Diesel Engine Gearbox Fault Diagnosis Based on Multi-features Extracted from Vibration Signals

Wang, Aina; Li, Yingshun; Du, Xian; Zhong, Chongquan

doi:10.1016/j.ifacol.2021.10.137

Cited by 7 publications

(10 citation statements)

References 17 publications

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“…The comparison results obtained from this are shown in Figure 3. As can be seen from Figure 3, although the reference [3] system and the reference [4] system have played a certain degree of denoising effect, some effective signals are still directly removed as abnormal components, and the original details of some signals are lost, which has some distortion problems; The designed system has the best denoising effect, which preserves the overall trend of the original signal to a greater extent, and at the same time presents the detailed information in the signal well, improving the accuracy of fault identification.…”

Section: Experimental Results and Analysismentioning

confidence: 95%

“…However, due to the influence of high noise and other factors, the pulse component is weak and the fault diagnosis is difficult. In order to reflect the superiority of the designed system, a group of engine vibration signals with strong noise are randomly collected in the experiment, and the signals are denoised by the designed system, the reference [3] system and the reference [4] system, respectively. The comparison results obtained from this are shown in Figure 3.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Simulation results show that the proposed method has good tracking performance. Reference [4] proposes the fault diagnosis of diesel gearbox based on multi-feature extraction of vibration signal, which extracts the feature vector from the natural mode function, and combines the time domain digital feature with Hilbert edge spectrum as the feature vector of gearbox. The fault classification of gearbox is identified by support vector machine.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of embedded identification system for motor vehicle engine fault based on deep learning

Chen,

Cheng,

Yang

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Aiming at the low efficiency of fault identification in the process of engine fault identification, an embedded identification system of motor vehicle engine fault based on deep learning is designed. A hardware system consisting of sensor, core processor and communicator is established. The sensor module contains qualitative sensor and quantitative sensor. The qualitative sensor adopts LEI sensor, the quantitative sensor adopts TDK sensor, and the core processor is used as the central processor. The insulation fault information of three bands of high, medium and low frequency is decomposed, the feature vector is determined, and the fault information extraction program is established to complete the design of embedded identification system for motor vehicle engine fault. The experimental results show that the designed system has the best denoising effect, retains the overall trend of the original signal to a greater extent, and presents the detailed information in the signal well, which improves the fault identification accuracy and has stronger fault identification performance.

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Section: Experimental Results and Analysismentioning

confidence: 95%

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of embedded identification system for motor vehicle engine fault based on deep learning

Chen,

Cheng,

Yang

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

show abstract

“…The results proved the effectiveness and superiority of SQSMD in gear fault diagnosis. Wang et al [4] used a vibration signal analysis method based on integrated empirical mode decomposition and support vector machine to diagnose diesel engine faults. The method accurately identified multiple fault information from the collected vibration signals under different states of the gearbox.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning

2023

Teh. vjesn.

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Under different working conditions of gearbox, the feature extraction of fault signals is difficult, and large difference in data distribution affects the fault diagnosis results. Based on the problems, the research proposes a method based on improved MobileNetV3 network and transfer learning (TL-Pro-MobilenetV3 network). Three timefrequency analysis methods are used to obtain time-frequency distribution. Among them, short time Fourier transform (STFT) combined with Pro-MobilenetV3 network takes the shortest time and has the highest accuracy. Furthermore, transfer learning is introduced into the model, and the optimal training parameters are selected training the network. Using the dataset from Southeast University, the TL-Pro-MobilenetV3 model is compared with four classical fault diagnosis models. The experimental results show the accuracy of the method proposed can reach 100% and the training time is the shortest in two working conditions, proving the proposed model has a good performance in generalization ability, recognition accuracy and training time.

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“…5 After that, the processed vibration of diesel engine was calculated to feature the fault pattern information of the vibration signal of diesel engines, including time-, frequency- and time-frequency- domain statistic parameters, including means, standard deviation, kurtosis, skewness, gray values, and waveform factor. These commonly signal processing methods include empirical mode decomposition (EMD), 16,17 intrinsic time scale decomposition (ITD), 18,19 wavelet packet transform (WPD), 20 variational mode decomposition (VMD), 21,22 empirical wavelet transform (EWT), 23 and their improved algorithms. 24,25 Although, the based signal processing methods to extract the fault features of the diesel engine vibration signals have been proved to be successfully applied to diagnose the faults of diesel engines by various optimized methods.…”

Section: Introductionmentioning

confidence: 99%

Refined composite multiscale fuzzy entropy based fault diagnosis of diesel engine

Zhang

Zhu

et al. 2022

Journal of Low Frequency Noise, Vibration and Active Control

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Due to complicated transfer paths and strong background noise interference, the fault pattern information deeply hides in common features of the vibration signal at the engine surface. In this study, the refined composite multiscale fuzzy entropy (RCMFE) used to measure the irregularity and self-similarity of time series is proposed to quantify the feature of various fault patterns. Followed by RCMFE, the features dug out are recognized by a parameter-adaptive support vector machine based on the firefly algorithm (FASVM). After putting forward the diagnosing schematics, the RCMFE-FASVM is applied to a fault diagnosis case of a diesel engine on a test rig. A comparative analysis of the four methods to extract features and the four methods to recognize fault patterns are conducted. Results indicate the proposed method has superior recognition performance and can effectively identify the working states of the diesel engine, contrasting the existing methods. Under the small samples and features task of identifying the working states of a diesel engine, the recognition rate of the proposed method with more stability can reach 98.2%, which is larger than other methods. Given the superior performance of the proposed method, the number of input features and training samples should vary from 8 to 20 and from 35 to 50.

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Diesel Engine Gearbox Fault Diagnosis Based on Multi-features Extracted from Vibration Signals

Cited by 7 publications

References 17 publications

Design of embedded identification system for motor vehicle engine fault based on deep learning

Design of embedded identification system for motor vehicle engine fault based on deep learning

Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning

Refined composite multiscale fuzzy entropy based fault diagnosis of diesel engine

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