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A novel sparse filtering approach based on time-frequency feature extraction and softmax regression for intelligent fault diagnosis under different speeds

一种应用于不同转速下智能故障诊断的基于时频特征提取和 softmax 回归的稀疏滤波新方法

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Abstract

Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis. However, the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation. To overcome this deficiency, a novel intelligent defect detection framework based on time-frequency transformation is presented in this work. In the framework, the samples under one speed are employed for training sparse filtering model, and the remaining samples under different speeds are adopted for testing the effectiveness. Our proposed approach contains two stages: 1) the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm, and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm; 2) different defect types are classified by the softmax regression using the defect features. The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment. The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds, but also obtains higher identification accuracy than the other methods.

摘要

现代农业机械化对农机使用过程中的故障诊断提出了更高的要求。 然而, 故障特征通常是在所 有转速下进行学习和分类的, 而没有考虑转速波动的影响. 为了克服这一缺陷, 本文提出了一种基于 时频变换的智能故障诊断新框架. 在该框架中, 一种转速下的样本用来训练稀疏滤波, 然后其他转速 下的样本用来测试稀疏滤波的性能. 本文提出的方法包括两个阶段:1)对机械原始振动数据进行短时 傅里叶变换(STFT), 得到时频域信号, 然后利用稀疏滤波模型从时频信号中提取故障特征. 2)基于学 习到的故障特征, 利用softmax 回归对不同的机械健康状况进行分类. 提出方法可以用来自适应的提 取故障特征, 是一种可对农业机械进行有效故障诊断的智能方法. 故障诊断结果表明, 该方法不仅在 不同转速下的故障诊断中下具有较强优势, 而且比其他方法具有更高的分类准确率.

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Authors and Affiliations

  1. State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Zhong-wei Zhang  (张忠伟) & Huai-hai Chen  (陈怀海)

  2. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Shun-ming Li  (李舜酩) & Jin-rui Wang  (王金瑞)

Authors
  1. Zhong-wei Zhang  (张忠伟)
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  2. Huai-hai Chen  (陈怀海)
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  3. Shun-ming Li  (李舜酩)
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  4. Jin-rui Wang  (王金瑞)
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Corresponding author

Correspondence to Huai-hai Chen  (陈怀海).

Additional information

Foundation item: Project(51675262) supported by the National Natural Science Foundation of China; Project(2016YFD0700800) supported by the National Key Research and Development Program of China; Project(6140210020102) supported by the Advance Research Field Fund Project of China; Project (NP2018304) supported by the Fundamental Research Funds for the Central Universities, China; Project(2017-IV-0008-0045) supported by the National Science and Technology Major Project

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Zhang, Zw., Chen, Hh., Li, Sm. et al. A novel sparse filtering approach based on time-frequency feature extraction and softmax regression for intelligent fault diagnosis under different speeds. J. Cent. South Univ. 26, 1607–1618 (2019). https://doi.org/10.1007/s11771-019-4116-5

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  • DOI: https://doi.org/10.1007/s11771-019-4116-5

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