Rotating Machine Fault Diagnosis Based on KPCA and Optimized BPNN- KNN Model

Dong Shao-Jiang; Sheng Jin-Lu; He Chang-Wei

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Rotating Machine Fault Diagnosis Based on KPCA and Optimized BPNN- KNN Model

机译：基于KPCA和优化BPNN- KNN模型的旋转机械故障诊断。

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摘要

In order to effectively recognize the rotating machine fault, a new method is proposed. Firstly, the gathered vibration signals are decomposed by the empirical mode decomposition (EMD), the corresponding intrinsic mode functions (IMF) are got. Then, Shannon entropy of the IMFs is used as the original features. But the extracted features have the problems of high dimension and redundancy serious. So, the KPCA is introduced to extract the characteristic features. The characteristic features are inputted to the BPNN-KNN model to train and construct the fault diagnosis model, the rotating machine fault condition identification is realized. The running states of a normal inner race and several inner races with different degree of fault were recognized, the results validate the effectiveness of the proposed algorithm.

机译：为了有效识别旋转机械故障，提出了一种新的方法。首先，通过经验模态分解（EMD）对采集到的振动信号进行分解，得到相应的固有模态函数（IMF）。然后，将IMF的Shannon熵用作原始特征。但是所提取的特征具有高维和冗余的问题。因此，引入了KPCA来提取特征。将特征特征输入到BPNN-KNN模型中，以训练和构建故障诊断模型，实现旋转机械故障状态识别。识别出正常内圈和若干故障程度不同的内圈的运行状态，结果验证了所提算法的有效性。

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《Journal of the Chinese Society of Mechanical Engineers, Series C: Transactions of the Chinese Society of Mechanical Engineers》 |2015年第4期|共7页
作者
Dong Shao-Jiang; Sheng Jin-Lu; He Chang-Wei;
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正文语种 eng
中图分类机械、仪表工业;
关键词
Empirical mode decomposition; kernel based principle component analysis; Back propagation neural network - k-nearest neighbor mode; Rotating machine;

机译：经验模式分解;基于核的主成分分析;反向传播神经网络-k近邻模式;旋转机;

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1. Rotating Machine Fault Diagnosis Based on KPCA and Optimized BPNN- KNN Model [J] . Dong Shao-Jiang, Sheng Jin-Lu, He Chang-Wei Journal of the Chinese Society of Mechanical Engineers, Series C: Transactions of the Chinese Society of Mechanical Engineers . 2015,第4期

机译：基于KPCA和优化BPNN- KNN模型的旋转机械故障诊断。
2. Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines [J] . Natalia Espinoza Sepulveda, Jyoti Sinha Machines . 2020,第4期

机译：基于振动的机器学习模型中的参数优化，用于旋转机器准确可靠的故障诊断
3. Rotating Machine Fault Diagnosis Based on Locality Preserving Projection and Back Propagation Neural Network-Support Vector Machine Model [J] . Dong Shaojiang, Xu Xiangyang, Liu Juan, Measurement and Control: Journal of the Institute of Measurement and Control . 2015,第7期

机译：基于保局部投影和BP神经网络的旋转机械故障诊断-支持向量机模型
4. Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines [C] . Natalia Espinoza Sepulveda, Jyoti Sinha International Conference on Vibration Engineering and Technology of Machinery . 2018

机译：基于时域和频域特征的机器学习模型对旋转机器故障诊断的比较
5. Fault detection and diagnosis in machining processes and rotating machinery using fuzzy approach and hidden Markov model [D] . Boutros, Tony 2006

机译：基于模糊方法和隐马尔可夫模型的机械加工和旋转机械故障检测与诊断
6. Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine [O] . Yinsheng Chen, Tinghao Zhang, Wenjie Zhao, 2019

机译：基于改进的多尺度幅度感知置换熵和多类关联向量机的旋转机械故障诊断
7. Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines [O] . Natalia Espinoza Sepulveda, Jyoti Sinha 2018

机译：基于时域和频域特征的机器学习模型对旋转机器故障诊断的比较

Rotating Machine Fault Diagnosis Based on KPCA and Optimized BPNN- KNN Model

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