Equipment Operation Status Evaluation System Based on Image Recognition Technology

Abstract

In line with the thematic focus of Frontiers in Computer Science on the integration of cutting-edge computational technologies, this research addresses the critical need for reliable and scalable equipment operation status evaluation systems. Traditional approaches to fault detection, 6 primarily based on expert-driven rules or simple signal processing, are challenged by high-dimensional data, operational variability, and limited labeled examples. These methods often fail in dynamic industrial environments where subtle temporal anomalies signal impending faults. To bridge this gap, we propose a robust evaluation system leveraging advanced image recognition and deep learning techniques. Our approach introduces a novel neural network architecture combining convolutional and bidirectional LSTM networks for hierarchical feature extraction and temporal pattern recognition. Additionally, a self-attention mechanism enhances interpretability by highlighting fault-indicative time steps. The system incorporates both supervised and unsupervised strategies, including reconstruction-based anomaly detection, ensuring adaptability across diverse operational conditions. Experimental validation demonstrates superior performance in detecting faults under noisy data and unseen scenarios, setting a new benchmark for intelligent maintenance systems. By unifying domain-specific preprocessing with innovative learning strategies, our system offers a scalable solution for next-generation equipment management.