2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)最新文献

{"title":"Surface Modification of Liquid Silicone Rubber by Nitrogen Plasma Treatment","authors":"Tushar Kunder, R. Poojary, R. Pai","doi":"10.1109/ICMIAM56779.2022.10146813","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10146813","url":null,"abstract":"Liquid Silicone Rubber (LSR) is an elastomer which is widely used in manufacturing industries for variety of applications. LSR has excellent physical and chemical properties which makes it ideal for manufacturing of different components. Plasma treatment of LSR enhances its wettability and adhesivity by converting its hydrophobic surface into hydrophilic one. In this work, nitrogen plasma treatment of the LSR samples were carried out and the changes in the surfaces were observed. The effect of hydrophobic recovery was also studied. Samples were subjected to varying electric current and exposure time in the microwave plasma cleaner. Contact angle measurements before and after the treatment were carried out to analyze the surface changes of the nitrogen plasma treated LSR samples. The droplets of Type II water, tear solution and saline solution were used in this analysis. Clear differences were observed in the samples exposed to different plasma parameters and droplets. Only under moderate current level (10A-27A) short time interval (45-75 sec) hydrophobic recovery of LSR samples was delayed for Type II water and tear solution droplets used in the contact angle measurement. Hence surface properties of LSR can easily be changed and enhanced by nitrogen plasma treatment.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122093493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study on major overhaul or replacement decision of a pendulum ride (Gyro Swing - Claw) at theme park","authors":"Senarath Godage Ravindra C. Madawala, G. Chattopadhyay, V. Chundhoo, H. Kandra, Adrian Summers","doi":"10.1109/ICMIAM56779.2022.10146991","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10146991","url":null,"abstract":"Routine maintenances are useful for reducing downtimes of rides in theme parks. Breakdown maintenances impact on the maintenance budget and customer experience and there is a possibility to minimize the unnecessary down time with the right decision on maintenance and replacements.Gyro swing is one of the oldest rides in the park studied in this paper. It will complete the 20 years of service life by 2024. Organization will have to allocate significant time, cost, and manpower for annual maintenance, shutdown for twenty-year overhaul or replacement. Cost for overhaul is estimated close to 2.23M AUD for the components, manpower, spare and services and 3.42M AUD for a replacement.This paper is on analysis and findings from a study conducted to evaluate the feasible economic decision on whether to overhaul or replace.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128075393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A neural network approach to establish the minimum film thickness in a multipad adjustable bearing","authors":"G. A, R. Pai, S. A. Abdul Khader, Girish H, Nitesh Kumar","doi":"10.1109/ICMIAM56779.2022.10147016","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10147016","url":null,"abstract":"Real-time fluid film modification technique is used in the active bearing technology to control the journal's equilibrium position for various operating conditions. This is achieved by the controlled movement of the flexible bearing element. The information of the minimum film thickness (MFT) is essential during the bearing element displacement to avoid the metal to metal contact. The bearing element-specific MFT develops in the multipad externally adjustable bearing with asymmetric combinations of adjustments. A single analytical expression fails to predict the multiple minimum film thickness for various bearing elements adjustment configurations. In this work, a neural network model is developed to predict the MFT in a multipad adjustable bearing having four adjustable bearing elements. Design of experiment and transformation technique is used to collect the data set between the eccentricity ratio 0.1 to 0.8. A feed-forward multilayer perceptron is used to model the MFT. The results show that an accurate estimation of the MFT is possible using a single neural network model.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133050256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a framework for generating realistic, but not real, synthetic maintenance records","authors":"Jo-ann Larkins, G. Chattopadhyay, Stephen Morey","doi":"10.1109/ICMIAM56779.2022.10146736","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10146736","url":null,"abstract":"Informed decisions for life-extension of complex long-life assets require knowledge of the current state of the asset as well as understanding of its maintenance and failure history. A lack of access to sufficient and reliable data for method validation, such as appropriately detailed maintenance logs, inhibits research into better-informed decisions for life-extension of long-life capital-intensive assets. Researchers must negotiate industry partnerships and overcome barriers to data access due to commercial sensitivities. This paper proposes a conceptual framework for generating realistic, but not necessarily real, synthetic maintenance records. Maintenance logs have strong structural synergies with electronic health records. We adapt and map methods for generating synthetic health records using only publicly available data sources to create synthetic maintenance records. Facet strings are used to construct a probabilistic framework for developing brief free notes. The generation of realistic synthetic maintenance records can be achieved using publicly available data, supported by expert engineering knowledge of the design and maintenance of the system.