scholarly journals A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2381 ◽  
Author(s):  
Shangjun Ma ◽  
Wei Cai ◽  
Wenkai Liu ◽  
Zhaowei Shang ◽  
Geng Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Learning Ability ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Network ◽  
Memory Space ◽  
Distribution Features

To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results.

Studies of filtering effect on fault diagnosis of spindle device in hoist

2021 ◽  
Vol 63 (6) ◽  
pp. 348-356
Author(s):  
Jun Gu ◽  
Yuxing Peng ◽  
Bobo Cao
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Correct Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Practical Significance ◽  
Support Vector ◽  
Mode Decomposition ◽  
Core Components ◽  
Filtering Effect

Spindle devices, which are among the core components of mine hoists, are typical rotor-bearing systems. Vibration-based fault diagnosis techniques are often used to help prevent mechanical failures of such systems. The fault vibration signals generally include pulse information reflecting fault type, independent vibration components caused by other non-faulty mechanical components, noise in the surrounding environment and so on. The reduction of noise in the vibration signal collected by the sensor is of practical significance for the correct diagnosis of subsequent rotating machinery faults. To solve this problem, a fault diagnosis method based on a smooth (SM) filtering algorithm combined with variational mode decomposition (VMD) and a support vector machine (SVM) is proposed. Wavelet transform (WT) and wavelet packet transform (WPT) methods are used to compare the noise reduction. The reliability and effectiveness of the method are verified by experiments on a hoist mechanical fault simulator. Experimental results show that the proposed method has high prediction accuracy and can provide a good practical reference for fault diagnosis of rotating machinery.

scholarly journals Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zong Yuan ◽  
Taotao Zhou ◽  
Jie Liu ◽  
Changhe Zhang ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Ranking And Selection ◽  
Fault Features ◽  
Feature Importance ◽  
Importance Ranking ◽  
Common Signal ◽  
Diagnosis Approach

The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.

scholarly journals Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Na Lu ◽  
Guangtao Zhang ◽  
Yuanchu Cheng ◽  
Diyi Chen
Keyword(s):  
Fault Diagnosis ◽  
Second Generation ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Support Vector ◽  
Signal Denoising ◽  
Denoising Method ◽  
Machine Method ◽  
Second Generation Wavelet ◽  
Machinery Fault Diagnosis

Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to improve the denoising effect of the vibration signal, an adaptive redundant second-generation wavelet (ARSGW) denoising method is proposed. In this method, a new index for denoising result evaluation (IDRE) is constructed first. Then, the maximum value of IDRE and the genetic algorithm are taken as the optimization objective and the optimization algorithm, respectively, to search for the optimal parameters of the ARSGW. The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. After that, features are extracted from the denoised signal and then input into the support vector machine method for fault recognition. The application result indicates that the proposed ARSGW denoising method can effectively improve the accuracy of rotating machinery fault diagnosis.

scholarly journals A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3109 ◽  
Author(s):  
Wenkai Liu ◽  
Ping Guo ◽  
Lian Ye
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Cell Structure ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Memory Space ◽  
Low Delay ◽  
Long Short Term Memory

Fault diagnosis is critical to ensuring the safety and reliable operation of rotating machinery systems. Long short-term memory networks (LSTM) have received a great deal of attention in this field. Most of the LSTM-based fault diagnosis methods have too many parameters and calculation, resulting in large memory occupancy and high calculation delay. Thus, this paper proposes a low-delay lightweight recurrent neural network (LLRNN) model for mechanical fault diagnosis, based on a special LSTM cell structure with a forget gate. The input vibration signal is segmented into several shorter sub-signals in order to shorten the length of the time sequence. Then, these sub-signals are sent into the network directly and converted into the final diagnostic results without any manual participation. Compared with some existing methods, our experiments illustrate that the proposed method has less memory space occupancy and lower computational delay while maintaining the same level of accuracy.

scholarly journals A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 128
Author(s):  
Chenbo Xi ◽  
Guangyou Yang ◽  
Lang Liu ◽  
Hongyuan Jiang ◽  
Xuehai Chen
Keyword(s):  
Fault Diagnosis ◽  
Single Channel ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Support Vector ◽  
Data Set ◽  
Rotary Machine ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Low Sensitivity

In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

The Fault Diagnosis Method for Roller Bearing with Harmonic Wavelet Packet and DT-SVM

2015 ◽  
Vol 724 ◽  
pp. 238-241
Author(s):  
Rui Pan ◽  
Tao Xu ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Support Vector ◽  
Time Frequency ◽  
Vibration Data ◽  
Electric Engineering ◽  
Harmonic Wavelet ◽  
Diagnosis Method

This paper studies the roller bearing fault diagnosis method with harmonic wavelet packet and Decision Tree-Support Vector Machine (DT-SVM). The harmonic wavelet packet possesses better performances for its box-shaped spectrum and unlimited subdivision compared with the conventional time-frequency feature exaction method. Firstly, the proposed method decomposes the roller bearing vibration signal with harmonic wavelet packet and extracts the feature energy with coefficients of each spectrum. After the feature energy is normalized, feature vector are available. Based on multi-level binary tree, this paper designs the multi-classification SVM model due to its superior nonlinear mapping capability. Three two-classifications are incorporated to diagnosis the roller bearing faults. Finally, the proposed method is illustrated with the vibration data from the roller bearing stand of electric engineering lab in case western reserve university. Experimental results illustrate the higher accuracy of the proposed method compared with conventional method.

scholarly journals Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Pengfei Li ◽  
Yongying Jiang ◽  
Jiawei Xiang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Hybrid Approach ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Support Vector ◽  
Gear Fault ◽  
Mechanical Faults ◽  
Energy Ratios ◽  
Robust To Noise

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples.

scholarly journals Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

2009 ◽  
Vol 16 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Junsheng Cheng ◽  
Dejie Yu ◽  
Jiashi Tang ◽  
Yu Yang
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Singular Values ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

Fault Diagnosis for Shaft System of Hydropower Unit Based on LS-SVM

2013 ◽  
Vol 325-326 ◽  
pp. 660-664
Author(s):  
Ye Zhou ◽  
Shu Tang ◽  
Luo Ping Pan ◽  
Ping Ping Li
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Least Square ◽  
Classification Model ◽  
Fault Classification ◽  
Support Vector ◽  
Shaft System ◽  
Condition Monitoring System ◽  
Energy Components

In this paper, shaft monitoring data in condition monitoring system of hydropower units was used to build the fault classification model based on the least square support vector machine (LS-SVM). By the wavelet packet signal decomposition for unit vibration signal, setting the signal energy components as the study sample, learning of fault diagnosis classifier was conducted, to achieve the diagnosis of common faults in shaft running of hydropower unit.

Sign in / Sign up

Export Citation Format

Share Document