A new raw signal fusion method using reweighted VMD for early crack fault diagnosis at spline tooth of clutch friction disc

Li, Zixian; Xiao, Jiawei; Ding, Xiaoxi; Wang, Li Ming; Yang, Yang; Zhang, Wanhao; Du, Minggang; Shao, Yimin

doi:10.1016/j.measurement.2023.113414

Cited by 5 publications

(1 citation statement)

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“…The nature and the frequency of these forces underscore the necessity for the clutch to exhibit a harmonious blend of strength and heat resistance. This is where the principles of materials science and engineering design come into play, guiding the selection of materials and the development of clutch components that can withstand the demanding operating conditions [2]. Recognizing the indispensable role of clutches in the realm of automotive propulsion highlights the importance of fault diagnosis.…”

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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Section: Introductionmentioning

confidence: 99%