Machine learning methods are widely used for rolling bearing fault diagnosis. Most of them are based on a basic assumption that training and testing data are adequate and follow the same ...distribution. However, for bearings working under multiple working conditions, dynamic changes are inevitable and labelled vibration data are usually insufficient. To deal with the issues, a new fault diagnosis method using deformable convolutional neural network (CNN), deep long short-term memory (DLSTM) and transfer learning strategies is designed. Specifically, a model is constructed by integrating deformable CNN, DLSTM and dense layers. Among them, deformable CNN enhances the ability of standard CNNs for local feature extraction using fixed geometric structures. DLSTM further encodes the sequential information contained in the output of deformable CNN. Dense layers are applied to capture high-level features then classify the data samples as each fault type. The model is firstly pre-trained using data samples under one working condition. Then, transfer learning strategies are implemented to fine-tune the pre-trained model utilising very few samples of another working condition, enabling it to identify fault types of bearing under new condition. Experiments are conducted and results show that the presented model yields higher than comparative performance compared with state-of-the-art methods.
Unsupervised domain adaptation (UDA)-based methods have made great progress in mechanical fault diagnosis under variable working conditions. In UDA, three types of information, including class label, ...domain label, and data structure, are essential to bridging the labeled source domain and unlabeled target domain. However, most existing UDA-based methods use only the former two information and ignore the modeling of data structure, which make the information contained in the features extracted by the deep network incomplete. To tackle this issue, a domain adversarial graph convolutional network (DAGCN) is proposed to model the three types of information in a unified deep network and achieving UDA. The first two types of information are modeled by the classifier and the domain discriminator, respectively. In data structure modeling, a convolutional neural network (CNN) is first employed to exact features from input signals. After that, the CNN features are input to the proposed graph generation layer to construct instance graphs by mining the relationship of structural characteristics of samples. Then, the instance graphs are modeled by a graph convolutional network, and the maximum mean discrepancy metric is leveraged to estimate the structure discrepancy of instance graphs from different domains. Experimental results conducted on two case studies demonstrate that the proposed DAGCN can not only obtain the best performance among the comparison methods, but also can extract transferable features for domain adaptation. The code library is available at: https://github.com/HazeDT/DAGCN .
In order to provide an accurate State-Of-Health (SOH) estimation, a novel estimation method is proposed in this paper. In this work, some battery SOH relate features are selected theoretically, ...proved and then re-screened mathematically. These features can reflect the battery degeneration from different aspects. Also, a new training set design idea is proposed for Least Squares Support Vector Machine algorithm, thereby a model that is suitable for lithium-ion Battery SOH estimation under multi-working conditions can be built. Several lithium-ion battery degeneration testing datasets from National Aeronautics and Space Administration Ames Prognostics Center of Excellence are used to validate the proposed method. Results demonstrate both the superiority of the proposed method and its potential applicability as an effective SOH estimation method for embedded Battery Management System.
•A novel estimation method for State-Of-Health estimation is proposed.•Some features related to battery SOH are selected and re-screened mathematically.•A model for lithium-ion Battery SOH estimation under multi-working conditions is built.
The slow bimolecular recombination that drives three-dimensional lead-halide perovskites' outstanding photovoltaic performance is conversely a fundamental limitation for electroluminescence. Under ...electroluminescence working conditions with typical charge densities lower than 10
cm
, defect-states trapping in three-dimensional perovskites competes effectively with the bimolecular radiative recombination. Herein, we overcome this limitation using van-der-Waals-coupled Ruddlesden-Popper perovskite multi-quantum-wells. Injected charge carriers are rapidly localized from adjacent thin few layer (n≤4) multi-quantum-wells to the thick (n≥5) multi-quantum-wells with extremely high efficiency (over 85%) through quantum coupling. Light emission originates from excitonic recombination in the thick multi-quantum-wells at much higher decay rate and efficiency than bimolecular recombination in three-dimensional perovskites. These multi-quantum-wells retain the simple solution processability and high charge carrier mobility of two-dimensional lead-halide perovskites. Importantly, these Ruddlesden-Popper perovskites offer new functionalities unavailable in single phase constituents, permitting the transcendence of the slow bimolecular recombination bottleneck in lead-halide perovskites for efficient electroluminescence.
Given that teacher shortage is an international problem, teacher job satisfaction merits closer attention. Not only is job satisfaction closely related to teacher retention, but it also contributes ...to the well-being of teachers and their students, overall school cohesion and enhanced status of the teaching profession. This study investigates the relations between teacher job satisfaction, school working conditions and teacher characteristics for eighth grade mathematics teachers. The study employs TIMSS 2015 (Trends in International Mathematics and Science Study) data from Sweden. Confirmatory factor analysis and structural equation modelling are used as main methods. Results demonstrate a substantial association between school working conditions and teacher job satisfaction. More specifically, teacher workload, teacher cooperation and teacher perceptions of student discipline in school were the factors most closely related to teacher job satisfaction. As to teacher characteristics, female teachers, teachers with more exposure to professional development and more efficacious teachers tended to have higher levels of job satisfaction. In addition, it was found that the relationship between the extent of teacher cooperation and job satisfaction was more pronounced for male teachers, while student discipline was more important for job satisfaction of teachers with lower self-efficacy beliefs. Implications for policy are further discussed.
