On the basis of the principal components analysis-particle swarm optimization-least squares support vector machine (PCA-PSO-LSSVM) algorithm, a fault diagnosis system is proposed for the compressor system. The relationship between the working principle of a compressor system, the fault phenomenon, and the root cause is analyzed. A fault diagnosis model is established based on the LSSVM optimized using PSO, the compressor fault diagnosis test experimental platform is used to obtain the fault signal of various fault occurrence states, and the PCA algorithm is employed to extract the characteristic data in the fault signal as input to the fault diagnosis model. The back-propagation neural network, the LSSVM algorithm, and the PSO-LSSVM algorithm are analyzed and compared with the proposed fault diagnosis model. Results show that the PCA-PSO-LSSVM fault diagnosis model has a maximum fault recognition efficiency that is 10.4% higher than the other three models, the test sample classification time is reduced by 0.025 s, the PCA algorithm can effectively reduce the input dimension, and the PSO-LSSVM fault diagnosis model based on the PCA algorithm for extracting features has a high recognition rate and accuracy. Therefore, the proposed fault diagnosis system can effectively identify the compressor fault and improve the efficiency of the compressor.

基于主成分分析-粒子群优化-最小二乘支持向量机(PCA-PSO-LSSVM)算法，提出了一种压缩机系统故障诊断系统。分析了压缩机系统的工作原理、故障现象及根本原因之间的关系。基于PSO优化的LSSVM建立故障诊断模型，利用压缩机故障诊断测试实验平台获取各种故障发生状态的故障信号，采用PCA算法提取故障信号中的特征数据为输入到故障诊断模型。对反向传播神经网络、LSSVM算法和PSO-LSSVM算法进行了分析，并与所提出的故障诊断模型进行了比较。结果表明，PCA-PSO-LSSVM故障诊断模型的最大故障识别效率比其他三种模型提高了10.4%，测试样本分类时间减少了0.025 s，PCA算法可以有效降低输入维数，基于PCA算法提取特征的PSO-LSSVM故障诊断模型具有较高的识别率和准确率。因此，所提出的故障诊断系统可以有效地识别压缩机故障并提高压缩机的效率。