•New dynamic predictive maintenance framework.•Complete process from data-driven prognostics to maintenance decisions.•New data-driven prognostics method based on the Long Short-Term Memory ...classifier.•Discussion of imperfect prognostics information impact on maintenance decisions.•Verification of the proposed methodology performance through a real application.
In Prognostic Health and Management (PHM) literature, the predictive maintenance studies can be classified into two groups. The first group focuses on the prognostics step but does not consider the maintenance decisions. The second group addresses the maintenance optimization question based on the assumptions that the prognostics information or the degradation models of the system are already known. However, none of the two groups provides a complete framework (from data-driven prognostics to maintenance decisions) investigating the impact of the imperfect prognostics on maintenance decision. Therefore, this paper aims to fill this gap of literature. It presents a novel dynamic predicive maintenance framework based on sensor measurements. In this framework, the prognostics step, based on the Long Short-Term Memory network, is oriented towards the requirements of operation planners. It provides the probabilities that the system can fail in different time horizons to decide the moment for preparing and performing maintenance activities. The proposed framework is validated on a real application case study. Its performance is highlighted when compared with two benchmark maintenance policies: classical periodic and ideal predicted maintenance. In addition, the impact of the imperfect prognostics information on maintenance decisions is discussed in this paper.
The identification of the active sites for the electrochemical reduction of CO2 (CO2RR) to specific chemical products is elusive, owing in part to insufficient data gathered on clean and atomically ...well‐ordered electrode surfaces. Here, ultrahigh vacuum based preparation methods and surface science characterization techniques are used with gas chromatography to demonstrate that subtle changes in the preparation of well‐oriented Cu(100) and Cu(111) single‐crystal surfaces drastically affect their CO2RR selectivity. Copper single crystals with clean, flat, and atomically ordered surfaces are predicted to yield hydrocarbons; however, these were found experimentally to favor the production of H2. Only when roughness and defects are introduced, for example by electrochemical etching or a plasma treatment, are significant amounts of hydrocarbons generated. These results show that structural and morphological effects are the key factors determining the catalytic selectivity of CO2RR.
Subtle changes in the preparation of well‐oriented Cu(100) and Cu(111) single‐crystal surfaces affect their CO2RR selectivity. Clean, flat, atomically ordered surfaces are predicted to yield hydrocarbons; but these actually favor production of H2. Only when roughness and defects are introduced, significant amounts of hydrocarbons are generated. Structural and morphological effects are the key factors determining the catalytic selectivity of CO2RR.
Having geographical proximity and a high volume of trade with China, the first country to record an outbreak of the new Coronavirus disease (COVID-19), Vietnam was expected to have a high risk of ...transmission. However, as of 4 April 2020, in comparison to attempts to containing the disease around the world, responses from Vietnam are seen as prompt and effective in protecting the interests of its citizens, with 239 confirmed cases and no fatalities. This study analyzes the situation in terms of Vietnam’s policy response, social media and science journalism. A self-made web crawl engine was used to scan and collect official media news related to COVID-19 between the beginning of January and April 4, yielding a comprehensive dataset of 14,952 news items. The findings shed light on how Vietnam—despite being under-resourced—has demonstrated political readiness to combat the emerging pandemic since the earliest days. Timely communication on any developments of the outbreak from the government and the media, combined with up-to-date research on the new virus by the Vietnamese science community, have altogether provided reliable sources of information. By emphasizing the need for immediate and genuine cooperation between government, civil society and private individuals, the case study offers valuable lessons for other nations concerning not only the concurrent fight against the COVID-19 pandemic but also the overall responses to a public health crisis.
Synthetic polymers have shown promise in combating multidrug‐resistant bacteria. However, the biological effects of sequence control in synthetic antimicrobial polymers are currently not well ...understood. As such, we investigate the antimicrobial effects of monomer distribution within linear high‐order quasi‐block copolymers consisting of aminoethyl, phenylethyl, and hydroxyethyl acrylamides made in a one‐pot synthesis approach via photoinduced electron transfer–reversible addition–fragmentation chain transfer polymerisation (PET‐RAFT). Through different combinations of monomer/polymer block order, antimicrobial and haemolytic activities are tuneable in a manner comparable to antimicrobial peptides.
The antimicrobial effects of monomer distribution within linear high‐order quasi‐block copolymers consisting of aminoethyl, phenylethyl, and hydroxyethyl acrylamides have been investigated. Sequence control results in bacterial genus specific killing.
The identification and isolation of genes underlying quantitative trait loci (QTLs) associated with agronomic traits in crops have been recently accelerated thanks to next-generation sequencing ...(NGS)-based technologies combined with plant genetics. With NGS, various revisited genetic approaches, which benefited from higher marker density, have been elaborated. These approaches improved resolution in QTL position and assisted in determining functional causative variations in genes. Examples of QTLs/genes associated with agronomic traits in crops and identified using different strategies based on whole-genome sequencing (WGS)/whole-genome resequencing (WGR) or RNA-seq are presented and discussed in this review. More specifically, we summarize and illustrate how NGS boosted bulk-segregant analysis (BSA), expression profiling, and the construction of polymorphism databases to facilitate the detection of QTLs and causative genes.
The swift development of next-generation sequencing (NGS) has accelerated quantitative trait locus (QTL) mapping and gene discovery in crops.
High-throughput NGS-based genotyping platforms provide an extensive capacity to develop comprehensive polymorphism datasets including SNPs, InDels, structural variations, and genomic rearrangement.
Whole-genome resequencing combined with bulk-segregant analysis offers a high density of informative SNPs, helping to detect QTLs without genotyping the entire mapping population. However, phenotyping of the entire population is still required.
RNA-seq is useful to simultaneously genotype and phenotype segregating populations, releasing genomic information on mRNA and expression levels.
