Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations

• URL: http://arxiv.org/abs/2401.15088v1
• Date: Mon, 22 Jan 2024 08:06:04 GMT
• Title: Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations
• Authors: Abu Hanif Md. Ripon, Muhammad Ahsan Ullah, Arindam Kumar Paul, Md. Mortaza Morshed
• Abstract summary: In the era of the fourth industrial revolution, it is essential to automate fault detection and diagnosis of machineries. Some machines health monitoring systems are used globally but they are expensive and need trained personnel to operate and analyse. Predictive maintenance and occupational health and safety culture are not available due to inadequate infrastructure, lack of skilled manpower, financial crisis, and others in developing countries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the era of the fourth industrial revolution, it is essential to automate fault detection and diagnosis of machineries so that a warning system can be developed that will help to take an appropriate action before any catastrophic damage. Some machines health monitoring systems are used globally but they are expensive and need trained personnel to operate and analyse. Predictive maintenance and occupational health and safety culture are not available due to inadequate infrastructure, lack of skilled manpower, financial crisis, and others in developing countries. Starting from developing a cost-effective DAS for collecting fault data in this study, the effect of limited data and resources has been investigated while automating the process. To solve this problem, A feature engineering and data reduction method has been developed combining the concepts from wavelets, differential calculus, and signal processing. Finally, for automating the whole process, all the necessary theoretical and practical considerations to develop a predictive model have been proposed. The DAS successfully collected the required data from the machine that is 89% accurate compared to the professional manual monitoring system. SVM and NN were proposed for the prediction purpose because of their high predicting accuracy greater than 95% during training and 100% during testing the new samples. In this study, the combination of the simple algorithm with a rule-based system instead of a data-intensive system turned out to be hybridization by validating with collected data. The outcome of this research can be instantly applied to small and medium-sized industries for finding other issues and developing accordingly. As one of the foundational studies in automatic FDD, the findings and procedure of this study can lead others to extend, generalize, or add other dimensions to FDD automation.

Related papers

• QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis [1.9521598508325781]
  We introduce QUIS, a fully automated EDA system that operates in two stages: insight generation (ISGen) driven by question generation (QUGen) The ISGen module analyzes data to produce multiple relevant insights in response to each question, requiring no prior training and enabling QUIS to adapt to new datasets.
  arXiv  Detail & Related papers  (2024-10-14T08:21:25Z)
• Enhancing Automata Learning with Statistical Machine Learning: A Network Security Case Study [4.2751988244805466]
  In this paper, we use automata learning to derive state machines from network-traffic data. We apply our approach to a commercial network intrusion detection system developed by our industry partner, RabbitRun Technologies. Our approach results in an average 67.5% reduction in the number of states and transitions of the learned state machines.
  arXiv  Detail & Related papers  (2024-05-18T02:10:41Z)
• Comprehensive Study Of Predictive Maintenance In Industries Using Classification Models And LSTM Model [0.0]
  The study aims to delve into various machine learning classification techniques, including Support Vector Machine (SVM), Random Forest, Logistic Regression, and Convolutional Neural Network LSTM-Based, for predicting and analyzing machine performance. The primary objective of the study is to assess these algorithms' performance in predicting and analyzing machine performance, considering factors such as accuracy, precision, recall, and F1 score.
  arXiv  Detail & Related papers  (2024-03-15T12:47:45Z)
• Self-consistent Validation for Machine Learning Electronic Structure [81.54661501506185]
  Method integrates machine learning with self-consistent field methods to achieve both low validation cost and interpret-ability. This, in turn, enables exploration of the model's ability with active learning and instills confidence in its integration into real-world studies.
  arXiv  Detail & Related papers  (2024-02-15T18:41:35Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• Condition monitoring and anomaly detection in cyber-physical systems [0.483420384410068]
  This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. For supervised cases, we achieve near-perfect accuracy of 98 percent. In contrast, the best-case accuracy in the unsupervised cases was 63 percent.
  arXiv  Detail & Related papers  (2023-01-22T00:58:01Z)
• How Can Subgroup Discovery Help AIOps? [0.0]
  We study how Subgroup Discovery can help AIOps. This project involves both data mining researchers and practitioners from Infologic, a French software editor.
  arXiv  Detail & Related papers  (2021-09-10T14:41:02Z)
• Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System [78.60415450507706]
  Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
  arXiv  Detail & Related papers  (2021-07-28T10:28:05Z)
• Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation [54.88777449903538]
  We introduce a novel textithybrid automatic differentiation (AD) system for sensitivity analysis. This enables modelling the sensitivity of arbitrary differentiable function compositions, such as the training of neural networks on private data. Our approach can enable the principled reasoning about privacy loss in the setting of data processing.
  arXiv  Detail & Related papers  (2021-07-09T07:19:23Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach [68.40429597811071]
  The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets. A model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context. Unlike traditional black-box regression models, the proposed algorithm self-learns an optimal maintenance policy and provides actionable recommendation for each equipment.
  arXiv  Detail & Related papers  (2020-07-07T10:00:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.