scholarly journals Weak Knock Characteristic Extraction of a Two-Stroke Spark Ignition UAV Engine Burning RP-3 Kerosene Fuel Based on Intrinsic Modal Functions Energy Method

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1148
Author(s):  
Jing Sheng ◽  
Rui Liu ◽  
Guoman Liu
Keyword(s):  
Energy Method ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Spark Ignition ◽  
Engine Block ◽  
Vibration Signals ◽  
Characteristic Extraction ◽  
Weight Value ◽  
Dominant Characteristic ◽  
Characteristic Component

To solve the problem of the weak knock characteristic extraction for a port-injected two-stoke spark ignition (SI) unmanned aerial vehicle (UAV) engine burning aviation kerosene fuel, which is also known as the Rocket Propellant 3 (RP-3), the Intrinsic modal Functions Energy (IMFE) method is proposed according to the orthogonality of the intrinsic modal functions (IMFs). In this method, engine block vibration signals of the two-stroke SI UAV engine are decomposed into a finite number of intrinsic modal function (IMF) components. Then, the energy weight value of each IMF component is calculated, and the IMF component with the largest energy weight value is selected as the dominant characteristic component. The knock characteristic frequency of the two-stroke SI UAV engine is obtained by analyzing the frequency spectrum of the dominant characteristic component. A simulation experiment is designed and the feasibility of the algorithm is verified. The engine block vibration signals of the two-stroke SI UAV engine at 5100 rpm and 5200 rpm were extracted by this method. The results showed that the knock characteristic frequencies of engine block vibration signals at 5100 rpm and 5200 rpm were 3.320 kHz and 3.125 kHz, respectively. The Wavelet Packet Energy method was used to extract the characteristics of the same engine block vibration signal at 5200 rpm, and the same result as the IMFE method is obtained, which verifies the effectiveness of the IMFE method.

Study on the Method of Vibration Signal De-Noising Using Wavelet Packet Based on QPSO

2014 ◽  
Vol 599-601 ◽  
pp. 1738-1744
Author(s):  
Kai Zhao ◽  
Ben Wei Li ◽  
Jing Chen
Keyword(s):  
Experimental Verification ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Threshold Function ◽  
Vibration Signals ◽  
Selection Of

Although many wavelet de-noising methods have been studied and proposed, the parameters of them are obtained by experience mostly, which makes the de-noising effect instable. To solve the issues, the solutions, such as the selection of wavelet function and threshold function, the calculation of decomposition levels, the optimal wavelet packet basis and the thresholds obtained based on QPSO, have been studied in this paper. Every parameter is obtained by calculation. This method is applied to the de-noising experiment of sine and vibration signals. Through the experimental verification, the effect of this de-noising method is obvious.

Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform

2018 ◽  
Vol 17 (02) ◽  
pp. 1850012 ◽  
Author(s):  
F. Sabbaghian-Bidgoli ◽  
J. Poshtan
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Hilbert Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
External Noise ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Hilbert Huang Transform

Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.

Evaluation of Diagnostic Performance for the Gear Box Considering the Measurement Position Effect With Wavelet Packet Transform

2009 ◽  
Author(s):  
Young-Sun Hong ◽  
Gil-Yong Lee ◽  
Young-Man Cho ◽  
Sung-Hoon Ahn ◽  
Chul-Ki Song
Keyword(s):  
Wavelet Packet ◽  
Position Effect ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Wavelet Packet Transform ◽  
Bevel Gear ◽  
Diagnostic Methods ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fault Features

There has been much research into monitoring techniques for mechanical systems to ensure stable production levels in modern industries. This is particularly true for the diagnostic monitoring of rotary machinery, because faults in this type of equipment appear frequently and quickly cause severe problems. Such diagnostic methods are often based on the analysis of vibration signals because they are directly related to physical faults. Even though the magnitude of vibration signals depends on the measurement position, the effect of measurement position is generally not considered. This paper describes an investigation of the effect of the measurement position on the fault features in vibration signals. The signals for normal and broken bevel gears were measured at the base, gearbox, and bevel gear, simultaneously, of a machine fault simulator (MFS). These vibration signals were compared to each other and used to estimate the classification efficiency of a diagnostic method using wavelet packet transform. From this experiment, the fault features are more prominently in the vibration signal from the measurement position of the bevel gear than from the base and gearbox. The results of this analysis will assist in selecting the appropriate measurement position in real industrial applications and precision diagnostics.

Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test

2019 ◽  
Vol 34 (07) ◽  
pp. 2059019 ◽  
Author(s):  
Hanxin Chen ◽  
Wenjian Huang ◽  
Jinmin Huang ◽  
Chenghao Cao ◽  
Liu Yang ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Hypothesis Test ◽  
Wavelet Packet ◽  
Principal Component ◽  
Vibration Signal ◽  
Component Analysis ◽  
Ratio Test ◽  
Vibration Signals ◽  
Time Domain Signal ◽  
Sequential Probability

A new method about the multi-fault condition monitoring of slurry pump based on principal component analysis (PCA) and sequential probability ratio test (SPRT) is proposed. The method identifies the condition of the slurry pump by analyzing the vibration signal. The experimental model is established using the normal impeller and the faulty impellers where the collected vibration signals were preprocessed using wavelet packet transform (WPT). The characteristic parameters of the vibration signals are extracted by time domain signal analysis and the dimension of data was reduced by PCA. The principal components with the largest contribution rate are chosen as the inputted signal to SPRT to assess the proposed algorithm. The new methodology is reasonable and practical for the multi-fault diagnosis of slurry pump.

Wavelets base Automatic Extraction Technology for Vibration Signal Characteristics of Hybrid Electric Vehicle Engine

2019 ◽  
Vol 11 (5) ◽  
Author(s):  
Dongfang Song ◽  
Guanfei Yin
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Domain Analysis ◽  
Extraction Technology ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Signal Characteristics

Traditional automatic characteristic extraction technology of engine vibration signals for hybrid electric vehicles (HEV) only focuses on the analysis of engine vibration signals in time domain and frequency domain. Single time domain analysis or single frequency domain analysis cannot accurately analyse the vibration signals, while both time domain analysis and frequency domain analysis have cross-analysis. As a result, the analysis results are repetitive and conflicting, which makes it difficult to extract the characteristics of engine vibration signals. The final extraction accuracy is not high and the extraction efficiency is low. For this reason, an automatic characteristic extraction technology of HEV engine vibration signal based on wavelet packet energy analysis is proposed. Firstly, the mechanical vibration of engine is converted into corresponding voltage and current signals by various sensors and then converted into digital signals by A/D (analog/digital) conditioner. The data of vibration signals are often mixed with various noises, which have a great impact on the final analysis of vibration signals. Data interception and pre-filtering are adopted. Wave, zero-mean, elimination of trend term and elimination of staggered points are used to pre-treat the vibration signals with mixed noise. Short-Time Fourier Transform (STFT) algorithm is introduced to analyse the pre-processed engine vibration signals and the fundamental properties of the non-stationary vibration signals in actual operation of the engine are obtained. The energy distribution of the analysed engine vibration signal is calculated by the wavelet packet energy analysis method. The calculated parameters of the energy distribution of the wavelet packet are taken as the characteristic parameters of the vibration signal. The vibration signal characteristics of the engine are automatically extracted. The experiment is carried out in the form of comparison with the traditional method. The experimental results show that the time-frequency joint analysis applied in the proposed technology can accurately analyse the essential characteristics of the engine vibration signal of HEV. The wavelet packet energy analysis method can ensure the extraction accuracy of the engine vibration signal characteristics.

HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings

2004 ◽  
Vol 127 (4) ◽  
pp. 299-306 ◽  
Author(s):  
Hasan Ocak ◽  
Kenneth A. Loparo
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Markov Modeling ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Hidden Markov Modeling ◽  
Rolling Element ◽  
Detection And Diagnosis

In this paper, we introduce a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. Features extracted from amplitude demodulated vibration signals from both normal and faulty bearings were used to train HMMs to represent various bearing conditions. The features were based on the reflection coefficients of the polynomial transfer function of an autoregressive model of the vibration signals. Faults can be detected online by monitoring the probabilities of the pretrained HMM for the normal case given the features extracted from the vibration signals. The new technique also allows for diagnosis of the type of bearing fault by selecting the HMM with the highest probability. The new scheme was also adapted to diagnose multiple bearing faults. In this adapted scheme, features were based on the selected node energies of a wavelet packet decomposition of the vibration signal. For each fault, a different set of nodes, which correlates with the fault, is chosen. Both schemes were tested with experimental data collected from an accelerometer measuring the vibration from the drive-end ball bearing of an induction motor (Reliance Electric 2 HP IQPreAlert) driven mechanical system and have proven to be very accurate.

Time-Frequency Characteristics of Vibration Signals Analysis for Large Instantaneous Impact Mill Rolling Based on Wavelet Packet

2013 ◽  
Vol 834-836 ◽  
pp. 1061-1064
Author(s):  
Qi Jun Xiao ◽  
Zhong Hui Luo
Keyword(s):  
Time Domain ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Signal Wave ◽  
Reconstruction Technique ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Impact Vibration ◽  
Signal Time

The wavelet packet decomposition and reconstruction technique is applied to time-frequency analysis of bite steel impact vibration signal by big rolling machine, it is obtained the bite steel impact signal wave packet. According to the size of the wavelet packet energy, it is reconstructed the signal of No.1 and No.2 wavelet packet. According to reconstruction of the signal time domain waveform and FFT spectrum chart, some meaningful conclusions are obtained.

Study on Energy Distribution of Single-Hole Blasting Vibration Signal Based on Wavelet Packet Transform

2013 ◽  
Vol 321-324 ◽  
pp. 1284-1289
Author(s):  
Dong Tao Li ◽  
Li Xin Xu ◽  
Yuan Yuan Sun ◽  
Qiu Rui Jia ◽  
Jing Long Yan
Keyword(s):  
Energy Distribution ◽  
Concentration Distribution ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Vibration Signal ◽  
Vibration Velocity ◽  
Blasting Vibration ◽  
Wavelet Packet Analysis ◽  
Vibration Signals ◽  
Single Hole

It is conducive to reducedamage of blasting vibration to realize energy distribution and attenuation lawof single-hole blasting vibration signal. With the measured single-holeblasting vibration velocity curves, used wavelet packet analysis technologywith high-resolution character, the law of energy distribution of single-holeblasting vibration signals in different frequency bands, and the effect ofblasting source and distance from the source on single-hole blasting vibrationsignal energy distribution were analysised. The results show that the energy ofsingle-hole blasting vibration signals attenuation very quickly in thefrequency domain concentration distribution in 0~100Hz; and distance from thesource has significant influence on energy distribution in the frequencydomain; The energy is mainly distributed in the low frequency band whendistance from the source is larger, which has guiding significance inmitigation of blast-induced vibrations.

scholarly journals Applying Energy-Equal Entropy of Wavelet Packet to Diagnose Circuit Breaker Faults

2014 ◽  
Vol 8 (1) ◽  
pp. 445-452
Author(s):  
Liu Mingliang ◽  
Wang Keqi ◽  
Sun Laijun ◽  
Zhang Jianfeng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Back Propagation ◽  
Vibration Signal ◽  
Characteristic Parameter ◽  
Vibration Signals

Aiming to better reflect features of machinery vibration signals of high-voltage (HV) circuit breaker (CB), a new method is proposed on the basis of energy-equal entropy of wavelet packet(WP). First of all, three-layer wavelet packet decomposes vibration signal, reconstructing 8 nodes of signals in the 3rd layer. Then, the vector is extracted with energy-equal entropy of reconstructed signals. At last, the simple back-propagation (BP) neural network for fault diagnosis contributes to classification of the characteristic parameter. This technology is the basis of a number of patents and patents pending, which is experimentally demonstrated by the significant improvement of diagnose faults.

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