Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis

• URL: http://arxiv.org/abs/2311.03413v1
• Date: Mon, 6 Nov 2023 09:17:57 GMT
• Title: Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis
• Authors: Lukas Moddemann and Henrik Sebastian Steude and Alexander Diedrich and Oliver Niggemann
• Abstract summary: consistency-based diagnosis is an established approach to diagnose technical applications, but suffers from significant modeling efforts. This paper presents the methodology Discret2Di for automated learning of logical expressions for consistency-based diagnosis. The solution presented combines machine learning from both the time series and the symbolic domain to automate the learning of logical rules for consistency-based diagnosis.
• Score: 48.252498836623154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consistency-based diagnosis is an established approach to diagnose technical applications, but suffers from significant modeling efforts, especially for dynamic multi-modal time series. Machine learning seems to be an obvious solution, which becomes less obvious when looking at details: Which notion of consistency can be used? If logical calculi are still to be used, how can dynamic time series be transferred into the discrete world? This paper presents the methodology Discret2Di for automated learning of logical expressions for consistency-based diagnosis. While these logical calculi have advantages by providing a clear notion of consistency, they have the key problem of relying on a discretization of the dynamic system. The solution presented combines machine learning from both the time series and the symbolic domain to automate the learning of logical rules for consistency-based diagnosis.

Related papers

• No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs [56.78271181959529]
  This paper proposes a conceptual shift to modeling low-dimensional dynamical systems by departing from the traditional two-step modeling process. Instead of first discovering a closed-form equation and then analyzing it, our approach, direct semantic modeling, predicts the semantic representation of the dynamical system. Our approach not only simplifies the modeling pipeline but also enhances the transparency and flexibility of the resulting models.
  arXiv  Detail & Related papers  (2025-01-30T18:36:48Z)
• A Constant Velocity Latent Dynamics Approach for Accelerating Simulation of Stiff Nonlinear Systems [0.0]
  Solving stiff ordinary differential equations (StODEs) requires sophisticated numerical solvers, which are often computationally expensive. In this work, we embark on a different path which involves learning a latent dynamics for StODEs, in which one completely avoids numerical integration. In other words, the solution of the original dynamics is encoded into a sequence of straight lines which can be decoded back to retrieve the actual solution as and when required.
  arXiv  Detail & Related papers  (2025-01-14T20:32:31Z)
• stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic [0.5956301166481089]
  We propose a semantically grounded vector representation (feature embedding) of logic formulae. We compute continuous embeddings of formulae with several desirable properties. We demonstrate the efficacy of the approach in two tasks: learning model checking and neurosymbolic framework.
  arXiv  Detail & Related papers  (2024-05-23T10:04:56Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation [85.12429517510311]
  LogicDiag is a neural-logic semi-supervised learning framework for semantic segmentation. Our key insight is that conflicts within pseudo labels, identified through symbolic knowledge, can serve as strong yet commonly ignored learning signals. We showcase the practical application of LogicDiag in the data-hungry segmentation scenario, where we formalize the structured abstraction of semantic concepts as a set of logic rules.
  arXiv  Detail & Related papers  (2023-08-24T06:50:07Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)
• Bayesian Structural Learning for an Improved Diagnosis of Cyber-Physical Systems [0.8379286663107844]
  This paper proposes a scalable algorithm for an automated learning of a structured diagnosis model. It offers equal performance to comparable algorithms while giving better interpretability.
  arXiv  Detail & Related papers  (2021-04-02T11:14:05Z)
• A non-autonomous equation discovery method for time signal classification [1.933681537640272]
  We develop a framework for analyzing time signals based on non-autonomous dynamical equations. We show how gradients can be efficiently computed using the adjoint method. We also demonstrate how the proposed method yields interpretability in the form of phase portraits.
  arXiv  Detail & Related papers  (2020-11-22T20:03:46Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)

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.