scholarly journals Study on the Method for Collecting Vibration Signals from Mill Shell Based on Measuring the Fill Level of Ball Mill

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Peng Huang ◽  
Minping Jia ◽  
Binglin Zhong
Keyword(s):  
High Sensitivity ◽  
Vibration Signal ◽  
Experimental Result ◽  
Vibration Signals ◽  
Vibration Data ◽  
Vibration Sensors ◽  
Coal Powder ◽  
Fill Level ◽  
Steel Balls ◽  
The Impact

At present the method for measuring the fill level which used the vibration signal of mill shell shows its advantage compared with other methods. However, this method is developed late, and the technique for collecting the vibration signal from mill shell is immature. In this paper, a novel method for collecting the vibration data from mill shell is proposed. Firstly, the layout scheme of vibration sensors on mill shell is given by analyzing the axial and circumferential movement of coal powder in roller. And a special data acquisition system is developed, which can acquire vibration data from different axial and circumferential positions on mill shell. Then the sampling frequency is obtained based on impact model and hierarchical model of steel balls. At the same time, the impact region on mill shell caused by steel balls is considered as the collecting region of vibration signals. Experimental result shows that vibration signals collected by the method proposed in this paper present a high sensitivity to the changes on fill level compared with vibration data of mill bearing, which provides a reliable basis for accurate measurement of the fill level.

scholarly journals Damped CAPES 2D Spectral Estimation for Real-Valued Vibration Signals

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Danhui Dan ◽  
Jiongxin Gong ◽  
Yiming Zhao
Keyword(s):  
Spectral Estimation ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
Angular Frequency ◽  
Frequency Resolution ◽  
Spectrum Estimation ◽  
Vibration Signals ◽  
Vibration Data ◽  
Decay Factor ◽  
Sine Term

We propose a 2D representation in the frequency-decay factor plane of an arbitrary real-world vibration signal. The signal is expressed as the sum of a decayed-attenuation sine term modulated by an amplitude function and a noise residue. We extend the combined approach of Capon estimation and amplitude and phase estimation (CAPES) to damped real vibration signals (DR-CAPES). In the proposed DR-CAPES method, the high-resolution amplitude and phase are estimated simultaneously for both angular frequency and decay factor grids. The performance of the proposed approach is tested numerically with noisy vibration data. Results show that the DR-CAPES method has an excellent frequency resolution, which helps to overcome difficulties in spectrum estimation when vibration modes are very close, and a small bias, which makes it suitable for obtaining accurate amplitude spectrums. The results also indicate that the proposed method can accurately estimate the amplitude spectrum with the use of averaging and denoising processes.

Research on Identification Method of Multi-Mixed Vibration Signal of Aeroengine

2009 ◽  
Vol 419-420 ◽  
pp. 801-804
Author(s):  
Xiang Yang Jin ◽  
Shi Sheng Zhong
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signal ◽  
Signal Separation ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Vibration Data ◽  
Monitoring And Diagnosis ◽  
Rotating Shafts

The effectiveness of separation and identification of mechanical signals vibrations is crucial to successful fault diagnosis in the condition monitoring and diagnosis of complex machines.Aeroengine vibration signals always include many complicated components, blind source separation (BSS) provides a efficient way to separate the independent component.In order to get the most effective algorithm of vibration signal separation,experiment has been done to acquire plenty of multi-mixed rotor vibration signals,three sets of vibration data generated from aeroengine rotating shafts were separated from the synthetic vibration signal. The results prove that blind source separation is effective and can be applied for vibration signal processing and fault diagnosis of aeroengine.

Means for providing information about danger to an employee equipped with an individual vibrating warning system

2019 ◽  
Vol 572 (5) ◽  
pp. 21-23
Author(s):  
Rafał Młyński ◽  
Emil Kozłowski ◽  
Leszek Morzyński
Keyword(s):  
Work Environment ◽  
Warning System ◽  
Vibration Signal ◽  
Original Research ◽  
Vibration Signals ◽  
Moving Vehicle ◽  
Research Results ◽  
Advantages And Disadvantages ◽  
Hearing Protectors ◽  
The Impact

The use of hearing protectors is a frequent way to avoid the impact of noise present in the work environment. However, it should be kept in mind that the use of hearing protectors, while reduces the threat created by noise, also diminishes the perception of sounds that are important for the safety of the employee. In such cases, employee’s safety can be improved/increased by using a system to detect the near presence of a moving vehicle. Such a system should be able to transmit information on detected danger to an employee using hearing protectors. The article discusses the possible ways of providing such information to employees using hearing protectors. The advantages and disadvantages of using acoustic, light and vibration signals for this purpose were considered. The authors also present original research results to confront the possibility of perceiving the vibration signal produced by two types of wearables.

The Blasting Test with Precise Delayed Time Interval and Wavelet Pocket Analysis for Vibration Signals’ Energy

2014 ◽  
Vol 1023 ◽  
pp. 198-204
Author(s):  
Li He ◽  
Dong Wang Zhong
Keyword(s):  
Total Energy ◽  
Vibration Signal ◽  
Propagation Distance ◽  
Vibration Velocity ◽  
Time Interval ◽  
Blasting Vibration ◽  
Vibration Signals ◽  
Slope Test ◽  
The Impact ◽  
Delayed Time

As a physical carrier, blasting vibration signal includes much information about blasting method, explosive charge structure and propagation medium. Based on the indoor concrete slope test with millisecond blasting and wavelet pocket analysis technology, the blasting seismic signal was analyzed in the features of energy distribution in order to control the blasting vibration hazard better. The attenuation law of the energy and the peak vibration velocity (PPV) with distance decreased were researched. The effects of delayed time interval on PPV and energy are investigated, and the paper have analyzed the weakening degree of energy and PPV of vibration signals when damping ditch exists, so was its effect on the distribution of energy. The conclusions show that: the impact is great about delayed time interval on the total energy of signals in millisecond blasting; the damping ditch made the predominant frequency for energy concentrate on the low frequency band, damping effect of the damping ditch reduced with the delay time interval increasing. When the propagation distance increased, the attenuation trend of the PPV and total energy slowed down gradually near blasting area. The PPV and energy are not necessarily meanwhile the maximum; the energy of the vibration signal is not only determined by the PPV.

