A Fuzzy‐Based Analytic Hierarchy Process for Mechanical Noise Source Identification of a Diesel Engine

Zhang, Mengjie; Zhou, Qidi; Lin, Jiewei; Li, Weidong; Tang, Zhoujie; Chaoyang, Duan

doi:10.1155/2019/2854836

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…of which, engine abnormal noise has accounted for the highest proportion, and is also the most difficult to control and solve. Engine abnormal noise with a wide frequency distribution comes from many different sources, such as combustion noise and mechanical noise [1][2][3][4][5][6]. combustion noise is mostly related to the unreasonable design of the combustion chamber structure, while Mechanical noise is mainly caused by poor mechanical performance of parts or improper assembly.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Noise Identification of Engines Based on Wavelet Transform and Bispectrum Analysis

Yang¹,

Zhang²,

Li³

2023

Preprint

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Abnormal noise is the most prominent problem for motorcycle, which affect consumers' purchasing desire and driving experience. The abnormal noise engines are always detected by manual auscultation(MA) after assembly. While MA is also affected by subjective and objective factors, its accuracy fluctuates greatly and has a large labor intensity. The most important is that it can not be applied in a corporation with mass production and high quality. To overcome those problems, this paper proposed an online engine abnormal noise detection method based on wavelet transform(WT) and bispectrum analysis(BA), which improves the accuracy and stability of engine abnormal noise identification and reduces the cost of the check. Firstly, the engine's acoustic signal is acquired by using a free-field microphone. Secondly, eliminated the background noise of signals by using the wavelet correlation coefficient(WCC) theory and extracted the signal features by applying power spectrum estimation and bispectrum analysis respectively. Thirdly, feature vectors are normalized before being used as support vector machine(SVM) samples. Fourthly, the appropriate kernel function and parameters are selected to train the vector machine using the training sample. Finally, testing samples are used to inspect the accuracy of vector machines. The result shows that the training accuracy and testing accuracy of the samples is high by using the method of WT-BA-SVM (wavelet transform - bispectrum analysis - support vector machines). WT-BA-SVM identified engine fault types effectively and provided the theoretical foundation for the establishment of an engine abnormal noise online detection system.

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Section: Introductionmentioning

confidence: 99%

Abnormal Noise Identification of Engines Based on Wavelet Transform and Bispectrum Analysis

Yang¹,

Zhang²,

Li³

2023

Preprint

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show abstract

“…Yao et al [13] adopted the VMD-Robust ICA method to separate the combustion noise and the piston slap noise from measured engine noise. Zhang et al [14] proposed the VMD-fuzzy analytic hierarchy process to weight the mechanical noise sources of a diesel engine. However, the accuracy and efficiency of the VMD strongly depend on the parameters pre-set manually.…”

Section: Introductionmentioning

confidence: 99%

A parameter-adaptive variational mode decomposition approach based on weighted fuzzy-distribution entropy for noise source separation

Zhou

Zhang

Zhou

et al. 2020

Meas. Sci. Technol.

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Due to limitations in the generalisation ability of currently proposed improved variational mode decomposition (VMD) methods, it is hard to precisely and efficiently discern signal characteristics from different power systems. Meanwhile, it is difficult to separate non-order noise sources in current studies. To address this issue, a novel scheme is proposed based on parameter-adaptive VMD and partial coherence analysis (PCA) for separating noise sources. In this approach, weighted fuzzy-distribution entropy (FuzzDistEn) is constructed to optimise the VMD to adaptively obtain the optimal parameters, considering the complexity of the signal system, and the mutual information between the decomposition components and the original signal. To verify the effectiveness and superiority of the proposed method, the paper respectively compares the decomposition results of the simulated signal using different objective functions, and shows that the weighted FuzzDistEn has a better decomposition effect. For the other issue, PCA is adapted to estimate the coherence between component vibration and radiated noise. In a case study, the parameter-adaptive VMD-PCA approach is implemented in a diesel engine noise identification field based on bench experiments. The results show that the proposed method can successfully separate five surface radiated noise sources. The research offers a new perspective on feature extraction problems.

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Selection of smart fuel opus for diesel engine depending on their fuel characteristics: an intelligent hybrid decision-making approach

Paramasivam

Kumanan

2021

Environ Sci Pollut Res

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A Fuzzy‐Based Analytic Hierarchy Process for Mechanical Noise Source Identification of a Diesel Engine

Cited by 3 publications

References 31 publications

Abnormal Noise Identification of Engines Based on Wavelet Transform and Bispectrum Analysis

Abnormal Noise Identification of Engines Based on Wavelet Transform and Bispectrum Analysis

A parameter-adaptive variational mode decomposition approach based on weighted fuzzy-distribution entropy for noise source separation

Selection of smart fuel opus for diesel engine depending on their fuel characteristics: an intelligent hybrid decision-making approach

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