Optimizing Predictive Maintenance in Intelligent Manufacturing: An Integrated FNO-DAE-GNN-PPO MDP Framework

• URL: http://arxiv.org/abs/2511.05594v1
• Date: Wed, 05 Nov 2025 13:21:29 GMT
• Title: Optimizing Predictive Maintenance in Intelligent Manufacturing: An Integrated FNO-DAE-GNN-PPO MDP Framework
• Authors: Shiqing Qiu,
• Abstract summary: We propose a novel Markov Decision Process (MDP) framework that integrates advanced soft computing techniques.<n>We show that the framework significantly outperforms multiple deep learning baseline models with up to 13% cost reduction.<n>The framework has considerable industrial potential to effectively reduce downtime and operating expenses through data-driven strategies.
• Score: 1.6921396880325779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of smart manufacturing, predictive maintenance (PdM) plays a pivotal role in improving equipment reliability and reducing operating costs. In this paper, we propose a novel Markov Decision Process (MDP) framework that integrates advanced soft computing techniques - Fourier Neural Operator (FNO), Denoising Autoencoder (DAE), Graph Neural Network (GNN), and Proximal Policy Optimisation (PPO) - to address the multidimensional challenges of predictive maintenance in complex manufacturing systems. Specifically, the proposed framework innovatively combines the powerful frequency-domain representation capability of FNOs to capture high-dimensional temporal patterns; DAEs to achieve robust, noise-resistant latent state embedding from complex non-Gaussian sensor data; and GNNs to accurately represent inter-device dependencies for coordinated system-wide maintenance decisions. Furthermore, by exploiting PPO, the framework ensures stable and efficient optimisation of long-term maintenance strategies to effectively handle uncertainty and non-stationary dynamics. Experimental validation demonstrates that the approach significantly outperforms multiple deep learning baseline models with up to 13% cost reduction, as well as strong convergence and inter-module synergy. The framework has considerable industrial potential to effectively reduce downtime and operating expenses through data-driven strategies.

Related papers

• A Negotiation-Based Multi-Agent Reinforcement Learning Approach for Dynamic Scheduling of Reconfigurable Manufacturing Systems [0.0]
  This study explores the application of multi agent reinforcement learning (MARL) for dynamic scheduling in soft planning of the RMS settings.<n>In the proposed framework, deep Qnetwork (DQN) agents trained in centralized training learn optimal job assignments in real time while adapting to events such as machine breakdowns and reconfiguration delays.
  arXiv  Detail & Related papers  (2025-11-11T00:04:35Z)
• COMponent-Aware Pruning for Accelerated Control Tasks in Latent Space Models [1.6874375111244326]
  The rapid growth of resource-constrained mobile platforms has increased the demand for computationally efficient neural network controllers (NNCs)<n>Deep neural networks (DNNs) demonstrate superior performance in control applications, their substantial computational complexity and memory requirements present significant barriers to practical deployment on edge devices.<n>This paper introduces a comprehensive model compression methodology that leverages component-aware structured pruning to determine the optimal pruning magnitude for each pruning group.
  arXiv  Detail & Related papers  (2025-08-11T16:16:51Z)
• The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks [56.37880529653111]
  The demand for large computation model (LAIM) services is driving a paradigm shift from traditional cloud-based inference to edge-based inference for low-latency, privacy-preserving applications.<n>In this paper, we investigate the LAIM-inference scheme, where a pre-trained LAIM is pruned and partitioned into on-device and on-server sub-models for deployment.
  arXiv  Detail & Related papers  (2025-05-14T08:18:55Z)
• Privacy-Aware Joint DNN Model Deployment and Partitioning Optimization for Collaborative Edge Inference Services [14.408050197587654]
  Edge inference (EI) has emerged as a promising paradigm to address the growing limitations of cloud-based Deep Neural Network (DNN) inference services.<n> deploying DNN models on resource-constrained edge devices introduces additional challenges, including limited/storage resources, dynamic service demands, and heightened privacy risks.<n>This paper presents a novel privacy-aware optimization framework that jointly addresses DNN model deployment, user-server association, and model partitioning.
  arXiv  Detail & Related papers  (2025-02-22T05:27:24Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.<n>By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.<n>GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators [0.8796261172196743]
  This paper presents a comprehensive methodology for exploring and enabling a holistic assessment of the impact of quantization on model accuracy, activation fault reliability, and hardware efficiency. A fully automated framework is introduced that is capable of applying various quantization-aware techniques, fault injection, and hardware implementation. The experiments on established benchmarks demonstrate the analysis flow and the profound implications of quantization on reliability, hardware performance, and network accuracy.
  arXiv  Detail & Related papers  (2024-01-17T12:55:17Z)
• 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)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Neural Stochastic Dual Dynamic Programming [99.80617899593526]
  We introduce a trainable neural model that learns to map problem instances to a piece-wise linear value function. $nu$-SDDP can significantly reduce problem solving cost without sacrificing solution quality.
  arXiv  Detail & Related papers  (2021-12-01T22:55:23Z)

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.