Journal of Engineering Research

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.

Non-standard automated equipment; Deep learning; Fault prediction; Maintenance strategy; Convolutional Neural Networks (CNNs)