摘要
随着工业4.0的推进和智能制造技术的发展，故障诊断与预测成为提升生产效率、降低维护成本的关键环节，而人工智能技术为这一领域提供了新的解决方案本研究旨在探索基于人工智能的智能制造系统故障诊断与预测方法，通过融合深度学习、机器学习及大数据分析技术，构建了一种高效、精准的故障诊断与预测框架该框架首先利用传感器数据采集与预处理技术获取高质量的运行状态数据，随后采用卷积神经网络与长短时记忆网络相结合的混合模型对数据进行特征提取与模式识别，并通过强化学习优化预测算法的性能实验结果表明，所提出的方法在多种复杂工况下均表现出优异的诊断准确率和预测精度，相较于传统方法提升了约20%此外，本研究还设计了一种自适应阈值调整机制，能够根据系统运行状态动态调整诊断策略，从而显著提高了系统的鲁棒性和适用性总体而言，本研究不仅为智能制造系统的故障诊断与预测提供了创新的技术路径，还为人工智能技术在工业领域的深入应用奠定了理论与实践基础
关键词：智能制造；故障诊断与预测；人工智能；深度学习；自适应阈值调整机制
Abstract
With the advancement of Industry 4.0 and the development of intelligent manufacturing technologies, fault diagnosis and prediction have become critical components for enhancing production efficiency and reducing maintenance costs, while artificial intelligence (AI) technologies offer novel solutions to this domain. This study aims to explore AI-based fault diagnosis and prediction methods for intelligent manufacturing systems by integrating deep learning, machine learning, and big data analytics to construct an efficient and accurate fault diagnosis and prediction fr amework. The fr amework first acquires high-quality operational status data through sensor data collection and preprocessing techniques, followed by feature extraction and pattern recognition using a hybrid model combining convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). Furthermore, reinforcement learning is employed to optimize the performance of the prediction algorithms. Experimental results demonstrate that the proposed method achieves superior diagnostic accuracy and prediction precision under various complex operating conditions, with an improvement of approximately 20% compared to traditional methods. Additionally, this study designs an adaptive threshold adjustment mechanism capable of dynamically modifying diagnostic strategies based on system operational states, thereby significantly enhancing the robustness and adaptability of the system. Overall, this research not only provides innovative technical approaches for fault diagnosis and prediction in intelligent manufacturing systems but also lays theoretical and practical foundations for the deeper application of AI technologies in industrial domains.
Keywords：Intelligent Manufacturing; Fault Diagnosis And Prediction; Artificial Intelligence; Deep Learning; Adaptive Threshold Adjustment Mechanism
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能制造系统故障诊断的研究背景 1
(二) 人工智能在故障诊断中的意义与价值 1
(三) 国内外研究现状与发展趋势 2
二、故障诊断数据的采集与预处理 2
(一) 数据采集技术在智能制造中的应用 2
(二) 数据清洗与特征提取方法研究 3
(三) 基于人工智能的数据预处理优化策略 3
三、基于人工智能的故障诊断模型构建 4
(一) 机器学习算法在故障诊断中的应用 4
(二) 深度学习模型的设计与实现 4
(三) 模型性能评估与优化策略 5
四、故障预测与系统可靠性分析 6
(一) 故障预测的核心技术与方法 6
(二) 基于时间序列的预测模型研究 6
(三) 系统可靠性评估与改进措施 7
结 论 8
参考文献 9
