Fault prediction and maintenance strategy for non-standard automated equipmentbased on deep learning
基于深度学习的非标自动化设备故障预测与维护策略

摘要

非标自动化设备具有高度的定制化和灵活性，能够满足各种复杂、多变的制造需求。然而，由于其设计复杂、部件多样，故障预测与维护成为一大挑战。本文探讨了基于深度学习的非标自动化设备故障预测与维护策略。首先，介绍了非标自动化设备的特点及其维护现状，阐述了深度学习在故障预测中的优势。其次，详细分析了基于深度学习的故障预测模型，包括卷积神经网络（CNN）、循环神经网络（RNN）及其变体长短时记忆网络（LSTM）和门控循环单元（GRU）等，并讨论了它们在故障特征提取、时序数据处理等方面的应用。接着，提出了一种结合深度学习与强化学习的维护策略，通过优化维护决策，提高设备可靠性和降低维护成本。最后，通过仿真实验验证了所提策略的有效性，并对其在实际应用中的前景进行了展望。

Abstract

Non-standard automated equipment features high customization and flexibility, capable of meeting various complex and ever-changing manufacturing demands. However, due to its complex design and diverse components, fault prediction and maintenance pose a significant challenge. This paper explores fault prediction and maintenance strategies for non-standard automated equipment based on deep learning. Firstly, it introduces the characteristics of non-standard automated equipment and the current status of its maintenance, elucidating the advantages of deep learning in fault prediction. Secondly, it provides a detailed analysis of fault prediction models based on deep learning, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their variants such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), discussing their applications in fault feature extraction and time-series data processing. Subsequently, a maintenance strategy combining deep learning and reinforcement learning is proposed, aiming to improve equipment reliability and reduce maintenance costs by optimizing maintenance decisions. Finally, simulation experiments verify the effectiveness of the proposed strategy, and its prospects for practical application are discussed.