Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Zhao, Chen; Sun, Jianliang; Lin, Shuilin; Peng, Ying

doi:10.3390/s21165494

Cited by 14 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under nominal, the SMOTE algorithm uses VDM to process the nominal attributes to calculate the distance, and under numeric, the SMOTE algorithm uses Euclidean distance to process the nominal attributes. Thus, the nominal option was chosen as the value of the parameter type [25]. Processing the dataset with the SMOTE algorithm helped to improve classification accuracy.…”

Section: Construction Of Tower Mechanical Failure Dataset Based On Sm...mentioning

confidence: 99%

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

Han

Wang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

In this paper, a classification recognition algorithm for tower mechanical faults is proposed, and a multiclass central segmentation hyperplane support vector machine (CSH-SVM) is proposed to improve the existing multiclass support vector machine for problems in which a certain sample satisfies multiple hyperplanes at the same time. The tilt angle change and wind direction data were extracted using the tilt sensors and anemometers attached to the tower, and the temperature and humidity sensors, as well as real-time rainfall and water accumulation information, were combined to construct a sample of the original dataset during the operation of the tower. The unbalanced samples were improved using the synthetic minority oversampling technique (SMOTE) algorithm to construct a balanced dataset suitable for machine learning and improve the prediction accuracy of machine learning. At the same time, the support vector machine hyperplane under the one-vs-all classification principle was additionally computed, and the new hyperplane was computed via the existing hyperplane not only to solve the classification problem of the transition area under the one-vs-all classification so that the samples located in this area no longer meet two hyperplane equations at the same time, but also to reduce the probability of incorrect classification to a certain extent. Through verification, CSH-SVM can classify 15 out of 77 misclassified samples into the correct category with slightly higher computational power than the traditional one-vs-all classification SVM, which can improve the classification prediction accuracy for unbalanced tower mechanical failure datasets and make an accurate judgment on the current state of the tower through the tower data as to when the tower may generate mechanical failure, thus reducing economic loss and personal safety threats.

show abstract

Section: Construction Of Tower Mechanical Failure Dataset Based On Sm...mentioning

confidence: 99%

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

Han

Wang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In [37], a method of using the CNN structure using 2D images for fault bearing diagnosis was proposed. In [38], an approach for fault bearing diagnosis using the 1D-CNN technique was proposed, and it added a preprocessing step in the diagnosis pipeline which calculates the frequency spectrum of vibration signals. Hao et al proposed an end-to-end solution for fault bearing diagnosis with one-dimensional convolutional long short-term memory (1D-CLSTM) networks [39].…”

Section: Convolutional Neural Network (Cnn)-based Bearing Fault Diagn...mentioning

confidence: 99%

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

2021

View full text Add to dashboard Cite

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented.

show abstract

“…Moreover, a “necessary” minute gap between the mill window and bearing housing has made the instability problem increasingly prominent. The aforementioned phenomenon affects the quality and precision of strip products, reduces the stability of the RBBH system, and increases the vibration amplitude of the strip mill as well as unpredictable accidents [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Characteristics of the RBBH System Based on Dynamics Model and Vibration Data Fusion

Lin

Sun

Chen

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

The roll-bearing-bearing housing (RBBH) system is one of the most common kernel structures used to determine strip mill stability and product surface quality in modern metallurgical machinery. To better understand dynamic characteristics of the RBBH system, this paper provides a nonlinear dynamic model and designs an engineering test platform on the RBBH system in the whole rolling process. First, a nonlinear dynamic model of the RBBH system supported by four-row rolling bearings under high speed and heavy load is established. Then, the method of combining Riccati transfer matrix and Newmark-β numerical integration is employed to solve nonlinear dynamic equations. After that, the engineering test platform is designed and assembled to capture and analyze the vibration signals of weathering steel (SPA-H) with finished thicknesses of 1.6 and 3.2 mm. Finally, the dynamic characteristics of the RBBH system are studied with the method of the dynamic model and vibration data fusion. The results show that the SPA-H with a finished thickness of 1.6 mm is rolled, the RBBH system fluctuates violently in both horizontal and vertical directions, and numerical results are highly consistent with experimental results in acceleration response, velocity response, and displacement response. In addition, the dynamic performance parameters of the four-row rolling bearing will also fluctuate greatly. Finally, there is significant interest to gain the benefits for the RBBH system design and mill stable rolling purposes.

show abstract

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Cited by 14 publications

References 36 publications

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Research on Dynamic Characteristics of the RBBH System Based on Dynamics Model and Vibration Data Fusion

Contact Info

Product

Resources

About