scholarly journals Construction of a Sensitive and Speed Invariant Gearbox Fault Diagnosis Model Using an Incorporated Utilizing Adaptive Noise Control and a Stacked Sparse Autoencoder-Based Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 18
Author(s):  
Cong Dai Nguyen ◽  
Alexander E. Prosvirin ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Noise Control ◽  
Vibration Characteristics ◽  
Vibration Signals ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Diagnosis Model ◽  
Adaptive Noise

Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration characteristics. Such characteristics are used for early fault detection to guarantee the enhanced safety of complex systems and their cost-effective operation. There exist many fault diagnosis models that have been developed for classifying various fault types in gearboxes. However, the classification results of the conventional fault classification models degrade when they are applied to gearbox systems with multi-level tooth cut gear (MTCG) faults operating under variable shaft speeds. These conditions cause difficulty in discriminating the gear fault types. Due to the improved computational capabilities of modern systems, the application of deep neural networks (DNNs) is getting popular in a variety of research fields, such as image and natural language processing. DNNs are capable of improving the classification results even when addressing complex problems such as diagnosing gearbox MTCG faults. In this research, an adaptive noise control (ANC) and a stacked sparse autoencoder–based deep neural network (SSA-DNN) are used to construct a sensitive fault diagnosis model that can diagnose a gearbox system with MTCG fault types under varying shaft rotation speeds, despite its complicatedness. An ANC is applied to gear vibration characteristics to remove a significant level of noise along the frequency spectrum of vibration signals to fix the most fault-informative components of each fault case. Next, the autoencoder learns the gear faults characteristic features from these fault-informative components to separate the fault types considered in this study. Furthermore, the implementation of the SSA-DNN is substituted for feature extraction, feature selection, and the classification processes in traditional fault diagnosis schemes by high-performance unity. The experimental results show that the proposed model outperforms conventional methodologies with higher classification accuracy.

Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network

2018 ◽  
Vol 311 ◽  
pp. 1-10 ◽  
Author(s):  
Lin Xu ◽  
Maoyong Cao ◽  
Baoye Song ◽  
Jiansheng Zhang ◽  
Yurong Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Open Circuit ◽  
Sparse Autoencoder ◽  
Circuit Fault Diagnosis ◽  
Power Rectifier

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

2019 ◽  
Vol 234 (1) ◽  
pp. 168-182 ◽  
Author(s):  
Jialin Li ◽  
Xueyi Li ◽  
David He ◽  
Yongzhi Qu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Deep Neural Network ◽  
Domain Adaptation ◽  
Diagnostic Model ◽  
Target Domain ◽  
Vibration Signals ◽  
Learning Network

In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

scholarly journals Rolling bearings fault diagnosis and classification based on optimal wavelet subband selection and deep neural network

2021 ◽  
Author(s):  
Rakesh Kumar Jha ◽  
Preety D Swami
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Vital Role ◽  
Fault Classification ◽  
Vibration Signals ◽  
Softmax Classifier ◽  
Practical Applications ◽  
Time Frequency ◽  
Diagnosis And Classification

Abstract Time-frequency analysis plays a vital role in fault diagnosis of nonstationary vibration signals acquired from mechanical systems. However, the practical applications face the challenges of continuous variation in speed and load. Apart from this, the disturbances introduced by noise are inevitable. This paper aims to develop a robust method for fault identification in bearings under varying speed, load and noisy conditions. An Optimal Wavelet Subband Deep Neural Network (OWS-DNN) technique is proposed that automatically extracts features from an optimal wavelet subband selected on the basis of Shannon entropy. After denoising the optimal subband, the optimal subbands are dimensionally reduced by the encoder section of an autoencoder. The output of the encoder can be considered as data features. Finally, softmax classifier is employed to classify the encoder output. The vibration signals were recorded on a machinery fault simulator setup for various combinations of speed and load for healthy and faulty bearings. The signals were subjected to various noise levels and the deep neural network was trained. The achieved experimental results reveal high accuracy in fault classification as compared to other techniques under comparison.

