A Novel Empirical Variational Mode Decomposition for Early Fault Feature Extraction

Xu, Bo; Li, Huipeng

doi:10.1109/access.2022.3232553

Cited by 6 publications

(2 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, c ij is updated according to Equation (7). If the correlation between the two capsules is high, the value of their coupling coefficient c ij will increase, and vice versa will decrease.…”

Section: Capsule Networkmentioning

confidence: 99%

“…Traditional composite fault diagnosis methods usually use signal processing techniques, such as wavelet transform analysis [4], envelope analysis [5], and empirical modal decomposition [6,7], for feature extraction and then shallow machine learning models, such as BP neural networks [8] and support vector machines [9], for fault classification. Although traditional methods have achieved fruitful results, the following drawbacks still exist in the era of big data [10]: (1) the process of feature extraction and selection using signal processing techniques is complex, requires manual operations, and relies mainly on engineering experience; (2) manual feature extraction reduces the complexity of the input data and causes rich fault state information to be lost in the original data; and (3) the traditional signal feature extraction techniques make it difficult to separate the coupled features of compound faults.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Deep Neural Network in Gearbox Compound Fault Diagnosis

Zhang

Hong

et al. 2023

Energies

View full text Add to dashboard Cite

Gearbox fault diagnosis is vital to ensure the efficient operation of rotating machinery, and most gearbox faults in industrial production occur in the form of compound faults. To realize the diagnosis of compound faults in gearboxes at different speeds, an “end-to-end” intelligent diagnosis method based on an efficient channel attention capsule network (ECA-CN) is proposed. First, the process uses a deep convolutional neural network to extract fault features from the collected raw vibration signals, embeds the efficient channel attention module to filter important fault features, uses the capsule network to vectorize the feature space information and, finally, calculates the correlation between different levels of capsules using a dynamic routing algorithm to achieve accurate gearbox compound fault diagnosis. The effectiveness of the proposed ECA-CN fault diagnosis method is verified using the composite fault dataset of the 2009 PHM Challenge gearbox, with an average accuracy of 99.63 ± 0.22%. In the comparison experiments using the traditional fault diagnosis method, the average accuracy of the ECA-CN method improved by 4.62%, and the standard deviation was reduced by 0.58%. The experimental results show that the ECA-CN has a more competitive diagnostic performance than traditional shallow machine learning models and CNNs.

show abstract

Section: Capsule Networkmentioning

confidence: 99%