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首页> 外文期刊>Shock and vibration >A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis
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A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis

机译：基于振动信号分析，使用VWC和MSFLA-SVM旋转机械故障诊断模型

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Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA-SVM), and SFLA-SVM. The experiment results show that MSFLA-SVM achieves a much higher fault classification rate than BPNN, ACROA-SVM, and SFLA-SVM.
机译：旋转机械故障诊断主要包括故障特征提取和故障分类。来自机械运行的振动信号通常可以帮助诊断设备的操作状态。不同类型的故障通常具有不同的振动特征，实际上是故障诊断的基础。本文提出了一种新的故障诊断模型，其通过在特征提取阶段结合振动严重性，二元小波能量时间谱和系数功率水平（VWC）的系数功率范围来提取特征。在故障分类阶段，我们设计了基于修改的混合青蛙跨越算法（MSFLA）的支持向量机（SVM），以获得准确的分类机械故障方法。具体地，我们使用MSFLA方法优化SVM参数。 MSFLA可以避免被困成局部最佳，加速收敛，提高分类准确性。最后，我们评估了我们在真正的旋转机械平台上的模型，它具有四种不同的状态，即正常状态，偏心轴断层（EAF），轴承基座故障（BPF）和密封环磨损故障（SRWF）。如结果所示，VWC方法在提取旋转机械的振动信号特征方面是有效的。基于提取的特征，我们进一步将我们的分类方法与其他三个故障分类方法进行比较，即反向衰减神经网络（BPNN），人工化学反应优化算法（ACROA-SVM）和SFLA-SVM。实验结果表明，MSFLA-SVM比BPNN，acroa-SVM和SFLA-SVM实现更高的故障分类率。

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