A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection

• URL: http://arxiv.org/abs/2401.11860v1
• Date: Mon, 22 Jan 2024 11:29:44 GMT
• Title: A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection
• Authors: Yuandi Wu, Brett Sicard, and Stephen Andrew Gadsden
• Abstract summary: PIML is the incorporation of known physical laws and constraints into machine learning algorithms. This study presents a comprehensive overview of PIML techniques in the context of condition monitoring.
• Score: 1.124958340749622
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study presents a comprehensive overview of PIML techniques in the context of condition monitoring. The central concept driving PIML is the incorporation of known physical laws and constraints into machine learning algorithms, enabling them to learn from available data while remaining consistent with physical principles. Through fusing domain knowledge with data-driven learning, PIML methods offer enhanced accuracy and interpretability in comparison to purely data-driven approaches. In this comprehensive survey, detailed examinations are performed with regard to the methodology by which known physical principles are integrated within machine learning frameworks, as well as their suitability for specific tasks within condition monitoring. Incorporation of physical knowledge into the ML model may be realized in a variety of methods, with each having its unique advantages and drawbacks. The distinct advantages and limitations of each methodology for the integration of physics within data-driven models are detailed, considering factors such as computational efficiency, model interpretability, and generalizability to different systems in condition monitoring and fault detection. Several case studies and works of literature utilizing this emerging concept are presented to demonstrate the efficacy of PIML in condition monitoring applications. From the literature reviewed, the versatility and potential of PIML in condition monitoring may be demonstrated. Novel PIML methods offer an innovative solution for addressing the complexities of condition monitoring and associated challenges. This comprehensive survey helps form the foundation for future work in the field. As the technology continues to advance, PIML is expected to play a crucial role in enhancing maintenance strategies, system reliability, and overall operational efficiency in engineering systems.

Related papers

• MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science [62.96434290874878]
  Current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks. We develop a new framework, named Multi-Modal Scientific Reasoning with Physics Perception and Simulation (MAPS) based on an MLLM. MAPS decomposes expert-level multi-modal reasoning task into physical diagram understanding via a Physical Perception Model (PPM) and reasoning with physical knowledge via a simulator.
  arXiv  Detail & Related papers  (2025-01-18T13:54:00Z)
• Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics [1.1510009152620668]
  An underexplored aspect of machine learning is how to develop minimally-optimal representations. Our own view is that ML-based modeling should be based in use of computational units that are fundamentally interpretable by design. We show, in the context of lumped catchment modeling, that physical interpretability and predictive performance can both be achieved using a relatively parsimonious distributed-state network.
  arXiv  Detail & Related papers  (2024-12-06T08:30:01Z)
• Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach [0.0]
  In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions. In practice, a fundamental challenge arises when the system needs to be operationalized and deployed to evolve and operate in real-life environments continuously. To address this challenge, Machine Learning Operations (MLOps) have emerged as a potential recipe for standardizing ML solutions in deployment.
  arXiv  Detail & Related papers  (2024-10-28T09:34:08Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems [3.0309252269809264]
  This paper investigates and explores Interpretable Machine Learning (IML), a branch of Machine Learning (ML) that enhances transparency and understanding of models and their inferences. We develop an innovative framework that combines the principles of Shapley values and the in-context learning feature of Large Language Models (LLMs) The paper presents a case study to demonstrate the feasibility of the developed IML framework for model predictive control-based precooling under demand response events in a virtual testbed.
  arXiv  Detail & Related papers  (2024-02-14T21:19:33Z)
• Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review [1.3812010983144802]
  This paper presents a systematic review on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining. We provide a comprehensive overview of the current methodologies and their applications across various application domains. Our findings aim to equip researchers and practitioners with a deeper understanding of how to develop and implement more trustworthy, transparent, and effective intelligent systems for process analytics.
  arXiv  Detail & Related papers  (2023-12-29T12:43:43Z)
• Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems [0.0]
  Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints. The basic premise of PIML is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in PIML for dynamical system modeling and control.
  arXiv  Detail & Related papers  (2023-06-24T05:24:48Z)
• Learning Physical Concepts in Cyber-Physical Systems: A Case Study [72.74318982275052]
  We provide an overview of the current state of research regarding methods for learning physical concepts in time series data. We also analyze the most important methods from the current state of the art using the example of a three-tank system.
  arXiv  Detail & Related papers  (2021-11-28T14:24:52Z)
• An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
  We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols. We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
  arXiv  Detail & Related papers  (2021-08-09T17:39:09Z)
• Practical Machine Learning Safety: A Survey and Primer [81.73857913779534]
  Open-world deployment of Machine Learning algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities. New models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. Our organization maps state-of-the-art ML techniques to safety strategies in order to enhance the dependability of the ML algorithm from different aspects.
  arXiv  Detail & Related papers  (2021-06-09T05:56:42Z)
• A Survey on Large-scale Machine Learning [67.6997613600942]
  Machine learning can provide deep insights into data, allowing machines to make high-quality predictions. Most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. Large-scale Machine Learning aims to learn patterns from big data with comparable performance efficiently.
  arXiv  Detail & Related papers  (2020-08-10T06:07:52Z)

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.