Sökning: "MAINTENANCE DATA"

Visar resultat 1 - 5 av 1213 uppsatser innehållade orden MAINTENANCE DATA.

  1. 1. Data analysis for predictive maintenance and potential challenges associated with the technology integration of steel industry machines.

    Master-uppsats, Högskolan i Gävle/Elektronik

    Författare :Pradip Nath; [2024]
    Nyckelord :Data Science; Data processing; Industrial Manufacturing; System Identification; Predictive maintenance; Conditional monitoring; Statistical Analysis; Signal processing; Hydraulic System; IoT; Sustainable Maintenance; Data vetenskap; Databehandling; Industriell tillverkning; System identifiering; Prediktivt underhåll; Tillståndsövervakning; Statistisk analys; Signal behandling;

    Sammanfattning : The recharge is the focus of data analysis of the different situations with the integration of the system and development of the two-stage 2/2 proportional cartridge valve for the steel industry machine. Using the statistical analysis technique to visualize the valve signal data behavior identify the accuracy of the machine data and apply the statistical feature extracting model using classification and clustering algorithms of real-time data analysis for the manufacturing. LÄS MER

  3. 2. Data analytics and machine learning for railway track degradation: Using Bothnia Line track measurements for maintenance forecasting

    Master-uppsats, KTH/Väg- och spårfordon samt konceptuell fordonsdesign

    Författare :Elie Roudiere; [2024]
    Nyckelord :Railway; Track geometry; Machine learning; Statistics; Predictive maintenance; Botniabanan; Järnväg; spårgeometri; maskininlärning; statistik; förebyggande underhåll; Botniabanan;

    Sammanfattning : In this paper, a statistical method is developed to improve predictive maintenance on railway track. The problem tackled is being able to predict when the next maintenance event should take place to guarantee a certain track quality class. LÄS MER

  4. 3. Implementing End-to-End MLOps for Enhanced Steel Production

    M1-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

    Författare :Marcus Westin; Jacob Berggren; [2024]
    Nyckelord :MLOps; Azure ML; Machine Learning; Computer Science; Microsoft Azure; MLOps; Azure ML; Maskininlärning; Datavetenskap; Microsoft Azure;

    Sammanfattning : Steel production companies must utilize new technologies and innovations to stay ahead of a highly competitive market. Recently, there has been a focus on Industry 4.0, which involves the digitalization of production to integrate with newer technologies such as cloud solutions and the Internet of Things (IoT). LÄS MER

  5. 4. Förbättringsförslag för kvalitetssäkring vid svetsning : En kvalitativ studie för implementation av kvalitetssystemet ISO 3834

    Kandidat-uppsats, Mittuniversitetet/Institutionen för kommunikation, kvalitetsteknik och informationssystem (2023-)

    Författare :Jonathan Sjödin; [2024]
    Nyckelord :Quality improvement; ISO 3834-2; Welding quality; Welding management; Cornerstone model; Swedish industry; Industry; Kvalitetsutveckling; ISO 3834–2; Svetskvalitet; Svetsledning; Hörnstensmodellen; svensk industri; Industri;

    Sammanfattning : Quality work has become important in recent decades for longterm profitability. ISO 3834 is a framework for achieving an effective quality system in welding. The advantage of using ISO 3834 is the ability to guarantee quality. Understanding Why quality assurance is important for companies, especially in welding, is crucial. LÄS MER

  7. 5. Time Series Forecasting on Database Storage

    Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

    Författare :Pranav Patel; [2024]
    Nyckelord :Machine Learning; Time Series Forecasting; Prediction; Neural Networks; CNN; RNN; Database Storage;

    Sammanfattning : Time Series Forecasting has become vital in various industries ranging from weather forecasting to business forecasting. There is a need to research database storage solutions for companies in order to optimize resource allocation, enhance decision making process and enable predictive data storage maintenance. LÄS MER