# Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Proposed Methodology

#### 3.1. Pattern Recognition Technique

- Step 1: Find the Fourier transform of the processed input signal.
- Step 2: Segment the Fourier spectrum by detecting the local maxima in the spectrum.
- Step 3: Sort the local maxima in decreasing order
- Step 4: Define the boundaries of every segment as the center between two successive maxima.
- Step 5: Follow the construction idea of Meyer’s wavelet to obtain a tight frameset
- Step 6: Obtain the corresponding signal filters (modes as defined in [24]).

#### 3.2. Proposed Deep Convolutional Neural Network

## 4. Experimental Results and Discussion

#### 4.1. Faults in Induction Motors

- Bearing Axis Deviation: The structure of the bearing is precise. If it is disturbed by some external forces, the structure of the bearing may be affected. After connecting the motor to the load, an earthquake, collision, and the assembly process may introduce an offset of midpoints on both ends of the connection, which causes heating problems and unwanted noise. A normal motor with a full load is used, and, for this experiment, the coupling is shifted 0.5 mm upward to imitate the deviation condition. The experimental motor model is shown in Figure 6d.
- Stator and Rotor Friction and Poor Insulation: Because of friction, overheating, insulation aging, dampness and corona, the stator or rotor coil is short-circuited, and hence it will break down if not diagnosed. The insulation of the adjacent turns in the stator coil will be damaged, causing a short circuit, as shown in the Figure 6a. When the motor is started, the short-circuit current value will be high due to the difference in excessive voltage caused by different wound turns in the stator, and the motor will be burnt. The experimental motor model is shown in Figure 6a.
- Rotor Aluminum End Ring Break: The outer ring damage is one of the most common faults. If the starting frequency is very high and/or the motor is overloaded, the rotor bar will break due to the excessive current. For this experiment, a hole with a diameter of 7 mm and a depth of 30 mm is made on the rotor bar to simulate the fault condition. The experimental motor model is shown in Figure 6b.
- Bearing Noise: Damage to the bearing’s outer race is considered one of the constant faults observed in bearings. The structure of the bearing is always kept precise. However, if the structure is disturbed by an external force or some other structures of bearing, this causes messy and numerous harmonics in the measured spectrum. A hole with a diameter and depth of 1 mm is made in the outer race to simulate the fault condition for this experiment. The experimental motor model is shown in Figure 6c.

#### 4.2. Dataset

#### 4.3. CNN Performance Evaluation Results

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 1.**The induction motor current signals: 10 cycles of current signals for each fault and healthy conditions motors (non-distinguishable patterns).

**Figure 2.**The EWT modes of the current signals: The EWT modes plot for the respective fault and healthy conditions (distinguishable patterns).

**Figure 4.**Grayscale images (32 × 32) for the EWT modes of each fault and the healthy conditions of the motor.

**Figure 5.**The proposed 3-stage Convolutional Neural Network Architecture diagram. 32 × 32 size grayscale images are fed into the CNN model. The architecture consists of three convolutions layers followed by pooling layers and two fully connected layers.

**Figure 6.**The experimental motor model: (

**a**) stator and rotor friction and poor insulation; (

**b**) rotor aluminum end ring break; (

**c**) bearing noise; and (

**d**) bearing axis deviation.

**Figure 8.**The confusion matrix for the test dataset. (Fault 0: Healthy, Fault 1: Bearing Axis Deviation, Fault 2: Stator and Rotor Friction, Fault 3: Rotor Aluminum End Ring Break, Fault 4: Bearing Noise, Fault 5: Poor Insulation).

Bearing Axis Deviation | Stator and Rotor Friction | Rotor Aluminum End Ring Break | Bearing Noise | Poor Insulation | Healthy | Total |
---|---|---|---|---|---|---|

150 | 150 | 150 | 150 | 150 | 150 | 900 |

Data Split Ratio | ||
---|---|---|

Training | 70% | 630 |

Validation | 15% | 135 |

Test | 15% | 135 |

Classification Report | ||||
---|---|---|---|---|

CLASS | Precision | Recall | F1-Score | Support |

Healthy | 1.00 | 1.00 | 1.00 | 23 |

Bearing Axis Deviation | 0.97 | 0.96 | 0.94 | 22 |

Stator and Rotor Friction | 0.95 | 0.97 | 0.96 | 22 |

Rotor Aluminum End Ring Break | 1.00 | 1.00 | 1.00 | 23 |

Bearing Noise | 0.93 | 1.00 | 0.96 | 22 |

Poor Insulation | 0.96 | 0.89 | 0.95 | 23 |

Accuracy | 0.97 | 135 | ||

Macro avg | 0.97 | 0.97 | 0.97 | 135 |

Weighted avg | 0.97 | 0.97 | 0.97 | 135 |

Methods | Accuracy (%) |
---|---|

DBN | 92.2 |

SVM | 89.8 |

Sparse filter | 96.4 |

ANN | 81.8 |

ADCNN | 96.2 |

Proposed CNN | 97.37 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Hsueh, Y.-M.; Ittangihal, V.R.; Wu, W.-B.; Chang, H.-C.; Kuo, C.-C.
Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform. *Symmetry* **2019**, *11*, 1212.
https://doi.org/10.3390/sym11101212

**AMA Style**

Hsueh Y-M, Ittangihal VR, Wu W-B, Chang H-C, Kuo C-C.
Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform. *Symmetry*. 2019; 11(10):1212.
https://doi.org/10.3390/sym11101212

**Chicago/Turabian Style**

Hsueh, Yu-Min, Veeresh Ramesh Ittangihal, Wei-Bin Wu, Hong-Chan Chang, and Cheng-Chien Kuo.
2019. "Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform" *Symmetry* 11, no. 10: 1212.
https://doi.org/10.3390/sym11101212