Dual-Ensemble Multi-Feedback Neural Network for Gearbox Fault Diagnosis

Xia, Tangbin; Jiang, Yimin; Zhuo, Pengcheng; Xi, Lifeng; Wang, Dong

doi:10.1109/tim.2021.3089250

Cited by 14 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, a shallow machine learning method to perform pattern recognition and to complete the intelligent diagnosis of gearbox faults can be conducted [12]. The commonly used shallow machine learning methods mainly include the BP neural network [13], the support vector machine (SVM) [14], the extreme learning machine (ELM) [15], and other models. Although the traditional machine learning algorithm can realize the intelligent diagnosis of faults, it improves the efficiency and accuracy of a fault diagnosis to a certain extent [16].…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation

et al. 2023

View full text Add to dashboard Cite

The vibration signal acquired by a single sensor contains limited information and is easily interfered by noise signals, resulting in the inability to fully express the operating characteristics and state of a gearbox. To address this problem, our study proposes a gearbox fault diagnosis method based on multi-sensor deep spatiotemporal feature representation. This method utilizes two vibration sensors to obtain the vibration information of the gearbox. A fault diagnosis model (PCNN–GRU) combined with a parallel convolutional neural network (PCNN) and gated recurrent unit (GRU) was used to fuse the gearbox vibration information. The parallel convolutional neural network was used to extract the spatial information of the vibration signals collected by different position sensors, and the timing information was mined through the gated recurrent unit. The deep spatiotemporal features that fuse the multi-sensor spatial and temporal information were composed. The collected multi-sensor vibration signals were directly input into the PCNN–GRU model, and an end-to-end intelligent diagnosis of the gearbox faults was realized. Finally, through experimental verification, the accuracy rate of this model can reach up to 99.92%. Compared with other models, this model has a higher diagnostic accuracy and stability.

show abstract

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation

et al. 2023

View full text Add to dashboard Cite

show abstract

“…It is known that feature extraction and identification of the fault patterns are the two main steps to accomplish the fault diagnosis of rotating machinery [10]. Usually, the traditional feature extraction methods mainly consist of statistical feature extraction [11], signal analysis techniques such as Fourier transform [12], wavelet transform [13], empirical modal decomposition [14], and more. Then, the typical pattern recognition methods include support vector machines [15], extreme learning machines [16], artificial neural networks [17] and other improved approaches [18].…”

Section: Introductionmentioning

confidence: 99%

“…de/en/kat/main-research/datacenter/bearing-datacenter/data-sets-and-download/, accessed on 21 October 2023) [35]. The two datasets are conducted by some fault diagnosis methods but it is difficult to achieve high diagnostic accuracy [14,25,33,[36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network

Zheng,

Chen,

et al. 2023

Sensors

View full text Add to dashboard Cite

The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods.

show abstract

“…However, most of the work done in the field of gearbox fault diagnosis using deep learning methods is focused on the automatic extraction of features, effectively reducing the dimensions of the data, and improving the diagnostic performance only for single working conditions (Li et al, 2020), (Liu et al, 2018b). There is significant scope to work on the generalization capability of the gearbox fault diagnosis approaches available in the literature (Xia et al, 2021). For instance, fault identification in multiple gears using data recorded under inconsistent and uneven working conditions has not been much explored.…”

Section: Introductionmentioning

confidence: 99%

Gearbox fault diagnosis using improved feature representation and multitask learning

et al. 2022

View full text Add to dashboard Cite

A gearbox is a critical rotating component that is used to transmit torque from one shaft to another. This paper presents a data-driven gearbox fault diagnosis system in which the issue of variable working conditions namely uneven speed and the load of the machinery is addressed. Moreover, a mechanism is suggested that how an improved feature extraction process and data from multiple tasks can contribute to the overall performance of a fault diagnosis model. The variable working conditions make a gearbox fault diagnosis a challenging task. The performance of the existing algorithms in the literature deteriorates under variable working conditions. In this paper, a refined feature extraction technique and multitask learning are adopted to address this variability issue. The feature extraction step helps to explore unique fault signatures which are helpful to perform gearbox fault diagnosis under uneven speed and load conditions. Later, these extracted features are provided to a convolutional neural network (CNN) based multitask learning (MTL) network to identify the faults in the provided gearbox dataset. A comparison of the experimental results of the proposed model with that of several already published state-of-the-art diagnostic techniques suggests the superiority of the proposed model under uneven speed and load conditions. Therefore, based on the results the proposed approach can be used for gearbox fault diagnosis under uneven speed and load conditions.

show abstract

Dual-Ensemble Multi-Feedback Neural Network for Gearbox Fault Diagnosis

Cited by 14 publications

References 35 publications

Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation

Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network

Gearbox fault diagnosis using improved feature representation and multitask learning

Contact Info

Product

Resources

About