Deep learning-based bearing fault diagnosis has been systematically studied in recent years. However, the success of most of these methods relies heavily on massive labeled data, which is not always ...available in real production environments. Training a robust bearing fault diagnosis model with limited data and working well under complex working conditions remains a challenge. In this paper, a novel meta-learning fault diagnosis method (MLFD) based on model-agnostic meta-learning is proposed to address this issue. The raw signals of different working conditions are first converted to time–frequency images and then randomly sampled to form tasks for MLFD according to the protocol of meta-learning. The MLFD model acquires prior knowledge by optimizing initialization parameters based on multiple fault classification tasks of known working conditions during the meta-training process, and achieves fast and accurate few-shot bearing fault diagnosis under unseen working conditions by leveraging the learned knowledge. To comprehensively evaluate the performance of our method, a series of experiments were conducted to simulate different industrial scenarios based on the Case Western Reserve University Bearing Fault Benchmark, and the results demonstrate the superiority of MLFD in solving the few-shot fault classification problem under complex working conditions.
•A Wasserstein distance-based weighted domain adversarial neural network (WD-WDANN) is proposed for RUL prediction.•Adaptive sample weights are utilized fully in the process of feature extraction and ...feature alignment to optimize specifically for a target domain.•Wasserstein distance has been introduced to solve the theoretical risk of gradient explosion.•Transfer learning based on the WD-WDANN has been effectively validated in the RUL prediction.
Various transfer learning methods have been applied in the remaining useful life estimation of bearings to reduce the data distribution discrepancy under different working conditions. However, the transferability of the sample (i.e., the sample quality) is always ignored. Low-quality samples caused by noise and outliers inevitably exist in the industrial data, which may negatively affect feature extraction and alignment. This article proposes a Wasserstein distance-based weighted domain adversarial neural network to utilize sample quality which is measured by the domain classifier. The feature extractor tends to learn the representations from the samples with cross-domain similarity. Feature alignment is fine-tuned according to the sample weights. The effectiveness of the proposed method is validated using IEEE PHM Challenge 2012 dataset. The comparison results prove the features extracted from the proposed approach are more domain-invariant.
Home-Based Telework and Presenteeism Across Europe Steidelmüller, Corinna; Meyer, Sophie-Charlotte; Müller, Grit
Journal of occupational and environmental medicine,
2020-December, Volume:
62, Issue:
12
Journal Article
Peer reviewed
Open access
Flexible work arrangements such as telework are gaining importance. Although telework is accompanied by advantages for employees such as increased flexibility, current research reveals associations ...between home-based telework and self-endangering behavior such as sickness presenteeism. As empirical evidence is still scarce, we explore the relationship between home-based telework and sickness presenteeism across Europe.
We perform multilevel analyses including 25,465 individuals who responded to the 6th wave of the European Working Conditions Survey 2015.
Home-based telework is positively related to sickness presenteeism. The results are quite robust across different measures of sickness presenteeism and to several sensitivity analyses.
Although sickness presenteeism can be functional for specific illnesses, organizations should be aware of possible risks related to home-based telework. They should design telework in a way that it reduces triggers for self-endangering behavior.
In this article, we examine how remote gig workers in Africa exercise agency to earn and sustain their livelihoods in the gig economy. In addition to the rewards reaped by gig workers, they also face ...significant risks, such as precarious working conditions and algorithmic workplace monitoring, thus constraining workers’ autonomy and bargaining power. Gig workers, as a result, are expected to have fewer opportunities to exert their agency – particularly so for workers in Africa, where the high proportion of informal economy and a lack of employment opportunities in local labour markets already constrain workers’ ability to earn livelihoods. Instead, we demonstrate how remote workers in Africa manage various constraints on one of the world’s biggest gig economy platforms through their diverse everyday resilience, reworking and resistance practices (after Katz, 2004). Drawing from a rich labour geography tradition, which considers workers to ‘actively produce economic spaces and scales’, our main theoretical contribution is to offer a reformulation of Katz’s notions of ‘resistance’, ‘resilience’ and ‘reworking’ as everyday practices of gig workers best understood as ‘hidden transcripts’ of the gig economy (Scott, 1990). The article draws on in-depth interviews (N=65) conducted with remote workers during the fieldwork in five selected African countries.
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