The transfer learning method represented by domain adaptation (DA) can effectively improve the prediction accuracy of rolling bearings' remaining useful life (RUL) under different working conditions. ...However, the difference in the bearing degradation process under the same working conditions limits the reliability and generalization of the transfer RUL prediction model. Owing to the aforementioned problems, this study proposed an RUL transfer predicting method for rolling bearings based on working conditions' common benchmark. An attention mechanism autoencoder is proposed to extract the common benchmark under each working condition and improve the commonness between deep features. The dynamic benchmark constraint under the same working conditions was proposed to ensure the reliability of a common benchmark and improve the prediction accuracy in the process of benchmark transfer under different working conditions. Verified by XJTU-SY bearing datasets, the proposed method can effectively obtain a common benchmark that can be used for DA under various working conditions. In the experimental design of six sets of RUL prediction tests under different working conditions, more than 50% of the experimental tasks have better prediction results using the proposed method. The proposed approach increases the overall prediction accuracy to 11.74% compared with the method without DA. Experimental results show that the proposed method can better meet the needs of intelligent operation and maintenance in practical engineering.
Laser powder bed fusion (LPBF) is an additive manufacturing technology with high practical value. In order to improve the quality of the fabricated parts, process monitoring has become a crucial ...solution, offering the potential to ensure manufacturing stability and repeatability. However, a cardinal challenge involves discerning a precise correlation between process characteristics and potential defects. This paper elucidates the integration of an off-axis vision monitoring mechanism via a high-speed camera focused on capturing the single-track melting phenomenon. An innovative image processing method was devised to segment the plume and spatters, while Kalman filter was employed for multi-object tracking of the spatters. The features of both the plume and spatters were extracted, and their relationship with molten states was investigated. Finally, the PSO-XGBoost algorithm was utilized to identify five molten states, achieving an accuracy of 92.16%. The novelty of this approach resides in its unique combination of plume characteristics, spatter features, and computationally efficient machine learning models, which collectively address the challenge of limited field of view prevalent in real production scenarios, thereby enhancing process monitoring efficacy. Relative to existing methodologies, the proposed PSO-XGBoost approach offers heightened accuracy, convenience, and appropriateness for the monitoring of the LPBF process. This work provides an effective and novel approach to monitor the LPBF process and evaluate the part fabrication quality for complex and changeable working conditions.
•One in two PhD students experiences psychological distress; one in three is at risk of a common psychiatric disorder.•The prevalence of mental health problems is higher in PhD students than in the ...highly educated general population, highly educated employees and higher education students.•Work and organizational context are significant predictors of PhD students’ mental health.
Research policy observers are increasingly concerned about the potential impact of current academic working conditions on mental health, particularly in PhD students. The aim of the current study is threefold. First, we assess the prevalence of mental health problems in a representative sample of PhD students in Flanders, Belgium (N=3659). Second, we compare PhD students to three other samples: (1) highly educated in the general population (N=769); (2) highly educated employees (N=592); and (3) higher education students (N=333). Third, we assess those organizational factors relating to the role of PhD students that predict mental health status. Results based on 12 mental health symptoms (GHQ-12) showed that 32% of PhD students are at risk of having or developing a common psychiatric disorder, especially depression. This estimate was significantly higher than those obtained in the comparison groups. Organizational policies were significantly associated with the prevalence of mental health problems. Especially work-family interface, job demands and job control, the supervisor’s leadership style, team decision-making culture, and perception of a career outside academia are linked to mental health problems.