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124851475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facility mapping for Manipal Institute of Technology, Manipal Campus","authors":"B. Jagadeesha Pai, Hiya Chetia, Prathiksha Nayak, Raksha Shetty, Ashwini Kumar Prasad","doi":"10.1109/ICMIAM56779.2022.10146869","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10146869","url":null,"abstract":"Being a technology that encompasses the capacity to capture geographic and spatial data and their analysis, as well as being able to combine both digital maps and features of traditional database systems, GIS is used in a variety of projects in different fields of specialization. This paper contains a preliminary database containing coordinates of facilities in the campus of Manipal Institute of Technology, MAHE, Manipal, which were then used in the formation of Facility Maps using GIS concepts. This paper also attempts at compiling different applications of GIS products being used in the Education sector in order to showcase the potential of a further detailed spatial database for facility mapping in the campus.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129875543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of performability index of biogas plants using graph theory and matrix method","authors":"Haris Jamal, M. Loganathan, P. Ramesh","doi":"10.1109/ICMIAM56779.2022.10146867","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10146867","url":null,"abstract":"The production and use of biogas benefits both the participating farmers and the wider community in terms of providing clean environment and affordable energy in accordance with the requirements specified in the sustainable development goal 7 (SGD 7). Uninterrupted and safe production of quality gas by employing simple digesters is the sustainable solution to achieve SDG 7, which could be met by considering performability in design and evaluation of the plant. The performability of a biogas production unit is a function of factors such as sustainability, safety, maintainability, reliability and quality. The evaluation of performability index is highly desirable for monitoring the performance of the plant, thus ensuring dependable and sustainable operation over its whole life cycle. Although these factors help to evaluate the performability index, yet their interdependencies that influence the performability intangibly cannot be neglected. The performability evaluation without considering the interdependencies among these factors will yield wrong performability value. It is therefore important to consider the performability factors along with their interdependencies while evaluating the index, which is the measure of performance of the biogas production plants. Graph theory in conjunction with matrix method, which has been extensively used to derive such indices considering interdependencies, is employed to address this issue. The index is useful not only in design stage for developing an efficient biogas plant but also in operation to assess and improve its performability.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116537944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning Model for the Development of a Digital Twin for a Control Valve for Oil and Gas Pipelines","authors":"Ilyasu Anda, R. Mishra, S. Iram, A. Aliyu, F. Jackson, L. Fleming","doi":"10.1109/ICMIAM56779.2022.10146806","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10146806","url":null,"abstract":"Control valves are integral to the piping infrastructure in most process industries including nuclear, oil and gas, and chemical. They serve as pressure, temperature, and flow rate regulators. Hence detailed information about these parameters within the complex valve shape is particularly useful for design, optimisation, and efficient operation of control valves. For better reliability and operation under safety-critical installations, extensive research is being carried out to develop multi-layer and knowledge-intensive digital twins for such valves. This paper reports a computational investigation to determine the local valve flow coefficient (Cv) at various valve opening positions (VOP)/flow rate conditions and develop an artificial neural network (ANN) model to predict the local Cv distribution within the valve-pipe system. This is expected to provide sufficient information to embed extensive knowledge base in the valve’s digital twin. Ten internal locations (at L/D=1 to 10) were chosen for data extraction to give a detailed Cv profile from the pipe inlet through the valve to the pipe outlet. Three VOPs (20, 60, and 100%) were considered and for each, three inlet velocities of 1, 2 and 3 m/s were simulated. This combination resulted in a database of 90 data-points which were used to train the ANN using the Levenberg-Marquardt algorithm. MATLAB’s Neural Network Toolbox was used with 70%, 15%, and 15% of the data used for training, validation, and testing, respectively. Model performance was measured using the mean-square-error and square-of-residuals (R2) metrics. Sensitivity analysis of hidden layer neuron number was conducted, and the results show that 10 neurons produced a robust prediction of the CV profile. An overall average R2 of 0.92 was obtained. Hence, it may be efficiently used for the valve’s internal (rather than global) CV prediction for design and other purposes – including implementation within digital twin architectures for remote/condition monitoring in difficult to reach areas such as offshore and within complex industrial sites.