An engine fault diagnosis system using intake manifold pressure signal and Wigner–Ville distribution technique

Wu, Jian-Da; Huang, Cheng-Kai

doi:10.1016/j.eswa.2010.06.099

Cited by 43 publications

(23 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…5. In contrast to the BPNN, the learning procedure for the GRNN is a one-pass learning algorithm; thus, the iterative training process is unnecessary [52]. There are 4 layers in the GRNN model: the input layer, pattern layer, summation layer and output layer.…”

Section: Parameter Settings and Comparison Schemesmentioning

confidence: 99%

Sound quality prediction of vehicle interior noise using deep belief networks

Huang

et al. 2016

Applied Acoustics

View full text Add to dashboard Cite

The sound quality of vehicle interior noise strongly influences passengers' psychological and physiological perceptions. To predict the sound quality of interior noise, a vehicle road test with four compact cars has been conducted. All recorded interior noise signals have been denoised via a discrete wavelet transform (DWT) denoising procedure and subsequently evaluated subjectively through the anchor semantic differential (ASD) test by a jury. In addition, a novel prediction method, namely, regression-based deep belief networks (DBNs), which substitute the support vector regression (SVR) layer for the linear softmax classification layer at the top of the general DBN's structure, has been proposed to predict the interior sound quality. The parameter selection of the DBN model has been compared and studied using a grid search. In addition, four conventional machine-learning-based methods have been introduced to enable a comparison of the performance with the newly developed DBNs. Furthermore, the feature fusion ability of DBNs has been studied by varying the amount of information that the dataset offers. The results show the following: 1) The accuracy and robustness of the proposed DBN-based sound quality prediction approach are better than those of the 4 other referenced

show abstract

Section: Parameter Settings and Comparison Schemesmentioning

confidence: 99%

Sound quality prediction of vehicle interior noise using deep belief networks

Huang

et al. 2016

Applied Acoustics

View full text Add to dashboard Cite

show abstract

“…Being rich in information on machinery health conditions, vibration signals are often processed by advanced signal processing techniques to extract features from the rotating machinery [6,7]. Up to now, multiple methods have been extensively applied for vibration signal processing, such as fast Fourier transform (FFT), short-time Fourier transform (STFT) [8], wavelet transform (WT) [9], Wigner-Ville distribution (WVD) [10], empirical mode decomposition (EMD) [11], multiwavelets transform (MWT) [12], etc. However, in spite of all the achievements these methods have made, they are fixed and independent of the measured signals.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis

Lü

Zhang²,

Zhang

et al. 2019

Shock and Vibration

View full text Add to dashboard Cite

Feature extraction is a key procedure in the fault diagnosis of rotating machinery. To obtain fault features with lower dimensionality and higher sensitivity, a feature extraction method based on adaptive multiwavelets transform (AMWT) and local tangent space alignment (LTSA) is proposed in this paper. AMWT is first used to obtain multiple features from the vibration signals of the machine under test to form a high-dimensional feature set. en, in order to avoid the adverse effect of the irrelevant features in this high-dimensional feature set on the fault diagnosis result, a detection index (DI) is investigated to evaluate the sensitivity of the features and those with lower sensitivity are removed. After that, LTSA is applied for feature fusion to reduce the redundant features in the high-dimensional feature set. To validate the proposed method, performance of four feature extraction schemes based on (i) wavelet and LTSA, (ii) Geronimo, Hardin, and Massopust (GHM) multiwavelets and LTSA, (iii) AMWT and principal component analysis (PCA), and (iv) AMWT and multidimensional scaling (MDS) is compared with the proposed method. e feature extraction results by these methods are then fed into K-medoids classifier to discriminate the faults. e results show that the proposed method can improve the sensitivity of the extracted features and obtain higher fault recognition rate.

show abstract

“…It should be self-adaptive and provides remarkable features with little human intervention to extract valid features under variable conditions. Currently, a lot time-frequency analysis methods have been employed for feature extraction for rotating machineries, such as bearing, gearbox [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Wavelet transform, as a well-known time-frequency analysis tool, has been employed wildly to decompose the nonstationary signals.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD

Wang

Jia

Qin

2018

Entropy

View full text Add to dashboard Cite

Rotating machineries often work under severe and variable operation conditions, which brings challenges to fault diagnosis. To deal with this challenge, this paper discusses the concept of adaptive diagnosis, which means to diagnose faults under variable operation conditions with self-adaptively and little prior knowledge or human intervention. To this end, a novel algorithm is proposed, information geometrical extreme learning machine with kernel (IG-KELM). From the perspective of information geometry, the structure and Riemannian metric of Kernel-ELM is specified. Based on the geometrical structure, an IG-based conformal transformation is created to improve the generalization ability and self-adaptability of KELM. The proposed IG-KELM, in conjunction with variation mode decomposition (VMD) and singular value decomposition (SVD) is utilized for adaptive diagnosis: (1) VMD, as a new self-adaptive signal processing algorithm is used to decompose the raw signals into several intrinsic mode functions (IMFs). (2) SVD is used to extract the intrinsic characteristics from the matrix constructed with IMFs. (3) IG-KELM is used to diagnose faults under variable conditions self-adaptively with no requirement of prior knowledge or human intervention. Finally, the proposed method was applied on fault diagnosis of a bearing and hydraulic pump. The results show that the proposed method outperforms the conventional method by up to 7.25% and 7.78% respectively, in percentages of accuracy.

show abstract

An engine fault diagnosis system using intake manifold pressure signal and Wigner–Ville distribution technique

Cited by 43 publications

References 14 publications

Sound quality prediction of vehicle interior noise using deep belief networks

Sound quality prediction of vehicle interior noise using deep belief networks

Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis

Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD

Contact Info

Product

Resources

About