Research on PCL Remote Monitoring and Fault Diagnosis System Integrated with Information Technology

Remote monitoring and fault diagnosis of industrial equipment are of great significance for ensuring operational stability and improving production efficiency. However, traditional methods often suffer from insufficient accuracy and real-time performance when dealing with complex and multi-dimensional signals. This study proposes a PCL remote monitoring and fault diagnosis system based on information technology, targeting various operating conditions in industrial environments. The system collects real-time signals such as vibration, temperature, and pressure from industrial sensors and employs an algorithm framework that integrates Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) to achieve efficient fault pattern recognition. The research indicates that the system exhibits excellent classification performance under different operating conditions. Experimental data cover four typical operating conditions: normal state, bearing wear, imbalance, and looseness, with classification accuracies reaching 94.8% (normal), 91.3% (bearing wear), 89.6% (imbalance), and 95.1% (looseness), respectively. Additionally, the model converges rapidly within 30 training rounds, with accuracy stabilizing at 95% and an overall F1 score of 91.3%. In practical applications, the system maintains a prediction confidence of 89.7% to 96.5% for typical faults, demonstrating its stability and applicability under various complex operating conditions. In summary, this study optimizes fault diagnosis methods through integrated algorithms, providing a reliable solution for intelligent monitoring and maintenance of industrial equipment and laying the foundation for further optimization and promotion.