# Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN

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## Abstract

**:**

## 1. Introduction

## 2. Construction of HMCVT Shift Hydraulic System Test Platform

## 3. Obtaining Test of Fault Sample of HMCVT Shift Hydraulic System

#### 3.1. Typical Failure Type

- (1)
- Normal mode (Fault Type 1): all of the parameters of the shift hydraulic system are in the normal working range, and there is no abnormality in the shifting process.
- (2)
- The clutch piston is stuck (Fault Type 2): The clutch will always be in a different degree of slipping state. The lighter can change the segment but cannot load (the slip will stall as soon as it is loaded), and the heavy will directly burn the clutch.
- (3)
- The seal ring at the rotary joint is damaged (Fault Type 3): Local internal leakage occurs in the oil passage, but this leakage occurs inside the oil passage. When the oil passage is filled for the first time, the oil pressure is difficult to establish. As the oil channel is filled with hydraulic oil, its influence on the establishment of the shift section hydraulic pressure is no longer significant, and this fault has an impact on the shift section quality of the transmission.
- (4)
- The outlet oil passage of the governor valve is blocked (Fault Type 4): The oil filling flow of the clutch is reduced, and the sliding time between the main and driven friction plates is extended. This not only deteriorates the quality of the shift, but also increases the risk of power interruption and clutch burnout. This fault mode has a gradual characteristic and is not easy to detect.
- (5)
- Leakage of the branch of the oil pipeline (Fault Type 5): the fault generally occurs at the place of the pipe joint of the branch oil pipeline, and a partial external leakage occurs in the oil pipeline.

#### 3.2. Fault Simulation and Data Obtained

_{f}, C

_{f}, K

_{f}, I

_{f}and S

_{f}are flow statistics, respectively, representing the root mean square value of flow, peak factor, kurtosis factor, pulse factor and form factor during the transition period; X

_{p}is the pressure statistic, which represents the root mean square pressure of the pressure during the transition; x

_{fi}and x

_{pi}represent the data of the i-th sampling point of flow and pressure, respectively, and N

_{f}and N

_{p}represent the total number of data sampling points of flow and pressure, respectively. After the attribute calculation, a sample set of 120 fault data characteristic attributes was obtained. Randomly, we set 80 of them as training samples and 40 of them as test samples.

## 4. BP Method for FAULT Diagnosis of HMCVT Shift Hydraulic System

#### 4.1. Fault Diagnosis of Shift Hydraulic System Based on BP Neural Network

#### 4.2. Fault Diagnosis of Shift Hydraulic System Based on PSO-BP Neural Network

#### 4.3. Fault Diagnosis of Shift Hydraulic System Based on BAS-BP Neural Network Model

## 5. CNN Method for Fault Diagnosis of HMCVT Shift Hydraulic System

#### 5.1. Convolutional Neural Network Overview

#### 5.1.1. Convolutional Layer

#### 5.1.2. Pooling Layer

#### 5.1.3. Fully Connected Layer

#### 5.1.4. Convolutional Neural Network Structure

#### 5.2. Attribute Reduction Based on Rough Set

_{f}, C

_{f}, K

_{f}, I

_{f}, S

_{f}and X

_{p}have different abilities to distinguish the fault mode. In order to reduce unnecessary attribute calculation, on the premise of ensuring the correct rate of fault diagnosis, the attributes that contribute less to fault diagnosis can be deleted. This paper is based on the rough set theory [30,31,32] for attribute reduction.

_{p}; mode T2 retains two characteristic attributes of X

_{f}and X

_{p}; mode T3 retains two feature attributes of X

_{f}and X

_{p}; mode T4 retains three feature attributes of X

_{f}, S

_{f}and X

_{p}and mode T5 retains two feature attributes of S

_{f}and X

_{p}.

_{p}exceeds 50%, which are common and indispensable characteristic attributes in the fault modes. That is to say, compared with the flow data, the pressure data has the greatest influence on the recognition of the fault mode of the shift hydraulic system. Therefore, on the premise of ensuring a high accuracy of fault diagnosis, it can be considered to use only the original pressure data of the shift hydraulic system as the input to train the convolutional neural network model, and then to obtain a fault diagnosis model with an ideal effect.

