Compressor fault diagnosis system based on PCA-PSO-LSSVM algorithm.

Area of Science:

• Engineering
• Artificial Intelligence
• Machine Learning

Background:

• Compressor systems are critical in various industrial applications.
• Effective fault diagnosis is essential for maintaining operational efficiency and preventing failures.
• Existing fault diagnosis methods may lack accuracy or efficiency.

Purpose of the Study:

• To propose an advanced fault diagnosis system for compressor systems.
• To enhance the accuracy and efficiency of fault recognition.
• To investigate the effectiveness of a novel PCA-PSO-LSSVM algorithm.

Main Methods:

• Developing a fault diagnosis model using Least Squares Support Vector Machine (LSSVM) optimized by Particle Swarm Optimization (PSO).
• Utilizing Principal Component Analysis (PCA) for effective feature extraction from compressor fault signals.
• Establishing a compressor fault diagnosis experimental platform to collect fault data.
• Comparing the proposed PCA-PSO-LSSVM model with Back-Propagation Neural Network, LSSVM, and PSO-LSSVM algorithms.

Main Results:

• The PCA-PSO-LSSVM model achieved a maximum fault recognition efficiency 10.4% higher than other models.
• Test sample classification time was reduced by 0.025 seconds.
• PCA effectively reduced input dimensions, enhancing model performance.
• The proposed model demonstrated high recognition rates and accuracy.

Conclusions:

• The PCA-PSO-LSSVM fault diagnosis system effectively identifies compressor faults.
• The system significantly improves the efficiency and accuracy of fault diagnosis.
• This approach offers a robust solution for real-time compressor monitoring and maintenance.