scholarly journals Fault diagnosis of synchronous hydraulic motor based on acoustic signals

2020 ◽  
Vol 12 (4) ◽  
pp. 168781402091610
Author(s):  
Jiaoyi Hou ◽  
Hongyu Sun ◽  
Aoyu Xu ◽  
Yongjun Gong ◽  
Dayong Ning
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Wavelet Packet ◽  
Pearson Correlation ◽  
Vibration Signal ◽  
Economic Loss ◽  
Acoustic Signals ◽  
Hydraulic Motor ◽  
Automatic Calculation ◽  
Hydraulic Motors

Synchronous hydraulic motors are used in high load conditions. Therefore, the failure of such motors must be promptly detected to avoid severe accidents and economic loss. The automation of signal processing and diagnostic processes in practical engineering applications can help improve engineering efficiency and reduce hazards. As a non-contact acquisition signal, an acoustic signal has easier acquisition than a vibration signal. This article proposes an automatic fault detection method for synchronous hydraulic motors, which uses acoustic signals. The proposed method includes the automatic calculation and pattern recognition of the parameters of fault feature vectors. The automatic calculation of the fault feature vector is based on the combination of wavelet packet energy and the Pearson correlation coefficient. Then, the nearest-neighbor classifier is used for fault diagnosis. This study verifies that the proposed method can effectively identify the normal state, gear wear, gear rust, and barrier block wear. This method provides a solution for the automatic fault diagnosis of synchronous hydraulic motors and other types of quasi-period 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.

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

2020 ◽  
pp. 107754632094971 ◽  
Author(s):  
Shoucong Xiong ◽  
Shuai He ◽  
Jianping Xuan ◽  
Qi Xia ◽  
Tielin Shi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Learning Algorithms ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Residual Network ◽  
Time Frequency ◽  
Correlation Information

Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.

Fault Diagnosis of Large Rotors Based on Wavelet Packet Neural Network

2014 ◽  
Vol 722 ◽  
pp. 363-366
Author(s):  
You Juan Zheng ◽  
Ping Liao ◽  
Cai Long Qin ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Test Bench ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Network Method ◽  
Spectrum Characteristic ◽  
Fault Information

Using wavelet packet neural network method which is consist of wavelet packet and BP neural network to diagnose large rotors by vibration signal .Firstly , according to the spectrum characteristic of large rotors’ common vibration fault ,using the improved wavelet packet method to compute the energy of the spectrum that can reflect the fault information .And then make the feature vector as the input to establish a model of improved wavelet packet neural network for fault diagnosis . Collect the data of five working conditions from the test bench , establish a improved wavelet packet neural network model, and then use the model to diagnose fault. The experimental results show that this method improves the accuracy obviously and calculate fast.

scholarly journals The Hybrid Method of VMD-PSR-SVD and Improved Binary PSO-KNN for Fault Diagnosis of Bearing

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Sheng-wei Fei
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Vibration Signal ◽  
Experimental Result ◽  
Binary Particle Swarm Optimization ◽  
K Nearest Neighbor ◽  
Mode Decomposition ◽  
Training Samples ◽  
Value Decomposition ◽  
Binary Pso

Fault diagnosis of bearing based on variational mode decomposition (VMD)-phase space reconstruction (PSR)-singular value decomposition (SVD) and improved binary particle swarm optimization (IBPSO)-K-nearest neighbor (KNN) which is abbreviated as VPS-IBPSOKNN is presented in this study, among which VMD-PSR-SVD (VPS) is presented to obtain the features of the bearing vibration signal (BVS), and IBPSO is presented to select the parameter K of KNN. In IBPSO, the calculation of the next position of each particle is improved to fit the evolution of the particles. The traditional KNN with different parameter K and trained by the training samples with the features based on VMD-SVD (VS-KNN) can be used to compare with the proposed VPS-IBPSOKNN method. The experimental result demonstrates that fault diagnosis ability of bearing of VPS-IBPSOKNN is better than that of VS-KNN, and it can be concluded that fault diagnosis of bearing based on VPS-IBPSOKNN is effective.