#### 5.3. Fault Diagnosis Results Based on CNN and Neighborhood Rough Set

## 6. Conclusions

- (1)
- Various types of faults have greater separability after nonlinear transformation by the BP network, the overall similarity is low and the model fault classification effect is good.
- (2)
- The optimized BP neural network model is better than the unoptimized BP neural network model for fault recognition. Its average diagnostic accuracy rate reached 92.5%.
- (3)
- The BAS-BP neural network model has the strongest ability to identify the fault of the oil channel blockage fault T4, which is conducive to further analysis to determine and eliminate the fault.
- (4)
- The experiment shows that the pressure data has the greatest influence on the recognition of the fault mode of the shift hydraulic system. It is feasible to use the pressure data of the hydraulic system as the only input parameter to identify the fault mode of the HMCVT shift hydraulic system.
- (5)
- The convolutional neural network under the same test conditions is significantly better than the shallow optimized BP neural network in the application of the fault diagnosis of the shift hydraulic system, and its fault diagnosis accuracy can reach 97.5%.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**HMCVT shift hydraulic control system. 1. Motor. 2. Oil pump. 3. Check valve. 4. Relief valve. 5. Flow sensors. 6. Pressure sensor. 7. Control valve. 8. Electromagnetic valve. 9. Integrated valve plate.

**Figure 2.**HMCVT overall test bench. 1. Diesel engines. 2. Front speed torque meter. 3. Transmission. 4. Hydraulic fixed motor. 5. Hydraulic variable pump. 6. Rear speed torque meter. 7. Loader. 8. Measurement and control platform. 9. Shift hydraulic control system.

**Figure 11.**Two-dimensional gray image under five types of fault modes. (

**a**) Normal mode T1, (

**b**) spring fault T2, (

**c**) seal ring fault T3, (

**d**) blockage of oil passage T4 and (

**e**) cavitation seal T5.

Number | Part Name | Model | Main Performance Parameters |
---|---|---|---|

1 | Asynchronous motor | JO2-22-4 | Rated speed: 1450 r/min |

2 | Vane pump | YB1-6.3 | Displacement: 6.3 mL/r |

3 | Relief valve | 2FRM5/10QB | Adjustment range: 0~10 L/min |

4 | Pressure sensor | NS-F | Measuring range: 0~10 MPa |

Output signal: 0~5 V | |||

5 | Flow sensors | LWGB-4 Turbine flow sensor | Measuring range: 0~0.4 m^{3}/h |

Output signal: 4~20 mA |

Models | T1 | T2 | T3 | T4 | T5 | Correct Rate | |
---|---|---|---|---|---|---|---|

Modes | |||||||

BP | 100% | 100% | 87.5% | 50% | 100% | 87.5% | |

PSO-BP | 100% | 100% | 87.5% | 75% | 100% | 92.5% | |

BAS-BP | 100% | 100% | 75% | 100% | 87.5% | 92.5% |

Modes | X_{f} | C_{f} | K_{f} | I_{f} | S_{f} | X_{p} |
---|---|---|---|---|---|---|

T1 | 0 | 0 | 0 | 0 | 0 | 1 |

T2 | 0.0278 | 0 | 0 | 0 | 0 | 0.9722 |

T3 | 0.2222 | 0 | 0 | 0 | 0 | 0.7778 |

T4 | 0.0217 | 0 | 0 | 0 | 0.3478 | 0.6304 |

T5 | 0 | 0 | 0 | 0 | 0.4324 | 0.5676 |

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## Share and Cite

**MDPI and ACS Style**

Wang, J.; Lu, Z.; Wang, G.; Hussain, G.; Zhao, S.; Zhang, H.; Xiao, M.
Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN. *Agriculture* **2023**, *13*, 461.
https://doi.org/10.3390/agriculture13020461

**AMA Style**

Wang J, Lu Z, Wang G, Hussain G, Zhao S, Zhang H, Xiao M.
Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN. *Agriculture*. 2023; 13(2):461.
https://doi.org/10.3390/agriculture13020461

**Chicago/Turabian Style**

Wang, Jiabo, Zhixiong Lu, Guangming Wang, Ghulam Hussain, Shanhu Zhao, Haijun Zhang, and Maohua Xiao.
2023. "Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN" *Agriculture* 13, no. 2: 461.
https://doi.org/10.3390/agriculture13020461