Fault diagnosis of wet clutch control system of tractor hydrostatic power split continuously variable transmission

Xue, Lijun; Honghua, Jiang; Zhao, Yehui; Jiabo, Wang; Wang, Guangming; Xiao, Maohua

doi:10.1016/j.compag.2022.106778

Cited by 14 publications

(8 citation statements)

References 20 publications

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“…According to different design principles, different clutch architectures have been devised such as friction clutch 2 , 3 , Electro mechanically clutch taking motor as power source 4 , 5 , hydraulic or electro hydraulic clutch 6 , 7 and electro pneumatic clutch 8 , 9 . To realize clutch engagement or disengagement control, clutch chamber pressure, transmission torque and clutch piston displacement are the mostly used control modes.…”

Section: Introductionmentioning

confidence: 99%

An improved adaptive position tracking strategy for automatic shift actuator with uncertain parameters

Yin,

Wang,

Gao

et al. 2024

Sci Rep

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Realizing precise and fast position control of the gear is a challenging issue because of its nonlinearity, parameter uncertainty and external disturbance. Therefore, this paper researches the clutch position control considering the influence because of the factor on the system performance. By virtue of the traditional adaptive control method, an improved strategy based on finite time theory is proposed to further improve the convergence rate as well as the position tracking precision. First, a model of electromechanical clutch actuator system is established by theoretical analysis. Then, an enhanced adaptive controller is designed using finite time idea by introducing power function in the virtual control. And parameter update rate is adopted in the control action. Next, the stability of the control system is proved theoretically. Finally, Matlab simulations and experimental bench test are carried out to exhibit the effectiveness of the presented method. The results show that the satisfactory performance has been achieved with accurate position tracking and fast convergence speed.

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Section: Introductionmentioning

confidence: 99%

An improved adaptive position tracking strategy for automatic shift actuator with uncertain parameters

Yin,

Wang,

Gao

et al. 2024

Sci Rep

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“…This functional significance of the clutch is underpinned by the theoretical principles of friction and thermal dynamics. The clutch's operation relies on the fundamental concept of friction, whereby surfaces in contact generate resistance to relative motion [1]. This resistance, manifested as frictional forces, is essential for controlled power transmission and the engagement and disengagement of the clutch mechanism.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based fault detection of automobile dry clutch system using spectrogram plots

Sai S,

Sridharan,

Chakrapani

et al. 2024

Eng. Res. Express

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In this research study, a novel approach that leverages the power of transfer learning (a famous deep learning technique) is proposed to diagnose multiple types of clutch faults including, worn release fingers, fractured pressure plates, deteriorated pressure plates, loss of friction material and distorted tangential strips using spectrogram plots. To train and validate the diagnostic system, vibration readings were taken from a specially designed test rig with the help of piezoelectric accelerometer while the clutch system was operated under different load conditions of 0 (no load), 5 and 10 kg This procedure of data collection was then repeated to acquire the vibration data for all of the fault conditions by replacing the good with fault components individually. These vibration signals were further processed and transformed into spectrogram plots that serves as the input data for the deep learning models considered. Fine-tuning techniques were applied on pretrained networks to maximise the prediction accuracy of the models to effectively determine and diagnose faults in the clutch system. For this study 12 pre-trained networks were chosen namely, Xception, InceptionResNet, DenseNet, AlexNet, VGG16, GoogLeNet, VGG19, ResNet101, ResNet50, InceptionV3, MobileNetV2 and ShuffleNet. To optimize the performance of deep learning models, a systematic adjustment of hyperparameters such as the train-test split ratio, learning rate, optimizer and batch size for each network model was carried out. Through careful experimentation and analysis, significant improvements in fault classification accuracy were achieved thereby enhancing the reliability and effectiveness of the diagnostic system. From the results it was noted that 100% classification accuracy was displayed by AlexNet (for the no load condition and the 10kg load condition) and GoogLeNet (for 5 kg load condition) with extremely low computation times.

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“…The structure of the transmission system of HMCVT is complex, but overall it can be divided into mechanical systems and hydraulic systems. Mechanical system faults are mainly gear faults, and the current fault diagnosis methods for mechanical systems are relatively mature, such as wavelet analysis [10,11], support vector machine [12], hidden Markov model [13], etc. Hydraulic system faults include pump motor hydraulic system failures and clutch hydraulic system failures, which can be identified by analyzing pressure, flow, power and other data.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2023

Agriculture

Self Cite

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There are some problems in the shifting process of hydraulic CVT, such as irregularity, low stability and high failure rate. In this paper, the BP neural network and convolutional neural network are used for fault diagnosis of the HMCVT hydraulic system. Firstly, through experiments, 120 groups of pressure and flow data under normal and four typical fault modes were obtained and preprocessed; they were divided into 80 groups of training samples and 40 groups of test samples via random extraction, using the BP neural network model and convolutional neural network model for fault classification. The results show that compared with BP, PSO-BP and other models, the fault diagnosis rate of the BAS-BP neural network model can reach 92.5%, and the average diagnosis accuracy rate of the convolutional neural network can reach 97.5%, which can be effectively applied to the fault diagnosis of the HMCVT hydraulic system and provide some reference for the shifting reliability of hydraulic CVT.

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Fault diagnosis of wet clutch control system of tractor hydrostatic power split continuously variable transmission

Cited by 14 publications

References 20 publications

An improved adaptive position tracking strategy for automatic shift actuator with uncertain parameters

An improved adaptive position tracking strategy for automatic shift actuator with uncertain parameters

Deep learning based fault detection of automobile dry clutch system using spectrogram plots

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

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