Analysis of automotive gearbox faults using vibration signal

The decomposition number K and penalty factor α in the variational mode decomposition (VMD) algorithm have a great influence on the decomposition effect and the accuracy of subsequent fault diagnosis. Therefore, a gear fault diagnosis method based on genetic mutation particle swarm optimization VMD and probabilistic neural network (GMPSO-VMD-PNN) algorithm is proposed in this paper. Firstly, the GMPSO algorithm is used to optimize the [K , α] parameter combination in the VMD algorithm, and the optimal [K , α] parameter combination of each gear fault vibration signal to be decomposed is selected. Then, the gear fault vibration signal is decomposed into several intrinsic mode functions (IMFs) by VMD, and the sample entropy value of each IMFs is extracted to form the feature vector of subsequent fault diagnosis. Finally, the characteristic vector of gear fault vibration signal is input into PNN model, and gear fault is accurately classified. By comparing with fixed parameter VMD algorithm, empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm, the superiority of this method in gear fault diagnosis is verified. Therefore, the GMPSO-VMD-PNN algorithm proposed in this paper has certain application value for gear fault diagnosis.

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“…Step 1. The decomposed IMFs {x K (t) |1 ≤ t ≤ N } is given the mode dimension, and the original sequence is used to form (13) where:…”

Section: Sample Entropymentioning

confidence: 99%

Gear Fault Diagnosis Based on Genetic Mutation Particle Swarm Optimization VMD and Probabilistic Neural Network Algorithm

Ding

Xiao

2020

IEEE Access

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“…e aim of the scientific paper by Barbieri et al was to identify the presence of component errors in car transmissions by comparing the vibration signals of damaged and undamaged transmissions. Different signal analysis techniques based on wavelet transform, mathematical morphology, and energy (entropy) were used to verify the presence of damage in the systems [7]. A signal processing technique combining pattern spectrum and selective filtering in certain frequencies ranges was used for identification of component failures [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Research into Correlation between the Lubrication Mode of Contact Surfaces and Dynamic Parameters of Turbo-Generator Transmissions

Kočiško

Baron

Török

et al. 2019

Advances in Materials Science and Engineering

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e paper presents the results of an experimental study aimed at assessing the correlation between the measurement of dynamic parameters (vibration, high-frequency vibration, and acoustic emission) and the analysis of friction mode and the state of lubrication of the contact surfaces of two gearboxes in the turbo-generator assembly (high-speed single-body steam turbine-gearbox-generator) with the transmission power of no more than 50 MW. e analysis confirmed the assumption of a significant correlation of the monitored high-frequency vibration signal with the unsatisfactory engagement of the gear teeth.rough vibration analysis, an increased level of the tooth vibration component and vibration multiples with increased acoustic emission were identified in gearbox operation. e gear oil of one of the gearboxes examined showed a loss of additive elements in the real operation of the contact surfaces of the teeth engagement. e trend analysis confirmed the complexity of the monitored transmission operation in terms of the friction mode and the influence of the oil quality on the state of the tooth flank microgeometry.

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“…CWT is used to create a timefrequency signal representation that offers very strong time and frequency localisation [20]. Lin and Qu [21] developed a novel method using the Morlet wavelet for identifying faults in a gearbox without de-noising. However, the researchers state that the continuous wavelet transform is an effective tool [22] [23] for inspecting the residual signals and localisation of faults in gearboxes.…”

Section: Introductionmentioning

confidence: 99%

Diagnostics and prognostics of planetary gearbox using CWT, auto regression (AR) and K-means algorithm

2021

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Analysis of automotive gearbox faults using vibration signal

Cited by 26 publications

References 30 publications

Gear Fault Diagnosis Based on Genetic Mutation Particle Swarm Optimization VMD and Probabilistic Neural Network Algorithm

Gear Fault Diagnosis Based on Genetic Mutation Particle Swarm Optimization VMD and Probabilistic Neural Network Algorithm

Research into Correlation between the Lubrication Mode of Contact Surfaces and Dynamic Parameters of Turbo-Generator Transmissions

Diagnostics and prognostics of planetary gearbox using CWT, auto regression (AR) and K-means algorithm

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