Application of Wavelet Analysis to Fault Diagnosis of Mechanical Equipment

2012 ◽  
Vol 590 ◽  
pp. 325-328
Author(s):  
Shan Zhen Xu ◽  
Cheng Wang
Keyword(s):  
Wavelet Analysis ◽  
Simulation Analysis ◽  
Wavelet Packet ◽  
Gear Transmission ◽  
Good Time ◽  
Mechanical Equipment ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Gear Fault ◽  
Adaptive Ability

Aiming at the characteristics of mechanical gear transmission, taking automobile main reducer as research object, this paper analyzes the gear transmission of failure mechanism and failure characteristics. According to the good time frequency character and adaptive ability of wavelet analysis, it proposes gear fault information extraction method based on wavelet packet analysis and have carried out simulation analysis. The results show that the method of wavelet packet analysis can effectively detect mutations in the signal part and noise to achieve the diagnosis of mechanical system failures.

scholarly journals Vehicle speed measurement by on-board acoustic signal processing

2018 ◽  
Vol 51 (5-6) ◽  
pp. 138-149 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Relative Motion ◽  
Wavelet Packet ◽  
Absolute Error ◽  
Frequency Resolution ◽  
Vehicle Speed ◽  
Air Conditioner ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Speed Sensor ◽  
Frequency Domain Methods

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

scholarly journals Flow Measurement by Wavelet Packet Analysis of Sound Emissions

2018 ◽  
Vol 51 (3-4) ◽  
pp. 104-112
Author(s):  
Hüseyin Göksu
Keyword(s):  
Flow Measurement ◽  
Acoustic Analysis ◽  
Wavelet Packet ◽  
Flow Speed ◽  
Frequency Resolution ◽  
Wavelet Packet Analysis ◽  
Resonance Effects ◽  
Time Frequency ◽  
Feature Vectors ◽  
Frequency Domain Methods

Fluid, when running through pipes, makes a complex sound emission whose parameters change nonlinearly with respect to flow speed. Especially, in household pipe systems, there may be spraying effects and resonance effects which make the emission more complex. We present a novel approach for predicting flow speed based on wavelet packet analysis of sound emissions rather than traditional time and frequency domain methods. Wavelet packet analysis, by providing arbitrary time–frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. Wavelet packet analysis subimages are further analyzed to obtain feature vectors of norm entropy. These feature vectors are fed into a multilayer perceptron for prediction. Prediction accuracy of 98.62%, with 3.99E−04 L/s mean absolute error and its corresponding 1.85% relative error is achieved. Time sensitivity is ±0.453 s and is open to improvement by varying window width. The result indicates that the proposed method is a good candidate for flow measurement by acoustic analysis.

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.

Wavelet Packet Analysis Based Feature Extraction of Vehicular Acoustic Signal

2011 ◽  
Vol 55-57 ◽  
pp. 1593-1598
Author(s):  
Xiao Xuan Qi ◽  
Jian Wei Ji ◽  
Xiao Wei Han ◽  
Zhong Hu Yuan
Keyword(s):  
Acoustic Signal ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Analysis ◽  
Moving Vehicle ◽  
Time Frequency ◽  
Vehicle Type ◽  
Feature Information ◽  
Vehicle Type Classification ◽  
Type Classification

In this paper, an approach based on wavelet packet analysis is proposed to deal with the problem that acoustic signal of moving vehicle is easily influenced by environmental noise in vehicle type classification. Wavelet packet analysis is applied to extract local and detail feature information of acoustic signal in the time-frequency domain. Firstly, raw acoustic signal is decomposed into different frequency bands by wavelet packet analysis, and then decomposition coefficients are reconstructed. The energy of every frequency band component is used to form the feature vector. Finally, vehicle type classification is implemented by RBF neural network on the basis of these feature vectors. Experimental results show that the proposed method is feasible and effective.

scholarly journals An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Wuqiang Liu ◽  
Xiaoqiang Yang ◽  
Shen Jinxing
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Good Time ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Nonlinear Dimensionality Reduction ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Fault Type ◽  
Complex Wavelet

The health condition of rolling bearings, as a widely used part in rotating machineries, directly influences the working efficiency of the equipment. Consequently, timely detection and judgment of the current working status of the bearing is the key to improving productivity. This paper proposes an integrated fault identification technology for rolling bearings, which contains two parts: the fault predetection and the fault recognition. In the part of fault predetection, the threshold based on amplitude-aware permutation entropy (AAPE) is defined to judge whether the bearing currently has a fault. If there is a fault in the bearing, the fault feature is adequately extracted using the feature extraction method combined with dual-tree complex wavelet packet transform (DTCWPT) and generalized composite multiscale amplitude-aware permutation entropy (GCMAAPE). Firstly, the method decomposes the fault vibration signal into a set of subband components through the DTCWPT with good time-frequency decomposing capability. Secondly, the GCMAAPE values of each subband component are computed to generate the initial candidate feature. Next, a low-dimensional feature sample is established using the t-distributed stochastic neighbor embedding (t-SNE) with good nonlinear dimensionality reduction performance to choose sensitive features from the initial high-dimensional features. Afterwards, the featured specimen representing fault information is fed into the deep belief network (DBN) model to judge the fault type. In the end, the superiority of the proposed solution is verified by analyzing the collected experimental data. Detection and classification experiments indicate that the proposed solution can not only accurately detect whether there is a fault but also effectively determine the fault type of the bearing. Besides, this solution can judge the different faults more accurately compared with other ordinary methods.