scholarly journals Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Changfan Zhang ◽  
Xiang Cheng ◽  
Jianhua Liu ◽  
Jing He ◽  
Guangwei Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Single Layer ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Sparse Autoencoder ◽  
Single Layer Network ◽  
Network Studies ◽  
Fine Tune

The model is difficult to establish because the principle of the locomotive adhesion process is complex. This paper presents a data-driven adhesion status fault diagnosis method based on deep learning theory. The adhesion coefficient and creep speed of a locomotive constitute the characteristic vector. The sparse autoencoder unsupervised learning network studies the input vector, and the single-layer network is superimposed to form a deep neural network. Finally, a small amount of labeled data is used to fine-tune training the entire deep neural network, and the locomotive adhesion state fault diagnosis model is established. Experimental results show that the proposed method can achieve a 99.3% locomotive adhesion state diagnosis accuracy and satisfy actual engineering monitoring requirements.

Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network

2017 ◽  
Vol 13 (7) ◽  
pp. 1336-1344 ◽  
Author(s):  
Yan-Bin Wang ◽  
Zhu-Hong You ◽  
Xiao Li ◽  
Tong-Hai Jiang ◽  
Xing Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Deep Neural Network ◽  
Protein Sequences ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
P Protein ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Protein–protein interactions (PPIs) play an important role in most of the biological processes.

The Engine Fault Diagnosis with RBFNN Based on AIC

2012 ◽  
Vol 468-471 ◽  
pp. 1066-1069
Author(s):  
Qiang Huang ◽  
Xiao Zhuo Ouyang ◽  
Cheng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Rbf Neural Network ◽  
Information Criterion ◽  
Vibration Signals ◽  
The Neural Network ◽  
Rotary Machines ◽  
Diagnosis Method ◽  
Diagnosis Model

In this paper, an engine diagnosis method with high precision and quickly response is proposed. Firstly, the Akaike Information Criterion (AIC) is used to improve the performance of the neural network to build the fault diagnosis model. Then the vibration signals are analyzed to estimate the states of the diesel engine. Finally, the five states of diesel engine are set to validate the veracity of diagnosis method. According to experiment and simulation researches, it indicates that the diagnosis method with RBF neural network based on AIC is effective. The veracity of identification is 100% to the single fault. It is a valuable reference to the vibration diagnosis for other complex rotary machines.

scholarly journals Translation Invariance-Based Deep Learning for Rotating Machinery Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Wenliao Du ◽  
Shuangyuan Wang ◽  
Xiaoyun Gong ◽  
Hongchao Wang ◽  
Xingyan Yao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Domain Knowledge ◽  
Translation Invariance ◽  
Complex Wavelet Transform ◽  
Translational Invariance ◽  
Vibration Signals ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Discriminative feature extraction is a challenge for data-driven fault diagnosis. Although deep learning algorithms can automatically learn a good set of features without manual intervention, the lack of domain knowledge greatly limits the performance improvement, especially for nonstationary and nonlinear signals. This paper develops a multiscale information fusion-based stacked sparse autoencoder fault diagnosis method. The autoencoder takes advantage of the multiscale normalized frequency spectrum information obtained by dual-tree complex wavelet transform as input. Accordingly, the multiscale normalized features guarantee the translational invariance for signal characteristics, and the stacked sparse autoencoder benefits the unsupervised feature learning and ensures accurate and stable diagnosis performance. The developed method is performed on motor bearing vibration signals and worm gearbox vibration signals, respectively. The results confirm that the developed method can accommodate changing working conditions, be free of manual feature extraction, and perform better than the existing intelligent diagnosis methods.

scholarly journals Deep Neural Network Hardware Implementation Based on Stacked Sparse Autoencoder

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 40674-40694 ◽  
Author(s):  
Maria G. F. Coutinho ◽  
Matheus F. Torquato ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Hardware Implementation ◽  
Neural Network Hardware ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder
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