Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

• URL: http://arxiv.org/abs/2401.07871v1
• Date: Mon, 15 Jan 2024 18:06:59 GMT
• Title: Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities
• Authors: Logan Cummins, Alex Sommers, Somayeh Bakhtiari Ramezani, Sudip Mittal, Joseph Jabour, Maria Seale, Shahram Rahimi
• Abstract summary: Methods allow maintainers of systems and hardware to reduce financial and time costs of upkeep. This attracts the field of Explainable AI (XAI) to introduce explainability and interpretability into the predictive system. XAI brings methods to the field of predictive maintenance that can amplify trust in the users while maintaining well-performing systems.
• Score: 2.913761513290171
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predictive maintenance is a well studied collection of techniques that aims to prolong the life of a mechanical system by using artificial intelligence and machine learning to predict the optimal time to perform maintenance. The methods allow maintainers of systems and hardware to reduce financial and time costs of upkeep. As these methods are adopted for more serious and potentially life-threatening applications, the human operators need trust the predictive system. This attracts the field of Explainable AI (XAI) to introduce explainability and interpretability into the predictive system. XAI brings methods to the field of predictive maintenance that can amplify trust in the users while maintaining well-performing systems. This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We categorize the different XPM methods into groups that follow the XAI literature. Additionally, we include current challenges and a discussion on future research directions in XPM.

Related papers

• Revolutionizing System Reliability: The Role of AI in Predictive Maintenance Strategies [0.0]
  The study explores how AI, especially machine learning and neural networks, is being used to enhance predictive maintenance strategies. The article provides insights into the effectiveness and challenges of implementing AI-driven predictive maintenance.
  arXiv  Detail & Related papers  (2024-04-20T19:31:05Z)
• X Hacking: The Threat of Misguided AutoML [2.3011205420794574]
  This paper introduces the concept of X-hacking, a form of p-hacking applied to XAI metrics such as Shap values. We show how an automated machine learning pipeline can be used to search for 'defensible' models that produce a desired explanation while maintaining superior performance to a common baseline.
  arXiv  Detail & Related papers  (2024-01-16T17:21:33Z)
• How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations [53.01494092422942]
  The number and complexity of personalised and user-centred approaches to XAI have rapidly grown in recent years. It emerged that user-centred approaches to XAI positively affect the interaction between users and systems. We propose an assessment task to objectively and quantitatively measure the goodness of XAI systems.
  arXiv  Detail & Related papers  (2023-12-07T15:49:39Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI. It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems [128.63953314853327]
  "Lifelong Learning" systems are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. We show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems.
  arXiv  Detail & Related papers  (2023-01-18T21:58:54Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it [10.85017740334476]
  We have analyzed the various building blocks needed to enable an AI-driven predictive maintenance use-case. Our survey can serve as a template needed to design a successful predictive maintenance use-case.
  arXiv  Detail & Related papers  (2021-10-11T05:56:03Z)
• Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls [0.0]
  This article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A) We review the current literature on machine learning in FP&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning.
  arXiv  Detail & Related papers  (2021-07-10T14:54:36Z)
• Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [54.478842696269304]
  The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. Our paper addresses both machine learning experts and safety engineers.
  arXiv  Detail & Related papers  (2021-04-29T09:54:54Z)
• Interpretable Predictive Maintenance for Hard Drives [0.5352699766206808]
  We consider the task of predicting hard drive failure in a data center using recent algorithms for interpretable machine learning. We demonstrate that these methods provide meaningful insights about short- and long-term drive health, while also maintaining high predictive performance.
  arXiv  Detail & Related papers  (2021-02-12T13:25:58Z)
• Unsupervised Quality Estimation for Neural Machine Translation [63.38918378182266]
  Existing approaches require large amounts of expert annotated data, computation and time for training. We devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. We achieve very good correlation with human judgments of quality, rivalling state-of-the-art supervised QE models.
  arXiv  Detail & Related papers  (2020-05-21T12:38:06Z)

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.