Intelligent Monitoring of Industrial Equipment: A Study on Fault Prediction Based on Deep Learning

Introduction

With the continuous development of industrial equipment, especially in manufacturing, the complexity and diversity of equipment have been steadily increasing, leading to a gradual rise in the risk of equipment failures. Equipment failures not only affect production efficiency and product quality but can also result in significant economic losses (Namuduri et al., 2020). Therefore, improving equipment reliability and reducing downtime have become key challenges in industrial production (Li, Wang, & Wang, 2019). Traditional equipment fault detection methods typically rely on rules and manual experience, but as the variety of equipment and the volume of data increase these methods are gradually showing their limitations (Carvalho et al., 2019; Panchi et al., 2022). However, despite significant successes in various fields, the application of deep learning in industrial equipment monitoring still faces many challenges. Traditional models, in particular, often exhibit instability in performance and poor generalization ability when dealing with complex sequential data, which limits their applicability in practical industrial scenarios.

Among existing deep learning models, Recurrent Neural Networks (RNNs) are a classic sequence modeling method. By introducing a recurrent structure, RNNs can capture dependencies in sequential data more effectively (Q. Wang et al., 2020). RNNs have been widely used in industrial equipment monitoring, but they face the problem of vanishing gradients when processing long sequences, leading to the loss of long-term dependencies and impairing the model’s performance (Zhu et al., 2022). Long Short Term Memory Networks (LSTMs) introduce a gating mechanism that successfully alleviates the vanishing gradient problem, making them more stable when handling long-term sequential dependencies (Jiang et al., 2022). However, LSTMs still face challenges, such as high computational complexity and long training times, especially when dealing with complex equipment states or large-scale sequential data, which limits their efficiency and applicability (Lai et al., 2019). Similar to LSTMs, temporal convolutional networks (TCNs) have made significant progress in processing long sequences in recent years (Li, Li, et al., 2019). TCNs extract information in the spatiotemporal domain through convolution operations, effectively overcoming the bottleneck in traditional RNNs and LSTMs when processing long sequences. With a larger receptive field, TCNs can better capture global patterns (Yueze et al., 2023). However, TCNs still face challenges in industrial equipment monitoring, such as sensitivity to sequence length and high computational complexity, which persist as efficiency issues in real-time monitoring and large-scale data processing (Yuan et al., 2021). Another common deep learning model is a graph convolutional network, which focuses on processing graph-structured data and is particularly useful in capturing complex relationships between equipment in industrial monitoring (Luo et al., 2022). By modeling the topological structure between equipment, graph convolutional networks help understand the evolution and mutual influence of equipment states (Y. Wang et al., 2020). However, graph convolutional networks still face problems in handling large-scale graph data, such as low computational efficiency and poor real-time performance, limiting their practical application in dynamic environments.