Structuring Data for Intelligent Predictive Maintenance in Asset Management

Predictive maintenance (PdM) within asset management improves savings in operational cost, productivity, and safety management capabilities. While PdM can be administered using various methods, growing interest in Arti cial Intelligence (AI) has lead to current state of the art PdM relying on machine learning (ML) technology. Like other tools used in PdM for asset management, standards for applying ML technology for PdM are required. This work introduces a standard of practice in regards to usage of asset data to develop ML analytic tools for PdM. It provides a standard method for ensuring asset data is in a form conducive to ML algorithms, and ensuring retention of asset information necessary for optimum PdM during the data transform. In the ML domain, it has been proven through research initiatives that the data structure used to train and test ML algorithms has a great impact on their performance and accuracy. Using poorly trained models for estimation due to improper data usage, can leave some AI-based PdM tools vulnerable to high rates of inaccurate estimations. Thus, leading to value loss during an asset's life cycle.This research was supported by the Australian Government Research Training Program Scholarship, American Australian Association Education Fund Fellowship, National GEM Consortium, Sustain-Owner (Sustainable Design and Management of Industrial Assets through Total Value and Cost of Ownership), a project sponsored by the EU Framework Programme Horizon 2020, MSCA-RISE-2014: Marie Sk lodowska-Curie Actions Research and Innovation Staff Exchange (Rise) (grant agreement number 645733 Sustain-owner H2020-MSCA-RISE-2014

A social network of collaborating industrial assets

The IoT (Internet of Things) concept is being widely regarded as the fundamental tool of the next industrial revolution – Industry 4.0. As the value of data generated in social networks has been increasingly recognised, social media and the IoT have been integrated in areas such as product-design, traffic routing, etc. However, the potential of this integration in improving system-level performance in industrial environments has rarely been explored. This paper discusses the feasibility of improving system-level performance in industrial systems by integrating social networks into the IoT concept. We propose the concept of a social internet of industrial assets (SIoIA) which enables the collaboration between assets by sharing status data. We also identify the building blocks of SIoIA and characteristics of one of its important components – social assets. A sketch of the general architecture needed to enable a social network of collaborating industrial assets is proposed and two illustrative application examples are given.</jats:p

Multiple Sclerosis Risk Variant HLA-DRB1*1501 Associates with High Expression of DRB1 Gene in Different Human Populations

The human leukocyte antigen (HLA) DRB1*1501 has been consistently associated with multiple sclerosis (MS) in nearly all populations tested. This points to a specific antigen presentation as the pathogenic mechanism though this does not fully explain the disease association. The identification of expression quantitative trait loci (eQTL) for genes in the HLA locus poses the question of the role of gene expression in MS susceptibility. We analyzed the eQTLs in the HLA region with respect to MS-associated HLA-variants obtained from genome-wide association studies (GWAS). We found that the Tag of DRB1*1501, rs3135388 A allele, correlated with high expression of DRB1, DRB5 and DQB1 genes in a Caucasian population. In quantitative terms, the MS-risk AA genotype carriers of rs3135388 were associated with 15.7-, 5.2- and 8.3-fold higher expression of DQB1, DRB5 and DRB1, respectively, than the non-risk GG carriers. The haplotype analysis of expression-associated variants in a Spanish MS cohort revealed that high expression of DRB1 and DQB1 alone did not contribute to the disease. However, in Caucasian, Asian and African American populations, the DRB1*1501 allele was always highly expressed. In other immune related diseases such as type 1 diabetes, inflammatory bowel disease, ulcerative colitis, asthma and IgA deficiency, the best GWAS-associated HLA SNPs were also eQTLs for different HLA Class II genes. Our data suggest that the DR/DQ expression levels, together with specific structural properties of alleles, seem to be the causal effect in MS and in other immunopathologies rather than specific antigen presentation alone

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

A social network of collaborating industrial assets

The IoT (Internet of Things) concept is being widely regarded as the fundamental tool of the next industrial revolution – Industry 4.0. As the value of data generated in social networks has been increasingly recognised, social media and the IoT have been integrated in areas such as product-design, traffic routing, etc. However, the potential of this integration in improving system-level performance in industrial environments has rarely been explored. This paper discusses the feasibility of improving system-level performance in industrial systems by integrating social networks into the IoT concept. We propose the concept of a social internet of industrial assets (SIoIA) which enables the collaboration between assets by sharing status data. We also identify the building blocks of SIoIA and characteristics of one of its important components – social assets. A sketch of the general architecture needed to enable a social network of collaborating industrial assets is proposed and two illustrative application examples are given

Structuring Data for Intelligent Predictive Maintenance in Asset Management

Predictive maintenance (PdM) within asset management improves savings in operational cost, productivity, and safety management capabilities. While PdM can be administered using various methods, growing interest in Artificial Intelligence (AI) has lead to current state of the art PdM relying on machine learning (ML) technology. Like other tools used in PdM for asset management, standards for applying ML technology for PdM are required. This work introduces a standard of practice in regards to usage of asset data to develop ML analytic tools for PdM. It provides a standard method for ensuring asset data is in a form conducive to ML algorithms, and ensuring retention of asset information necessary for optimum PdM during the data transform. In the ML domain, it has been proven through research initiatives that the data structure used to train and test ML algorithms has a great impact on their performance and accuracy. Using poorly trained models for estimation due to improper data usage, can leave some AI-based PdM tools vulnerable to high rates of inaccurate estimations. Thus, leading to value loss during an asset's life cycle