An optimized fuzzy logic model for proactive maintenance

• URL: http://arxiv.org/abs/2212.12757v1
• Date: Sat, 24 Dec 2022 15:49:46 GMT
• Title: An optimized fuzzy logic model for proactive maintenance
• Authors: Abdelouadoud Kerarmi, Assia Kamal-idrissi, Amal El Fallah Seghrouchni
• Abstract summary: The ITTFLM can generate outputs in 5ms, the results demonstrate that this model based on the Trapezoidal membership functions identifies the failure states with high accuracy. The ITTFLM was tested on fan data collected in real-time from a plant machine.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Fuzzy logic has been proposed in previous studies for machine diagnosis, to overcome different drawbacks of the traditional diagnostic approaches used. Among these approaches Failure Mode and Effect Critical Analysis method(FMECA) attempts to identify potential modes and treat failures before they occur based on subjective expert judgments. Although several versions of fuzzy logic are used to improve FMECA or to replace it, since it is an extremely cost-intensive approach in terms of failure modes because it evaluates each one of them separately, these propositions have not explicitly focused on the combinatorial complexity nor justified the choice of membership functions in Fuzzy logic modeling. Within this context, we develop an optimization-based approach referred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) that smartly generates fuzzy logic rules using Truth Tables. The ITTFLM was tested on fan data collected in real-time from a plant machine. In the experiment, three types of membership functions (Triangular, Trapezoidal, and Gaussian) were used. The ITTFLM can generate outputs in 5ms, the results demonstrate that this model based on the Trapezoidal membership functions identifies the failure states with high accuracy, and its capability of dealing with large numbers of rules and thus meets the real-time constraints that usually impact user experience.