Intellectual Gear Fault Detection Based on Wavelet Time-Frequency Analysis

2013 ◽  
Vol 373-375 ◽  
pp. 762-769 ◽  
Author(s):  
Juan Li Zhou
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Gear System ◽  
Support Vector ◽  
Rotating Speed ◽  
Time Frequency ◽  
Fault Recognition ◽  
Vector Machines ◽  
Gear Fault

In this paper, wavelet packet transform and support vector machines are used to detect gear system faults. Testing signals were obtained by measuring the vibration signals of gear system at different rotating speed for different faults. Vibration feature signals were analyzed using wavelet de-noising. By using wavelet packet transform (WPT), signals were decomposed into different frequency bands. the fault detection is used for calculation of energy percents of every frequency. All these were used for fault recognition using Support vector machine (SVM). SVM and neural network transform results were compared. The research indicates that the de-noised signal is superior to the original one. When dealing with various signals, such as Multi-Faults, the diagnosis identification rates are over 92%. This method can be effectively used not only in engineering diagnosis of different faults of gear system, but also for other machinery fault style classification.

Gear Pitting Corrosion Fault Diagnosis Based on Local Mean Decomposition

2012 ◽  
Vol 562-564 ◽  
pp. 812-815 ◽  
Author(s):  
Ya Nong Chen ◽  
Tian He ◽  
Deng Hong Xiao ◽  
Hai Tao Cui
Keyword(s):  
Fault Diagnosis ◽  
Pitting Corrosion ◽  
Simulation Analysis ◽  
Analysis Method ◽  
Local Mean Decomposition ◽  
Time Frequency ◽  
Gear Fault ◽  
Modulation Signal ◽  
Adaptive Time ◽  
Local Mean

The local mean decomposition (LMD), a new adaptive time-frequency analysis method, is the research focus in the fault diagnosis field in recent years. In this paper, the LMD’s characteristics are obtained by processing multi-component frequency and amplitude modulation signal, which are usually used to describe the gear pitting corrosion fault signals. Base on the simulation analysis, LMD is presented to deal with the vibration signals of gear pitting corrosion fault, comparing with traditional method. The results show that the gear pitting corrosion defect can be diagnosed by LMD effectively, and LMD can eliminate the false composition effect, thus improving the accuracy of gear fault diagnosis.

Research of Computer-Aided Diagnosis about Pulmonary Interstitial Pathology Based on Wavelet Decomposition

2014 ◽  
Vol 519-520 ◽  
pp. 611-614
Author(s):  
Xu Cao ◽  
Hua Xun Zhang
Keyword(s):  
Wavelet Analysis ◽  
Wavelet Decomposition ◽  
Simulation Experiment ◽  
Computer Aided Diagnosis ◽  
Good Time ◽  
Time Frequency ◽  
Image Characteristics ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Denoised Image

This paper present a way of pulmonary interstitial pathology diagnosis of computer-aided diagnosis based on wavelet analysis. It is difficult to diagnosis qualitatively in pulmonary interstitial pathology because various lesions of the image analogous and the image interlap. The method based on good time-frequency characters is put. The effectiveness and accuracy of the means is verified through the simulation experiment of denoised image, image segmentation and image characteristics extraction. Along with the further research and application of the wavelet technique, it will have more space that use wavelet analysis in computer-aided diagnosis.

Petroleum Pipe Leakage Detection and Location Embeded in SCADA

2004 ◽  
Author(s):  
Likun Wang ◽  
Jian Li ◽  
Ke Peng ◽  
Shijiu Jin ◽  
Zhuang Li
Keyword(s):  
Wavelet Analysis ◽  
Data Exchange ◽  
Wavelet Packet ◽  
Economic Loss ◽  
Frequency Component ◽  
False Alarms ◽  
Leakage Detection ◽  
Wavelet Packet Analysis ◽  
Oil Pipeline ◽  
Scada System

With the increase of the age of the transport oil pipeline and the man-made destruction to pipeline, leaks are often found. The system for pipeline leakage detection and location must be established to find leakage and locate the leak positions to reduce serious environmental pollution and economic loss caused by leakage. The negative pressure wave method is an effective way to locate the leak position, because over 98 percent pipe leakage in China is paroxysmal. There is a SCADA (supervisory control and data acquisition) system to monitor operation for long transport petroleum pipe, but the function of leakage detection and location is not included in existing SCADA system in China. This paper used Dynamic Data Exchange (DDE) method to obtain pipe operation parameters such as pressure, flow rate, temperature, bump current, valve position and so on from the SCADA system. That takes full advantage of the abundant data collection function of the SCADA system to provide data for leakage detection and location. The wavelet packet analysis-based fault diagnosis method can directly use the change of parameters such as energy of frequency component to detect faults without system model. In the paper, a wavelet packet analysis-based characteristic extraction method is used to extract the characteristic information of leak pressure signals. The eigenvector indexes along with the parameters obtained from the SCADA system can be used to avoid false alarms. Wavelet analysis was used to locate leak positions accurately in this paper. Such a wavelet analysis-based leakage detection and location scheme embedded in the SCADA system has been successfully applied to a pipeline in PetroChina. Practical run demonstrated its well effect.

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