with WSNs. The motivation of this review is to investigate the energy sources, stimulate the application of energy harvesting based WSNs, and evaluate the improvement of energy harvesting systems for mechanical condition monitoring. This paper overviews the principles of a number of energy harvesting technologies applicable to industrial machines by investigating the power consumption of WSNs and the potential energy sources in mechanical systems. Many models or prototypes with different features are reviewed, especially in the mechanical field. Energy harvesting technologies are evaluated for further development according to the comparison of their advantages and disadvantages. Finally, a discussion of the challenges and potential future research of energy harvesting systems powering WSNs for machine condition monitoring is made

Models to assess the effects of non-identical sex ratio augmentations of Wolbachia-carrying mosquitoes on the control of dengue disease

The introduction of endosymbiont Wolbachia into laboratory-reared mosquito populations,which are then released to mix with natural populations to prevent the mosquito vectors from reproducing and thus break the transmission cycle of dengue disease, is an innovative new technology. Field trials of Wolbachia-carrying mosquitoes have now been implemented in many countries where there have been the outbreaks of dengue disease. A mathematical model is proposed to investigate the effects of non-identical sex ratio releases of Wolbachia carrying mosquitoes on the control of dengue transmission. Firstly, we analyzed the existence and stability of equilibria for the system and proved the existence of forward and backward bifurcations. Secondly, bifurcation diagrams, the basins of attraction of the equilibria and the effects of mosquito augmentation for the system with imperfect and perfect transmission rates were obtained. Thirdly, three possible results for mosquito augmentation were summarized for different parameter regions. Further we explored an uncertainty and sensitivity analysis of solutions to estimate the effects of different parameter values on the success or failure of population replacement. Based on the above analysis, we considered a series of relevant issues such as (a) whether or not mosquito augmentation can ensure the success of population replacement? (b) If not, what are the parameter regions for the success or possible success of population replacement? (c) How does the initial density of natural mosquitoes and the quantity of mosquito augmentations affect the success of population replacement? (d) Whether all population replacements are effective for reducing the spread of dengue virus in the end? The results of this study will be helpful for public health authorities in designing proper strategies of mosquito augmentations for the control of dengue disease

Measured and perceived impacts of evidence-based leadership in nursing: a mixed-methods systematic review protocol

Introduction Despite the abundance of existing literature on evidence-based nursing practice, knowledge regarding evidence-based leadership, that is, leadership supported by an evidence-based approach, is lacking. Our aim is to conduct a mixed-methods systematic review with qualitative and quantitative studies to examine how evidence is used to solve leadership problems and to describe the measured and perceived effects of evidence-based leadership on nurses and nurse leaders and their performance as well as on organisational and clinical outcomes. Methods and analysis We will search the following databases with no year limit or language restrictions: CINAHL (EBSCO), Cochrane Library, Embase (Elsevier), PsycINFO (EBSCO), PubMed (MEDLINE), Scopus (Elsevier) and Web of Science. In addition, the databases for prospectively registered trials and other systematic reviews will be screened. We will include articles using any type of research design as long as the study includes a component of an evidence-based leadership approach. Three reviewers will independently screen all titles, abstracts and full-text articles and two reviewers will extract the data according to the appropriate checklists. The quality of each study will be appraised using specific appraisal tool fitting in study design used in each study. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) grid, PRISMA Protocols, Synthesis Without Meta-analysis and ENTREQ will guide the study process and reporting. Outcomes related to individual or group performance of nurses or nurse managers regarding leadership skills (e.g., communication skills), organisational outcomes (e.g., work environment, costs) and clinical outcomes (e.g., patient quality of life, treatment satisfaction) will be extracted and synthesised. Ethics and dissemination This systematic review will not include empirical data, and therefore, ethics approval will not be sought. The results of the review will be disseminated in a peer-reviewed scientific journal and in a conference presentation. PROSPERO registration number CRD42021259624.</p

Models to assess how best to replace dengue virus vectors with Wolbachia-infected mosquito populations

Dengue fever is increasing in importance in the tropics and subtropics. Endosymbiotic Wolbachia bacteria as novel control methods can reduce the ability of virus transmission. So, many mosquitoes infected with Wolbachia are released in some countries so that strategies for population replacement can be fulfilled. However, not all of these field trails are successful, for example, releases on Tri Nguyen Island, Vietnam in 2013 failed. Thus, we evaluated a series of relevant issues such as (a) why do some releases fail? (b) What affects the success of population replacement? And (c) Whether or not augmentation can block the dengue diseases in field trials. If not, how we can success be achieved? Models with and without augmentation, incorporating the effects of cytoplasmic incompatibility (CI) and fitness effects are proposed to describe the spread of Wolbachia in mosquito populations. Stability analysis revealed that backward bifurcations and multiple attractors may exist, which indicate that initial quantities of infected and uninfected mosquitoes, augmentation methods (timing, quantity, order and frequency) may affect the success of the strategies. The results show that successful population replacement will rely on selection of suitable strains of Wolbachia and careful design of augmentation methods