Related papers

• Log anomaly detection via Meta Learning and Prototypical Networks for Cross domain generalization [0.0]
  We propose a new end-to-end framework based on a meta-learning approach to detect log anomalies.<n>Our methodology first gets the data ready by combining a Drain3 log parsing mechanism with a dynamic drift-based labeling technique.<n>Model Agnostic Meta-Learning (MAML) and Prototypical Networks models are trained to adapt quickly and effectively.
  arXiv  Detail & Related papers  (2026-01-20T12:09:33Z)
• An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models [59.13182819190547]
  Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields.<n>They face challenges such as complex design specifications and scalability issues with large datasets.<n>This paper proposes an Integrated Fusion Framework that merges the strengths of both paradigms to enhance model performance and interpretability.
  arXiv  Detail & Related papers  (2025-11-11T10:28:23Z)
• ORGEval: Graph-Theoretic Evaluation of LLMs in Optimization Modeling [18.8099769877788]
  ORGEval is a graph-theoretic evaluation framework for assessing Large Language Models' capabilities in formulating linear and mixed-integer linear programs.<n>We show that ORGEval can successfully detect model equivalence and produce 100% consistent results across random parameter configurations.<n>Our results reveal that although optimization modeling remains challenging for all LLMs, DeepSeek-V3 and Claude-Opus-4 achieve the highest accuracies under direct prompting.
  arXiv  Detail & Related papers  (2025-10-31T16:35:52Z)
• Making Mathematical Reasoning Adaptive [61.45161826629692]
  We propose the AdaR framework to enable adaptive reasoning in large language models (LLMs)<n>AdaR synthesizes logically equivalent queries by varying variable values, and trains models with RLVR on these data to penalize spurious logic.<n> Experimental results demonstrate that AdaR improves robustness and generalization, achieving substantial improvement in mathematical reasoning.
  arXiv  Detail & Related papers  (2025-10-06T09:30:05Z)
• Influence-Guided Concolic Testing of Transformer Robustness [2.78712872004245]
  Concolic testing for deep neural networks alternates concrete execution with constraint solving to search for inputs that flip decisions.<n>We present an influence-guided concolic tester for Transformer classifiers that ranks path predicates by SHAP-based estimates of their impact on the model output.
  arXiv  Detail & Related papers  (2025-09-28T11:09:15Z)
• RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learning [27.235259453535537]
  RationAnomaly is a novel framework that enhances log anomaly detection by synergizing Chain-of-Thought fine-tuning with reinforcement learning.<n>We have released the corresponding resources, including code and datasets.
  arXiv  Detail & Related papers  (2025-09-18T07:35:58Z)
• User-Based Sequential Modeling with Transformer Encoders for Insider Threat Detection [0.005755004576310333]
  Insider threat detection presents unique challenges due to the authorized status of malicious actors.<n>Existing machine learning methods treat user activity as isolated events, thereby failing to leverage sequential dependencies in user behavior.<n>We propose a User-Based Sequencing (UBS) methodology, transforming the CERT insider threat dataset into structured temporal sequences suitable for deep sequential modeling.
  arXiv  Detail & Related papers  (2025-06-30T00:47:31Z)
• Solving Inverse Problems with FLAIR [59.02385492199431]
  Flow-based latent generative models are able to generate images with remarkable quality, even enabling text-to-image generation.<n>We present FLAIR, a novel training free variational framework that leverages flow-based generative models as a prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments [5.5855749614100825]
  This paper addresses the hypothesis that leveraging multiple pre-trained models can mitigate this recall reduction.<n>We formulate the challenge of identifying and managing conflicting predictions from various models as a consistency-based abduction problem.<n>Our results validate the use of consistency-based abduction as an effective mechanism to robustly integrate knowledge from multiple imperfect models in challenging, novel scenarios.
  arXiv  Detail & Related papers  (2025-05-25T23:17:47Z)
• ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model [2.55347686868565]
  ADALog is an adaptive, unsupervised anomaly detection framework.<n>It operates on individual unstructured logs, extracts intra-log contextual relationships, and performs adaptive thresholding on normal data.<n>We evaluate ADALog on benchmark datasets BGL, Thunderbird, and Spirit.
  arXiv  Detail & Related papers  (2025-05-15T17:31:40Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• Reliability analysis for non-deterministic limit-states using stochastic emulators [0.0]
  This paper introduces reliability analysis for models and addresses it by using suitable surrogate models to lower its typically high computational cost. Specifically, we focus on the recently introduced generalized models and chaos expansions. We validate our methodology through three case studies. First, using an analytical function with a closed-form solution, we demonstrate that the emulators converge to the correct solution.
  arXiv  Detail & Related papers  (2024-12-18T11:08:56Z)
• Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model [66.91323540178739]
  Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior. We revisit SR from a novel information-theoretic perspective and find that sequential modeling methods fail to adequately capture randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests.
  arXiv  Detail & Related papers  (2024-10-31T14:52:01Z)
• Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up [9.42385235462794]
  Large language models (LLMs) have shown remarkable performance in reasoning tasks but face limitations in mathematical and complex logical reasoning. We propose Reversal of Thought (RoT) to enhance the logical reasoning abilities of LLMs during the warm-up phase prior to batch inference. RoT utilizes a Preference-Guided Reverse Reasoning warm-up strategy, which integrates logical symbols for pseudocode planning.
  arXiv  Detail & Related papers  (2024-10-16T07:44:28Z)
• DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation [18.77296551727931]
  We propose DECIDER, a novel approach that leverages priors from large language models (LLMs) and vision-language models (VLMs) to detect failures in image models. DECIDER consistently achieves state-of-the-art failure detection performance, significantly outperforming baselines in terms of the overall Matthews correlation coefficient.
  arXiv  Detail & Related papers  (2024-08-01T07:08:11Z)
• LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection [12.596635603629725]
  We develop a unified model to detect anomalies from objects belonging to multiple classes when only normal data is accessible. We first explore the generative-based approach and investigate latent diffusion models for reconstruction. We introduce a feature editing strategy that modifies the input feature space of the diffusion model to further alleviate identity shortcuts''
  arXiv  Detail & Related papers  (2023-07-16T14:41:22Z)
• Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize [57.22851616806617]
  We show that our method achieves state-of-the-art results in four domains from the literature. Our approach outperforms the best existing method by nearly 200% when the localness assumption is broken.
  arXiv  Detail & Related papers  (2023-05-26T11:17:45Z)
• Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery [1.6114012813668934]
  The Work Disability Functional Assessment Battery (WD-FAB) is a multidimensional item response theory (IRT) instrument for assessing work-related mental and physical function. We develop a Bayesian hierarchical model for self-consistently performing the following simultaneous tasks. We compare the resulting item discriminations obtained using the traditional posthoc method.
  arXiv  Detail & Related papers  (2022-10-20T01:55:59Z)
• Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL [154.13105285663656]
  A cooperative Multi-A gent R einforcement Learning (MARL) with permutation invariant agents framework has achieved tremendous empirical successes in real-world applications. Unfortunately, the theoretical understanding of this MARL problem is lacking due to the curse of many agents and the limited exploration of the relational reasoning in existing works. We prove that the suboptimality gaps of the model-free and model-based algorithms are independent of and logarithmic in the number of agents respectively, which mitigates the curse of many agents.
  arXiv  Detail & Related papers  (2022-09-20T16:42:59Z)
• Linear Temporal Logic Modulo Theories over Finite Traces (Extended Version) [72.38188258853155]
  This paper studies Linear Temporal Logic over Finite Traces (LTLf) proposition letters are replaced with first-order formulas interpreted over arbitrary theories. The resulting logic, called Satisfiability Modulo Theories (LTLfMT), is semi-decidable.
  arXiv  Detail & Related papers  (2022-04-28T17:57:33Z)
• RoMA: Robust Model Adaptation for Offline Model-based Optimization [115.02677045518692]
  We consider the problem of searching an input maximizing a black-box objective function given a static dataset of input-output queries. A popular approach to solving this problem is maintaining a proxy model that approximates the true objective function. Here, the main challenge is how to avoid adversarially optimized inputs during the search.
  arXiv  Detail & Related papers  (2021-10-27T05:37:12Z)
• Squashing activation functions in benchmark tests: towards eXplainable Artificial Intelligence using continuous-valued logic [0.0]
  This work demonstrates the first benchmark tests that measure the performance of Squashing functions in neural networks. Three experiments were carried out to examine their usability and a comparison with the most popular activation functions was made for five different network types. Results indicate that due to the embedded nilpotent logical operators and the differentiability of the Squashing function, it is possible to solve classification problems.
  arXiv  Detail & Related papers  (2020-10-17T10:42:40Z)
• Tractable Inference in Credal Sentential Decision Diagrams [116.6516175350871]
  Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. We develop the credal sentential decision diagrams, a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with credal sets of mass functions. For a first empirical validation, we consider a simple application based on noisy seven-segment display images.
  arXiv  Detail & Related papers  (2020-08-19T16:04:34Z)

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.