Polymorphism databases generated by whole-genome sequencing using large-accession collections constitute new shared resources that facilitate QTL detection and gene discovery.
•Consider joint optimization of imperfect inspection and replacement decisions•Discretely formulate a continuous degradation process and observations•Propose an integrated quality-cost based ...imperfect inspection model•Develop a cost model integrating both inspection and maintenance•Compare the performance of different maintenance and inspections policies
The quality of condition monitoring is an important factor affecting the effectiveness of a condition-based maintenance program. It depends closely on implemented inspection and instrument technologies, and eventually on investment costs, i.e., a more accurate condition monitoring information requires a more sophisticated inspection, hence a higher cost. While numerous works in the literature have considered problems related to condition monitoring quality, (e.g., imperfect inspection models, detection and localization techniques, etc.) few of them focus on adjusting condition monitoring quality for condition-based maintenance optimization. In this paper, we investigate how such an adjustment can help to reduce the total cost of a condition-based maintenance program. The condition monitoring quality is characterized by the observation noises on the system degradation level returned by an inspection. A dynamic condition-based maintenance and inspection policy adapted to such a observation information is proposed and formulated based on Partially Observable Markov Decision Processes. The use and advantages of the proposed joint inspection and maintenance model are numerically discussed and compared to several inspection-maintenance policies through numerical examples.
For dealing with uncertainty in Remaining Useful Life (RUL) predictions, numerous studies in literature use stochastic models to characterize the degradation process and predict the RUL distribution. ...However, in practice, it is difficult to derive stochastic models to capture degradation mechanisms of complex physical systems. Besides, the outstanding achievements in sensing technologies have facilitated the development of data-driven methods. Among them, deep learning methods become one of the most popular trends in recent studies; but they usually provide point predictions without quantifying the output uncertainties. In this paper, we present a new probabilistic deep leaning methodology for uncertainty quantification of multi-component systems’ RUL. It is a combination of a probabilistic model and a deep recurrent neural network to predict the components’ RUL distributions. Then, using the information about the system’s architecture, the formulas to quantify system reliability or system-level-RUL uncertainty are derived. The performance of the proposed methodology is investigated through the benchmark data provided by NASA. The obtained results highlight the point prediction accuracy and the uncertainty management capacity of the proposed methodology. In addition, thanks to the explicit RUL distributions of components, the system reliability for different structures is obtained with high accuracy, especially for series structures.
•New data-driven methodology for dealing with uncertainties in prognostics.•Handling uncertainty propagation from component’s RUL to system reliability.•New approach based on combination between probabilistic model and neural network.•Discussion of data quantity impact on system prognostics uncertainty management.•Verification of proposed methodology performance through an engineering application.
•Proposition of a practical and effective method for condition monitoring.•Construction of a new health indicator based on different data-types.•Utilization of electrical signals to detect most ...electrical machine defects.•Fault classification based on an improved ANFIS model.•Application of the proposed methodology on various systems.
Smart manufacturing is one of the key parts of the fourth industry revolution (Industry 4.0). It offers promising perspectives for high reliability, availability, maintainability and safety production process, but also makes the systems more complex and challenging for health assessment. To deal with these challenges, one needs to develop a robust approach to monitor and assess the system health state. In this paper, a practical and effective method that can be applied for fault detection and diagnostics of a given system is developed. The proposed method relies on a pattern recognition technique based on the construction of a new health indicator. This health indicator, which can be applied to different types of sensor measurements, is fed to an Adaptive Neuro-Fuzzy Inference System (ANFIS) to detect the health states of the system and diagnose the causes. Furthermore, the performance and the robustness of the proposed method are highlighted by considering various case studies under numerous operating conditions.
Polymer hydrogels have been widely explored as therapeutic delivery matrices because of their ability to present sustained, localized and controlled release of bioactive factors. Bioactive factor ...delivery from injectable biopolymer hydrogels provides a versatile approach to treat a wide variety of diseases, to direct cell function and to enhance tissue regeneration. The innovative development and modification of both natural- (e.g., alginate (ALG), chitosan, hyaluronic acid (HA), gelatin, heparin (HEP), etc.) and synthetic- (e.g., polyesters, polyethyleneimine (PEI), etc.) based polymers has resulted in a variety of approaches to design drug delivery hydrogel systems from which loaded therapeutics are released. This review presents the state-of-the-art in a wide range of hydrogels that are formed though self-assembly of polymers and peptides, chemical crosslinking, ionic crosslinking and biomolecule recognition. Hydrogel design for bioactive factor delivery is the focus of the first section. The second section then thoroughly discusses release strategies of payloads from hydrogels for therapeutic medicine, such as physical incorporation, covalent tethering, affinity interactions, on demand release and/or use of hybrid polymer scaffolds, with an emphasis on the last 5 years.
•Overview of Lévy processes for degradation modeling and RUL prediction.•Survey of classic criteria and prognostic criteria for model selection.•Introduction of a new hybrid criterion for model ...selection.•Discussion of performances and limits of criteria through numerical examples.
Health monitoring data are increasingly collected and widely used for reliability assessment and lifetime prediction. They not only provide information about degradation state but also could trace failure mechanisms of assets. The selection of a deterioration model that optimally fits in with health monitoring data is an important issue. It can enable a more precise asset health prognostic and help reducing operation and maintenance costs. Therefore, this paper aims to address the problem of degradation model selection including goals, procedure and evaluation criteria. Focusing on continuous degradation modeling including some currently used Lévy processes, the performance of classical and prognostic criteria are discussed through numerous numerical examples. We also investigate in what circumstances which methods perform better than others. The efficiency of a new hybrid criterion is highlighted that allows to take into account the information of goodness-of-fit of observation data when evaluating prognostic measure.
PDF
