A Data-Driven Fault Diagnosis Method using Modified Health Index and Deep Neural Networks of a Rolling Bearing

2021 ◽  
pp. 1-20
Author(s):  
Muyangzi Lin ◽  
Miyuan Shan ◽  
Jie Zhou ◽  
Yunjie Pan
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Maintenance Cost ◽  
Health Index ◽  
Diagnosis Accuracy ◽  
Fault Mode

Abstract To improve fault diagnosis accuracy, a data-driven fault diagnosis model based on the adjustment Mahalanobis-Taguchi system (AMTS) was proposed. This model can analyze and identify the characteristics of vibration signals by using degradation monitoring as the classifier to capture and recognize the faults of product more accurately. To achieve this goal, we firstly used the modified ensemble empirical mode decomposition (MEEMD) scalar index to capture the bearing condition; then, by using the key intrinsic mode function (IMF) extracted by AMTS as the input of classifier, the optimized properties of bearing is decomposed and extracted effectively. Next, in order to improve the accuracy of the fault diagnosis we tested different modes; employing the modified health index (MHI), which is designed to overcome the shortcomings of the proposed health index as a classifier in single fault mode, and the deep neural networks (DNN) as a classifier in multi-fault mode. To evaluate the effectiveness of our model, the Case Western Reserve University (CWRU) bearing data were used for verification. Results indicated a strong robustness with 99.16% and 1.09s, 99.86% and 6.61s fault diagnosis accuracy in different data modes respectively. Furthermore, we argue that this data-driven fault diagnosis obviously lowers the maintenance cost of complex systems by significantly reducing the inspection frequency and improves future safety and reliability.

scholarly journals Rolling Bearing Fault Diagnosis Using a Deep Convolutional Autoencoding Network and Improved Gustafson–Kessel Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yaochun Wu ◽  
Rongzhen Zhao ◽  
Wuyin Jin ◽  
Linfeng Deng ◽  
Tianjing He ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Clustering Algorithm ◽  
Rolling Bearing ◽  
Fault Identification ◽  
Layer By Layer ◽  
Bearing Fault Diagnosis ◽  
Novel Method ◽  
Low Dimensional

Deep learning (DL) has been successfully used in fault diagnosis. Training deep neural networks, such as convolutional neural networks (CNNs), require plenty of labeled samples. However, in mechanical fault diagnosis, labeled data are costly and time-consuming to collect. A novel method based on a deep convolutional autoencoding network (DCAEN) and adaptive nonparametric weighted-feature extraction Gustafson–Kessel (ANW-GK) clustering algorithm was developed for the fault diagnosis of bearings. First, the DCAEN that is pretrained layer by layer by unlabeled samples and fine-tuned by a few labeled samples is applied to learn representative features from the vibration signals. Then, the learned representative features are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the low-dimensional main features are obtained. Finally, the low-dimensional features are input ANW-GK clustering for fault identification. Two datasets were used to validate the effectiveness of the proposed method. The experimental results show that the proposed method can effectively diagnose different fault types with only a few labeled samples.

Deep neural networks-based rolling bearing fault diagnosis

2017 ◽  
Vol 75 ◽  
pp. 327-333 ◽  
Author(s):  
Zhiqiang Chen ◽  
Shengcai Deng ◽  
Xudong Chen ◽  
Chuan Li ◽  
René-Vinicio Sanchez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks

Solar Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 48-56
Author(s):  
Max Pargmann ◽  
Daniel Maldonado Quinto ◽  
Peter Schwarzbözl ◽  
Robert Pitz-Paal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Data Driven ◽  
State Prediction ◽  
Accuracy Data

Framework for TCAD augmented machine learning on multi- I–V characteristics using convolutional neural network and multiprocessing

2021 ◽  
Vol 42 (12) ◽  
pp. 124101
Author(s):  
Thomas Hirtz ◽  
Steyn Huurman ◽  
He Tian ◽  
Yi Yang ◽  
Tian-Ling Ren
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Information Technologies ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Sufficient Data ◽  
Learning Models ◽  
Simulation Tools ◽  
New Information

Abstract In a world where data is increasingly important for making breakthroughs, microelectronics is a field where data is sparse and hard to acquire. Only a few entities have the infrastructure that is required to automate the fabrication and testing of semiconductor devices. This infrastructure is crucial for generating sufficient data for the use of new information technologies. This situation generates a cleavage between most of the researchers and the industry. To address this issue, this paper will introduce a widely applicable approach for creating custom datasets using simulation tools and parallel computing. The multi-I–V curves that we obtained were processed simultaneously using convolutional neural networks, which gave us the ability to predict a full set of device characteristics with a single inference. We prove the potential of this approach through two concrete examples of useful deep learning models that were trained using the generated data. We believe that this work can act as a bridge between the state-of-the-art of data-driven methods and more classical semiconductor research, such as device engineering, yield engineering or process monitoring. Moreover, this research gives the opportunity to anybody to start experimenting with deep neural networks and machine learning in the field of microelectronics, without the need for expensive experimentation infrastructure.

scholarly journals An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

2014 ◽  
Vol 6 ◽  
pp. 676205 ◽  
Author(s):  
Meijiao Li ◽  
Huaqing Wang ◽  
Gang Tang ◽  
Hongfang Yuan ◽  
Yang Yang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Outer Race ◽  
Mode Decomposition ◽  
Acceleration Sensors

In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder

2019 ◽  
Vol 9 (13) ◽  
pp. 2743 ◽  
Author(s):  
Dai ◽  
Tang ◽  
Shao ◽  
Huang ◽  
Wang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Health Condition ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Diagnostic Model ◽  
Denoising Autoencoder ◽  
Essential Information ◽  
Mode Function

Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods.

Sign in / Sign up

Export Citation Format

Share Document