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"947 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127009732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metaverse for Intelligent Asset Management","authors":"Ravdeep Kour, R. Karim, Amit Patwardhan, Manish Kumar, Hanna Eriksson, U. Kumar","doi":"10.1109/ICMIAM56779.2022.10146891","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10146891","url":null,"abstract":"Industries are utilising Artificial Intelligence (AI) and digital technologies to facilitate asset management. Increased implementation of AI and digital technologies requires new concepts to facilitate and simplify the Human- System-Interaction (HSI) in industrial contexts. One of the concepts for improving the HSI is by enabling a multi-space environment that integrates the physical space and the virtual space. Such a multi-space environment, materialised as metaverse, aims to augment the interaction between liveware, software, and hardware. In industry, the concept of metaverse can be used to facilitate various industrial processes, such as asset management, operation, and maintenance. One of the main challenges when enabling a metaverse in industrial contexts is the aspect of visualisation of physical and virtual assets. Asset visualisation can be supported through various emerging technologies such as gaming technology, Virtual Reality (VR), Augmented Reality (AR), and eXtented Reality (XR).In this paper, the applicability of such advanced technologies i.e., eXtended Reality (XR) have been reviewed for the asset management within various industrial domains. To carry out this research work, literature review has been conducted related to available XR technologies and their applications within the industries. It has been found that these technologies are available, but not applied in industrial asset management. Thus, the aim of this paper is to describe the developed Metaverse for asset management to enhance the operation and maintenance processes in industrial contexts utilising AI, digitalisation, and XR technologies.The developed Metaverse has been verified using a case study in railway using railway’s digital assets within eMaintenance LAB. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in various industrial domains.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129178361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of LSCP and Cluster Coverage Model for Emergency Train’s Assets Management:Use Case; North Eastern Railway","authors":"K. Mishra, Swastika Mishra, Abhyuday","doi":"10.1109/ICMIAM56779.2022.10146802","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10146802","url":null,"abstract":"Deployment of Accident Relief Medical Vans (ARMV) used for accident relief and rescue operations is generally done heuristically. ARMV is an ambulance-train equipped with various tools like jacks, heavy cutting tools, generators, operation theatre, artificial breathing equipment etc. to ease the rescue process. Taking North Eastern Railway (NER) as a case study, Location Set Covering Problem (LSCP) model will be used to find the minimum number of ARMVs required for the given network and their locations so that all demand points can be ‘covered’ within a maximum of one hour @ 100 kmph for each of the nearest facility. This paper proposes a new mathematical model based on the Cluster Coverage Model (CCM) to provide a better coverage of ARMV’s demands. We have used Girvan-Newman (GN) algorithm, a weight-based graph for clustering of NER network where nodes are taken as depots with ARMV parking facilities & crew availability and distance between these nodes is taken as edge weight of the graph. Finally, we present a comparison of results between the proposed cluster coverage models with LSCP and suggest relocations of the existing ARMVs and show that our proposed model is economical and provides a better coverage of ARMVs demands.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125450638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FauDigPro: A Machine Learning based Fault Diagnosis and Prognosis System for Electrocardiogram Sensors","authors":"Aakash Agarwal, Aparna Sinha, Debanjan Das","doi":"10.1109/ICMIAM56779.2022.10146898","DOIUrl":"https://doi.org/10.1109/ICMIAM56779.2022.10146898","url":null,"abstract":"Nowadays, sensors play a vital role in monitoring many appliances in various sectors, including the medical field. The electrocardiogram (ECG) is a test used to check the heart’s electric activity, using the ECG sensors. These sensors can get faulty and cause serious havoc. In this paper, a real-time classification and prognostics system is proposed that could classify between normal and faulty ECG signals and could even predict whether the sensor will get faulty based on previous data. Drift and bias fault are considered in this study. Four different machine learning algorithms, the Support Vector Machine, K-Nearest Neighbour, Decision Tree and Naive Bayes, are used and compared for classification purposes. Three statistical time-domain features, mean of the signal, energy of the signal and the peak-to-peak value of the signal, are used as the feature set in this study. The autoregression algorithm is used for fault prognosis. To replicate a practical scenario in an industrial system, an unbalanced dataset is generated having a larger number of normal signals than faulty signals. The model is implemented on raspberry pi for low resource hardware implementation. The performance of the classifiers is evaluated using different metrics. From the results, we can see that the KNN algorithm achieves the best accuracy of 95% which is suitable for real-time deployment. We also see that a very good result in the case of fault prognosis is achieved owing to the fact that the faults introduced are linear in nature. Different Root Mean Squared Error-values are presented in the paper for both faults.","PeriodicalId":311353,"journal":{"name":"2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114343510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}