Objectives The aims of this position paper are to (i) summarize research on precarious employment (PE) in the context of occupational health; (ii) develop a theoretical framework that distinguishes ...PE from related concepts and delineates important contextual factors; and (iii) identify key methodological challenges and directions for future research on PE and health. Methods This position paper is the result of a working group consisting of researchers from the EU, Turkey and the USA, who have discussed the issue over the course of six months (October 2018-April 2019), meeting both online and face-to-face on several occasions. Results The lack of a common theoretical framework of PE hinders it from becoming an established part of occupational and public health research. There are also issues regarding operationalization in surveys and registers. Further, previous research on PE and health suffers from methodological limitations including inadequate study designs and biased assessments of exposure and outcomes. PE is highly dependent on contextual factors and cross-country comparison has proven very difficult. We also point to the uneven social distribution of PE, ie, higher prevalence among women, immigrants, young and low educated. We propose a theoretical framework for understanding precarious employment as a multidimensional construct. Conclusions A generally accepted multidimensional definition of PE should be the highest priority. Future studies would benefit from improved exposure assessment, temporal resolution, and accounting for confounders, as well as testing possible mechanisms, eg, by adopting multi-level and intersectional analytical approaches in order to understand the complexity of PE and its relation to health.
Friction-induced wear is a major cause of energy consumption and equipment failure and graphene as a novel solid lubricant has become a hot topic in tribological engineering. Micro/nanoscale and ...macroscale superlubricity has been observed from graphene-based solid lubricants and the ability to mass produce high-quality graphene by chemical vapor deposition (CVD) is attractive, especially for applications requiring operation under harsh working conditions. This comprehensive review discusses the relationship between the structure and friction properties of solid graphene lubricants, mechanisms of macroscale superlubricity, applications pertaining to harsh working conditions, strategies to prolong macroscale superlubricity, as well as challenges in order to provide guidance for future research and development of graphene-based solid lubricants.
•A metric adversarial domain adaptation approach is proposed to successfully achieve cross-domain RUL prediction.•A feature extraction scheme with a dual self-attention module is developed to learn ...features with multi-scale semantics.•A supervised positive contrastive module is designed to maximize the target-specific mutual information.
Many existing domain adaptation-based methods try to derive domain invariant features to address domain shifts and obtain satisfactory remaining useful life (RUL) of bearings under multiple working conditions. However, most methods may not consider local semantics about degradation features and mutual information from target-specific data when aligning distribution discrepancies, thus resulting in limitations. Additionally, the use of contrastive learning to maintain mutual information may introduce unstable negative samples. To overcome these issues, a metric adversarial domain adaptation approach (MADA) is proposed to evaluate the bearing RULs under multiple working conditions. More specifically, an adversarial domain adaptation architecture with a supervised positive contrastive module is developed to consider mutual information without a negative sample, further learning domain invariant features. Also, the dual self-attention module is designed to extract multi-scale contextual semantics between degradation features. Meanwhile, extensive experiments are conducted in twelve cross-domain scenarios for two bearing cases. The experimental results show that the proposed method is more competitive.
•A novel method called DRHRML is proposed for bearing fault diagnosis with small samples under different working conditions.•Improved sparse denoising autoencoder (ISDAE) is proposed to preprocess ...the raw vibration data.•Two novel task datasets are constructed for verifying the proposed method.
Recently, intelligent fault diagnosis has made great achievements, which has aroused growing interests in the field of bearing fault diagnosis due to its strong feature learning ability. Sufficient bearing fault samples are taken for granted in existing intelligent fault diagnosis methods generally. In practice, however, the lack of fault samples has been a knotty problem. Therefore, in this paper, a novel method called data reconstruction hierarchical recurrent meta-learning (DRHRML) is proposed for bearing fault diagnosis with small samples under different working conditions. This approach contains data reconstruction and meta-learning stages. In the data reconstruction stage, noise is reduced and the useful information hidden in the raw data is extracted. In the meta-learning stage, the proposed method is trained by a recurrent meta-learning strategy with one-shot learning way. This approach is demonstrated on the bearing fault database with 92 working conditions from Case Western Reserve University and with 56 working conditions from laboratory. Results show that the proposed method is effective for bearing intelligent fault diagnosis with small samples under different working conditions.
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.
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