VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

• URL: http://arxiv.org/abs/2103.17003v1
• Date: Wed, 31 Mar 2021 11:35:51 GMT
• Title: VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance
• Authors: Spyridon Paraschos, Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
• Abstract summary: Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.
• Score: 5.845912816093006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensure continuous operation to prevent money loss. Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.

Related papers

• Verification of Machine Unlearning is Fragile [48.71651033308842]
  We introduce two novel adversarial unlearning processes capable of circumventing both types of verification strategies. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
  arXiv  Detail & Related papers  (2024-08-01T21:37:10Z)
• TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework [58.474610046294856]
  Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. This paper introduces an integrated framework that leverages the capabilities of the Transformer model-based neural networks and deep reinforcement learning (DRL) algorithms to optimize system maintenance actions.
  arXiv  Detail & Related papers  (2023-09-29T02:27:54Z)
• Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimization [1.6447597767676658]
  This paper presents a literature review on the applications of reinforcement and deep reinforcement learning for maintenance planning and optimization problems. By leveraging the condition monitoring data of systems and machines with reinforcement learning, smart maintenance planners can be developed, which is a precursor to achieving a smart factory.
  arXiv  Detail & Related papers  (2023-07-07T22:47:29Z)
• Safe AI for health and beyond -- Monitoring to transform a health service [51.8524501805308]
  We will assess the infrastructure required to monitor the outputs of a machine learning algorithm. We will present two scenarios with examples of monitoring and updates of models.
  arXiv  Detail & Related papers  (2023-03-02T17:27:45Z)
• Prescriptive maintenance with causal machine learning [4.169130102668252]
  We learn the effect of maintenance conditional on a machine's characteristics from observational data on similar machines. We validate our proposed approach using real-life data on more than 4,000 maintenance contracts from an industrial partner.
  arXiv  Detail & Related papers  (2022-06-03T13:35:57Z)
• Predictive Maintenance using Machine Learning [0.0]
  Predictive maintenance (PdM) is implemented to effectively manage maintenance plans of the assets. Data is collected over a certain period of time to monitor the state of equipment.
  arXiv  Detail & Related papers  (2022-05-19T09:05:37Z)
• 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)
• Explainable Machine Learning for Fraud Detection [0.47574189356217006]
  The application of machine learning to support the processing of large datasets holds promise in many industries, including financial services. In this paper, we explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background datasets and runtime trade-offs on both supervised and unsupervised models.
  arXiv  Detail & Related papers  (2021-05-13T14:12:02Z)
• Monitoring and explainability of models in production [58.720142291102135]
  Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. We discuss the challenges to successful implementation of solutions in each of these areas with some recent examples of production ready solutions using open source tools.
  arXiv  Detail & Related papers  (2020-07-13T10:37:05Z)
• 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.