A Feasible Framework for Maintenance Digitalization

The entire industry is changing as a result of new developments in digital technology, and maintenance management is a crucial procedure that may take advantage of the opportunities brought about by industrial digitalization. To support digital innovation in maintenance management, this study intends to meet the cutting-edge necessity of addressing a transformation strategy in industrial contexts. Setting up a customized pathway with adequate methodologies, digitalization tools, and collaboration between the several stakeholders involved in the maintenance environment is the first step in this process. The results of a previous conference contribution, which revealed important digitalization variables in maintenance management, served as the foundation for the research approach herein suggested. We lead a thorough assessment of the literature to categorize the potential benefits and challenges in maintenance digitalization to be assessed in conjunction with the important digitalization aspects previously stated. As a starting point for maintenance management transformation, we offer a feasible framework for maintenance digitalization that businesses operating in a variety of industries can use. As industrial processes and machines have become more sophisticated and complex and as there is a growing desire for more secure, dependable, and safe systems, we see that this transition needs to be tailored to the specific application context

Diversity and regulatory impact of copy number variation in the primate Macaca fascicularis.

Copy number variations (CNVs) are a significant source of genetic diversity and commonly found in mammalian genomes. We have generated a genome-wide CNV map for Cynomolgus monkeys (Macaca fascicularis). This crab-eating macaque is the closest animal model to humans that is used in biomedical research. We show that Cynomolgus monkey CNVs are in general much smaller in size than gene loci and are specific to the population of origin. Genome-wide expression data from five vitally important organs demonstrates that CNVs in close proximity to transcription start sites associate strongly with expression changes. Among these eQTL genes we find an overrepresentation of genes involved in metabolism, receptor activity, and transcription. These results provide evidence that CNVs shape tissue transcriptomes in monkey populations, potentially offering an adaptive advantage. We suggest that this genetic diversity should be taken into account when using Cynomolgus macaques as models

Recombinant polypeptides for serology of malaria

We have evaluated 3 molecularly defined polypeptides encoded by encloned Plasmodium falciparum genes for their ability to serve as antigens for detecting antimalaria antibodies. The recombinant proteins correspond to (i) a conserved part of 190-200 kDa schizont merozoite surface component, (ii) the carboxy terminal part of the P. falciparum aldolase, and (iii) the 5·1 antigen. Antibodies were detected using enzyme-linked immunosorbent assays (ELISA) in a high percentage of sera from individuals from a malaria endemic area in The Gambia (up to 99% for some adult groups). These results were further improved, especially for detection of antimalaria antibodies in children, when a pool of all 3 polypeptides (ELISA MIXT) was used as antigen. This ELISA MIXT improves presently available assays for the detection of antimalaria antibodies directed against asexual blood stages in respect of standardization, sensitivity and specificit

Focal Point: Biotechnology: Technologies for Genome Analysis: Applications in Biomedical Research

With the deciphering of whole genomes, a tremendous amount of data has been made available for research. In order to analyze all this information within a reasonable time frame and at reasonable cost, new technologies in DNA analysis as well as enzyme assays have been developed in the last few years in a symbiosis of biological, automation and miniaturization technologies. Examples are chip technologies for DNA analysis and microplate enzyme assays for high-throughput screening. Applications in biomedical research and in microbial diagnostics were presented

Digital Transformation in Maintenance Management

The relationship between technology and maintenance is mutually beneficial since technology is continuously improving with consequent substantial advancements in the field of maintenance. Maintenance management may be effectively modernized through digitalization. Developing advanced technologies promotes indeed the possibility of maintaining a competitive and long-term position in this field. Digitalization is consistently transforming organizations by allowing them to use suitable technologies for collecting data automatically. Various equipment and components are nowadays capable of collecting their operating data over an extended period, which may yield a plethora of intriguing insights employing digitalization. However, to achieve effective prediction of any type of failure, maintenance management requires several smart technologies which offer wider applications for digitalization, including artificial intelligence (AI), big data, Internet of Things (IoT), digital twins, novel sensor technologies, data collection and distribution from various smart sensors, and investigating a lot of data utilizing machine/deep learning. Smart sensors facilitate the collection of large amounts of data to be effectively evaluated for enabling maintenance management and decision-making of more complex systems. The focus of this study is to investigate which type of data should have to be digitally collected for effectively implementing predictive maintenance policies. This can be identified by studying the latest trends of digitalization in maintenance management. Moreover, this study aims to elaborate a decision-making model supporting the implementation of maintenance management policies. This will be done by first identifying critical factors for maintenance management and secondly analyzing their mutual relationships in a structured way. In detail, a Fuzzy Cognitive Map (FCM) will be built to model such relations, in order to identify those factors having a greater influence on all the other ones. In this direction, this study may have positive impacts on economic, social, and environmental factors

Managerial decision making for complex service systems Optimization

The present paper deals with managerial decisions for Predictive Maintenance (PrdM) of complex service systems. We propose a Multi-Criteria Decision-Making (MCDM) approach aimed at sorting those failure modes potentially involving critical components into risk classes for interventions prioritisation and maintenance control. In this context, the sorting technique ELimination Et Choix Traduisant la REalité (ELECTRE) TRI is applied to support in finding the root causes that can be eliminated for failure prevention and/or minimization. This methodology presents the advantage to not rely on comparisons (as well as on their transitivity) between pairs of elements, simplifying computations for complex systems. To be sorted, decision elements are indeed compared with single reference profiles and the final assignment may constitute a valid alternative to the traditional ranking of failures achievable by other MCDM techniques and, among others, consistency-based methodologies. The proposed approach will be eventually applied to a case study from the industrial reality

An integrated methodological approach for optimising complex systems subjected to predictive maintenance

The present paper addresses the relevant topic of maintenance management, widely recognised as a fundamental issue involving complex engineering systems and leading companies towards the optimisation of their assets while pursuing cost efficiency. With this regard, our research aims to provide companies with a hybrid methodological approach based on Multi-Criteria Decision-Making (MCDM) capable to deal with the main failures potentially involving complex systems subjected to predictive maintenance. Such an approach is going to be integrated within the framework of traditional Failure Mode Effects and Criticality Analysis (FMECA), whose strengths and weaknesses are considered. In particular, the ELimination Et Choix Traduisant la REalit\ue9 (ELECTRE) TRI is suggested to sort failure modes into risk priority classes while the Decision Making Trial and Evaluation Laboratory (DEMATEL) is proposed to highlight the most influencing failures within each risk class. The approach is applied to a real service system, whose critical components are monitored by sensors and subjected to predictive maintenance. Final results clearly demonstrate as highlighting the elements impacting the occurrence of other failures within specific risk classes is a significant driver towards the implementation of effective maintenance, maximising the whole level of performance of the analysed system over its lifecycle