Vibration-Based Fault Diagnosis of Pump Using WPT

2008 ◽  
Author(s):  
Pan Hong ◽  
Zheng Yuan
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Accurate Method ◽  
Singularity Analysis ◽  
Good Time ◽  
Signal Denoising ◽  
Good Tool ◽  
Time Frequency ◽  
Diagnosis Method

A vibration-based fault diagnosis method of pump units based on wavelet packet transform (WPT) is proposed in this paper. Compared with Fourier transform (FT) and wavelet transform (WT), WPT can subdivide the whole time-frequency domain. It can perform signals with good time resolution at high frequency and vice versa. WPT is considered as a good tool to signal denoising, accounting for its perfect ability in decomposing and reconstructing signal and its characteristic of no redundancy and divulges after denoising. In addition, WPT modulus maximal coefficient provides a simple but accurate method in calculating the Lipschitz exponents, which is the measurement of signal singularity. According to the singularity analysis results of vibration signal, we can recognize the fault pattern of pump units. This paper makes a detail research on signal denoising and singularity analysis based on WPT. Taking the main shaft and thrust bearing vibration signal for example, the experimental results show that WPT is effectively in the fault diagnosis system of pump unit.

Fan Fault Feature Extraction Based on Wavelet Packet Transform

2014 ◽  
Vol 668-669 ◽  
pp. 999-1002
Author(s):  
Xin Li ◽  
Pan Feng Guo
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Research Topic ◽  
Signal Characteristic ◽  
Manufacturing Enterprises ◽  
Important Position ◽  
Fault Feature Extraction

Fan occupies the important position in many industry, it give rise to that fault diagnosis become the new hot research topic, also is the urgent demand of many manufacturing enterprises. This paper based on the theory of wavelet packet transform, selecting wavelet packet transform and energy spectrum to wavelet de-noising and fault feature extraction the fan vibration signal. And use the MATLAB get the fan vibration signal characteristic vector, lay the foundation for the fan fault diagnosis.

Shaft Vibration Signal Denoising Based on Wavelet Analysis

2013 ◽  
Vol 300-301 ◽  
pp. 1110-1113
Author(s):  
Tie Qiang Sun ◽  
Rong Liu ◽  
Zhi Qi Qiu
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wavelet Packet ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Axial Vibration ◽  
Denoising Method ◽  
Original Source ◽  
Separation Result

In actual , there exist inevitably a lot of interference from neighbor machine and noise from surrondings in mechanical vibration signal measured by sensor ,which is disadvantageous for condition monitoring and fault diagnosis. In order to eliminate the axial vibration signal in the noise, using Wavelet packet denoising method in this article, Emulating experiment s were carried out under the MATLAB software ,original signals adopted vibration impulsion signal produced by vice position of faulty bear. Separation result s confirm this method successfully ext ract original source ,efficiently removes noise.

Signal Processing by Energy Normalization Method Based on Wavelet Packet

2009 ◽  
Vol 413-414 ◽  
pp. 613-619
Author(s):  
Tao Chen ◽  
Xiao Li Xu ◽  
Shao Hong Wang
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Flue Gas ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Normalization Method ◽  
Stationary Vibration ◽  
Non Stationary Signal ◽  
Fault Feature Extraction ◽  
Normalization Process

Fault diagnosis is a key technology to ensure safe and reliable operation of large electromechanical equipments. Fault feature extraction is important in fault diagnosis process. For non-stationary vibration signal, energy normalization method based on wavelet packet is used to make signal processing with signals decomposed into different frequency bands to perform energy normalization process in corresponding frequency band so as to extract fault feature. Flue Gas Turbine is chosen as research object and analysis shows that energy normalization processing method based on wavelet packet can effectively extract non-stationary signal characteristic parameters and make effective nondestructive testing analysis of equipments condition.

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.

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