A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery

Jiang, Hongkai; Shao, Haidong; Chen, Xinxia; Huang, Jiayang

doi:10.3233/jifs-169530

Cited by 31 publications

(18 citation statements)

References 18 publications

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“…Sohaib and Kim [26] developed a fault diagnosis scheme, which can overcome fluctuations of the load using complex envelope spectra and stacked sparse auto-encoder based on deep neural networks. The traditional DBN affects certain mining features of the different states of rolling bearings under varying loads, but accuracy still needs improving [27]. The binary visible neurons can be replaced by Gaussian continuous-valued neurons to generate a Gaussian-Bernoulli DBN (GDBN), which is not limited by the binary constraint of the input node [28].…”

Section: Introductionmentioning

confidence: 99%

Method of state identification of rolling bearings based on deep domain adaptation under varying loads

Kang

Chen

Wang

et al. 2020

IET Science, Measurement & Technology

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Section: Introductionmentioning

confidence: 99%

Method of state identification of rolling bearings based on deep domain adaptation under varying loads

Kang

Chen

Wang

et al. 2020

IET Science, Measurement & Technology

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“…Deep learning method, as a new field of machine learning, can effectively solve the drawbacks of the traditional diagnosis methods by relying on its deep structure [21]. Deep learning model contains multiple hidden layers, which are used for extracting the deep features of the complex signals.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, DBN and CNN have attracted wide attention recently. The main attribution is that DBN consists of multiple RBMs and is trained by greedy learning layer by layer, which makes it more possible to learn complex nonlinear characteristics [21]. CNN has the characteristics of weight sharing and sparse connection, so that fewer parameters need to be optimized in the training process [23].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

Zou

Jiang

et al. 2019

IEEE Access

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The traditional intelligent diagnosis methods of rotating machinery generally require feature extraction of the raw signals in advance. However, it is a very time-consuming and laborious process for extracting the sensitive feature information to improve classification performance. Deep learning method, as a novel machine learning approach, can simultaneously achieve feature extraction and pattern classification. With the characteristics of Deep Belief Network (DBN) and one-dimensional Convolutional Neural Network (1D-CNN) (e.g. learning complex nonlinear, sparse connection and weight sharing), a precise diagnosis method based on the combination of DBN and 1D-CNN is proposed. Firstly, the DBN composed of three pre-trained restricted Boltzmann machines (RBMs) is constructed to achieve feature extraction and dimensionality reduction of the high-dimensional raw data. Secondly, the low-dimensional features extracted by DBN are fed into 1D-CNN for further extracting the abstract features. Finally, Soft-max classifier is employed to identify different operating conditions of rotating machinery. The superiority of the proposed method is validated by comparison with several state-of-the art fault diagnosis methods on two experimental cases. Meanwhile, the proposed method is tested in different background noises and on the imbalanced datasets. The results show that it has higher efficiency and accuracy than the state-of-the art fault diagnosis methods. INDEX TERMS Deep belief network (DBN), one-dimensional convolutional neural network (1D-CNN), rotating machinery, feature extraction, intelligent fault diagnosis.

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“…In addition, exponential moving average (EMA) technique was used to improve diagnostic accuracy of the constructed deep model. Jiang et al [ 28 ] proposed a feature fusion DBN method to diagnose rotating machinery fault. Moreover, the locality preserving projection (LPP) was used to fusion deep features to further improve the quality of the deep features.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Early Gear Pitting Fault Diagnosis Using Stacked SAE and GBRBM

et al. 2019

Sensors

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Research on data-driven fault diagnosis methods has received much attention in recent years. The deep belief network (DBN) is a commonly used deep learning method for fault diagnosis. In the past, when people used DBN to diagnose gear pitting faults, it was found that the diagnosis result was not good with continuous time domain vibration signals as direct inputs into DBN. Therefore, most researchers extracted features from time domain vibration signals as inputs into DBN. However, it is desirable to use raw vibration signals as direct inputs to achieve good fault diagnosis results. Therefore, this paper proposes a novel method by stacking spare autoencoder (SAE) and Gauss-Binary restricted Boltzmann machine (GBRBM) for early gear pitting faults diagnosis with raw vibration signals as direct inputs. The SAE layer is used to compress the raw vibration data and the GBRBM layer is used to effectively process continuous time domain vibration signals. Vibration signals of seven early gear pitting faults collected from a gear test rig are used to validate the proposed method. The validation results show that the proposed method maintains a good diagnosis performance under different working conditions and gives higher diagnosis accuracy compared to other traditional methods.

show abstract

A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery

Cited by 31 publications

References 18 publications

Method of state identification of rolling bearings based on deep domain adaptation under varying loads

Method of state identification of rolling bearings based on deep domain adaptation under varying loads

Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

A Novel Method for Early Gear Pitting Fault Diagnosis Using Stacked SAE and GBRBM

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