# A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data

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## Abstract

**:**

## 1. Introduction

- (1)
- Acoustic and three directions vibration signals are simultaneously sampled to be regarded as the input of the model to reinforce the diagnosis knowledge of bearings.
- (2)
- MMD is introduced to minimize the distribution difference between source and target domains, thus improving the transferability of learned features. Combining the advantages of the ResNet framework, it can guarantee high recognition accuracy from one defect degree to another defect category.

## 2. Materials and Methods

#### 2.1. MMD Definition

#### 2.2. ResNet

## 3. The Proposed Model of DA-ResNet

## 4. Experimental Verification

#### 4.1. Datasets Introduction

#### 4.2. Experimental Configuration

- (1)
- Baseline: MLP

- (2)
- BiLSTM

- (3)
- CNN

- (4)
- ResNet

#### 4.3. Cross-Domain Fault Diagnosis

#### 4.4. Transfer Diagnosis of Multi-Source Signal

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Structure of the proposed method. (

**a**) multi-source data; (

**b**) cross-domain fault diagnosis; (

**c**) structure of ResNet; (

**d**) structure of Res-block.

**Figure 5.**Waveform of vibration and acoustic signals. (

**a**) inner race fault; (

**b**) normal; (

**c**) outer race fault; (

**d**) roller fault.

**Figure 8.**Feature representation of the methods in task A→B. (

**a**) MLP; (

**b**) BiLSTM; (

**c**) CNN; (

**d**) ResNet; (

**e**) DA-CNN; (

**f**) DA-ResNet.

**Figure 9.**Confusion matrix demonstrating classification performance of methods in the task A→B. (

**a**) MLP; (

**b**) BiLSTM; (

**c**) CNN; (

**d**) ResNet; (

**e**) DA-CNN; (

**f**) DA-ResNet.

Bearing Type | Ball Number Z | Pitch Diameter D | Ball Diameter d | Contact Angle θ | Inner Race Diameter | Outer Race Diameter |
---|---|---|---|---|---|---|

NU205E | 13 | 38.9 mm | 7.5 mm | 0^{o} | 25 mm | 52 mm |

Dataset | Fault Types | Defect Width (mm) | Labels | Dataset | Fault Types | Defect Width (mm) | Labels |
---|---|---|---|---|---|---|---|

A | N | 0 | 0 | C | N | 0 | 0 |

IR-I | 0.43 | 1 | IR-III | 1.56 | 7 | ||

OR-I | 0.42 | 2 | OR-III | 1.55 | 8 | ||

RO-I | 0.49 | 3 | RO-III | 1.73 | 9 | ||

B | N | 0 | 0 | D | N | 0 | 0 |

IR-II | 1.01 | 4 | IR-IV | 2.03 | 10 | ||

OR-II | 0.86 | 5 | OR-IV | 1.97 | 11 | ||

RO-II | 1.16 | 6 | OR-IV | 2.12 | 12 |

Layer (Type) | Output Shape | Param # |
---|---|---|

inputs1 (Input layer) | (4096, 1) | 0 |

c0 (Conv1D) | (1024, 4) | 20 |

c11 (Conv1D) | (512, 8) | 104 |

c12 (Conv1D) | (512, 8) | 200 |

add_1_2 (Add) | (512, 8) | 0 |

x1p (Max-pooling1D) | (256, 8) | 0 |

c21 (Conv1D) | (256, 16) | 400 |

c22 (Conv1D) | (256, 16) | 784 |

add_2_2 (Add) | (256, 16) | 0 |

x2p (Max-pooling1D) | (128, 16) | 0 |

c31 (Conv1D) | (128, 32) | 1568 |

c32 (Conv1D) | (128, 32) | 3104 |

add_3_2 (Add) | (128, 32) | 0 |

x3p (Max-pooling1D) | (64, 32) | 0 |

flatten (Flatten) | (2048) | 0 |

out (Dense) | (128) | 262,272 |

Model | Embedding | Characteristics | Params |
---|---|---|---|

MLP | None | Fully connection | 528,554 |

BiLSTM | Convolution | LSTM | 16,704 |

CNN | None | Convolution | 65,820 |

ResNet | None | Residual block | 268,452 |

Model | A→B | A→C | A→D | D→A |
---|---|---|---|---|

MLP | 64.00% | 41.87% | 44.62% | 45.12% |

BiLSTM | 87.83% | 59.25% | 75.38% | 74.62% |

CNN | 95.10% | 73.35% | 80.75% | 92.50% |

ResNet | 96.50% | 71.3% | 91.62% | 95.50% |

DA-CNN | 98.37% | 76.75% | 97.70% | 94.50% |

DA-ResNet | 99.87% | 83.5% | 98.12% | 95.40% |

Model | A→B | A→C | A→D | D→A |
---|---|---|---|---|

MLP | 60.25% | 37.03% | 38.22% | 38.45% |

BiLSTM | 87.88% | 54.22% | 71.26% | 87.49% |

CNN | 94.50% | 73.10% | 77.53% | 92.34% |

ResNet | 96.50% | 85.50% | 87.10% | 95.50% |

DA-CNN | 98.46% | 70.07% | 97.70% | 94.40% |

DA-ResNet | 99.80% | 83.62% | 98.10% | 95.70% |

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## Share and Cite

**MDPI and ACS Style**

Liu, Y.; Xiang, H.; Jiang, Z.; Xiang, J.
A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data. *Sensors* **2023**, *23*, 3068.
https://doi.org/10.3390/s23063068

**AMA Style**

Liu Y, Xiang H, Jiang Z, Xiang J.
A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data. *Sensors*. 2023; 23(6):3068.
https://doi.org/10.3390/s23063068

**Chicago/Turabian Style**

Liu, Yi, Hang Xiang, Zhansi Jiang, and Jiawei Xiang.
2023. "A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data" *Sensors* 23, no. 6: 3068.
https://doi.org/10.3390/s23063068