# Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder

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## Abstract

**:**

## 1. Introduction

## 2. Principle

#### 2.1. Autoencoder Network Principle

#### 2.2. Stacked Denoising Autoencoder Structure

#### 2.3. SVM Classifier

## 3. Method

#### 3.1. SDAE Diagnostic Process

- The vibration and speed signals collected from the PMSM fault experiment are divided into training samples and test samples, and the data samples of known fault types are packaged to establish the motor fault signal database;
- The vibration data are normalized and preprocessed according to the (0,1) standardized formula, and the dimensional vibration signal is transformed into the dimensionless signal expression through Equation (18) so as to improve the sample training speed;

- 3.
- The training samples are randomly set to 0, or Gaussian noise is added to realize the “damage noise” addition to simulate the fault data collected in the actual test and determine the network structure, such as the number of the SDAE input layer nodes, the number of hidden layer nodes and the number of nodes in each layer;
- 4.
- The single hidden layer feedforward neural network is used as the basic model to construct multiple autoencoders, and the pseudo-inverse learning algorithm is used to train each autoencoder separately to obtain the connection weight and offset of the i-layer autoencoder. The hidden layer output of the former autoencoder is used as the input of the latter autoencoder, and the above steps are repeated to train the new autoencoder step-by-step;
- 5.
- Fine-tune the parameters of the SDAE network according to the known types of faults, complete the sample feature extraction, and use the SDAE output data as the input of the support vector machine for training, diagnosis and classification.

#### 3.2. SVM Classification

## 4. Results

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Failure Mode | Fault Size/mm | Number of Samples |
---|---|---|

normal | 0 | 400 |

Inner circle | 0.1778 | 400 |

Inner circle | 0.3556 | 400 |

Inner circle | 0.5334 | 400 |

Outer ring | 0.1778 | 400 |

Outer ring | 0.3556 | 400 |

Outer ring | 0.5334 | 400 |

Rolling element | 0.1778 | 400 |

Rolling element | 0.3556 | 400 |

Rolling element | 0.5334 | 400 |

A Phase Current | B Phase Current | C Phase Current | Negative Sequence Current | Electromagnetic Torque |
---|---|---|---|---|

1.0026 | 1.0086 | 0.9946 | 0.036 | 3.67 |

1.1210 | 1.0023 | 0.9934 | 0.064 | 3.77 |

1.1230 | 1.0020 | 0.9867 | 0.070 | 3.79 |

1.1339 | 1.0018 | 0.9812 | 0.079 | 3.81 |

1.1472 | 1.0007 | 0.9745 | 0.082 | 3.88 |

1.2486 | 0.9898 | 0.9658 | 0.182 | 3.92 |

1.5684 | 0.9750 | 0.9562 | 0.486 | 4.12 |

1.6982 | 0.9698 | 0.9236 | 0.669 | 4.18 |

1.8675 | 0.9672 | 0.8645 | 0.948 | 4.26 |

2.0784 | 0.9542 | 0.7996 | 1.072 | 4.40 |

Classification Algorithm | Test Accuracy/% | Standard Deviation of Accuracy |
---|---|---|

RBF | 86.6 | 2.21 |

SVM | 89.1 | 0.89 |

DAE | 90.6 | 1.49 |

SDAE + SVM | 94.2 | 0.88 |

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**MDPI and ACS Style**

Xu, X.; Feng, J.; Zhan, L.; Li, Z.; Qian, F.; Yan, Y.
Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder. *Entropy* **2021**, *23*, 339.
https://doi.org/10.3390/e23030339

**AMA Style**

Xu X, Feng J, Zhan L, Li Z, Qian F, Yan Y.
Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder. *Entropy*. 2021; 23(3):339.
https://doi.org/10.3390/e23030339

**Chicago/Turabian Style**

Xu, Xiaowei, Jingyi Feng, Liu Zhan, Zhixiong Li, Feng Qian, and Yunbing Yan.
2021. "Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder" *Entropy* 23, no. 3: 339.
https://doi.org/10.3390/e23030339