Use of Neural Networks in Road Recognition by Vibration Data

2010 ◽  
Author(s):  
Giacomo Reggiani ◽  
Marco Cocconcelli ◽  
Riccardo Rubini ◽  
Francesco Lolli
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sensitivity Analysis ◽  
Vibration Signal ◽  
Accelerometer Sensor ◽  
Vibration Signals ◽  
Vibration Data ◽  
The Neural Network ◽  
Different Types ◽  
Control Set

This paper deals with road recognition by the use of neural networks on vibration signals. In particular, an accelerometer sensor is used and it acquires the vibrations transmitted in the contact between the tyres and the ground. Twelve different types of road have been tested at different speed of the car. Based on these data a neural network has been proposed to correlate a set of suitable characteristics of the vibration signal with the type of road. The effectiveness of the resulting neural network has been proved on a control set of data. Moreover the paper reports a sensitivity analysis of the neural network in order to minimize the number of inputs needed and to make it rugged.

scholarly journals A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

2021 ◽  
Vol 11 (11) ◽  
pp. 4996
Author(s):  
Gang Yao ◽  
Yunce Wang ◽  
Mohamed Benbouzid ◽  
Mourad Ait-Ahmed
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Optimization Techniques ◽  
Fault Classification ◽  
Vibration Signals ◽  
Kernel Parameter ◽  
Diagnosis Method ◽  
The Impact ◽  
Fault Feature Extraction

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.

scholarly journals Prediction and analysis of bearing vibration signal with a novel gray combination model

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402091924
Author(s):  
Qiang Yuan ◽  
Yu Sun ◽  
Rui-ping Zhou ◽  
Xiao-fei Wen ◽  
Liang-xiong Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Model ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Data Fitting ◽  
Layer By Layer ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Vibration Data ◽  
Bearing Vibration

Bearings are the core components of ship propulsion shafting, and effective prediction of their working condition is crucial for reliable operation of the shaft system. Shafting vibration signals can accurately represent the running condition of bearings. Therefore, in this article, we propose a new model that can reliably predict the vibration signal of bearings. The proposed method is a combination of a fuzzy-modified Markov model with gray error based on particle swarm optimization (PGFM (1,1)). First, particle swarm optimization was used to optimize and analyze the three related parameters in the gray model (GM (1,1)) that affect the data fitting accuracy, to improve the data fitting ability of GM (1,1) and form a GM (1,1) based on particle swarm optimization, which is called PGM (1,1). Second, considering that the influence of historical relative errors generated by data fitting on subsequent data prediction cannot be expressed quantitatively, the fuzzy mathematical theory was introduced to make fuzzy corrections to the historical errors. Finally, a Markov model is combined to predict the next development state of bearing vibration signals and form the PGFM (1,1). In this study, the traditional predictions of GM (1,1), PGM (1,1), and newly proposed PGFM (1,1) are carried out on the same set of bearing vibration data, to make up for the defects of the original model layer by layer and form a set of perfect forecast system models. The results show that the predictions of PGM (1,1) and PGFM (1,1) are more accurate and reliable than the original GM (1,1). Hence, they can be helpful in the design of practical engineering equipment.

scholarly journals Integrated Fault Evaluation Through Fusion Algorithm Supported by Kalman Filter

2020 ◽  
Vol 37 (6) ◽  
pp. 975-987
Author(s):  
Sadra Mousavi ◽  
Duygu Bayram Kara ◽  
Sahin Serhat Seker
Keyword(s):  
Kalman Filter ◽  
Source Identification ◽  
Fusion Algorithm ◽  
Process Noise ◽  
Vibration Signals ◽  
Vibration Data ◽  
Sensor Validation ◽  
The Impact ◽  
Fault Source

An Integrated Fault Evaluation (IFE) process is proposed in this study. It includes Sensor Validation (SV), Fault Detection (FD) and Fault Source Identification (FSI). The proposed algorithm employs data fusion algorithm enhanced by Kalman filter (KF). As the case study, vibration signals representing different aging states of an induction motor are used. The vibration data collected from two identical sensors with different measurement and process noises are achieved. Through the statistical and frequency domain characteristics, IFE is realized. The most prominent contribution of the study is the capability of distinction between the aging of the system and the process problems. For this aim, a rate representing the healthiness, which can discern the impact of the process noise and system aging, is calculated.

Detecting Vibration Problems in Machines and Structures Using Motion Capturing by Camera

2021 ◽  
Vol 21 (1) ◽  
pp. 38-49
Author(s):  
Husam Sattar Jasim ◽  
Jaafar Khalaf Ali
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Vibration Signal ◽  
Vibration Measurement ◽  
High Speed Camera ◽  
Full Field ◽  
Phase Angle Difference ◽  
Vibration Data ◽  
Vibration Sensors ◽  
The Time Domain

Vibration in rotating machines and structures is normally measured using accelerometers and other vibration sensors. For large machines and structures, the process of collecting vibration data is tedious and time-consuming due to the large number of points where vibration data must be measured. In this paper, a novel non-contact vibration measurement method has been introduced by using a high-speed camera as a vibration measurement device. This method has many advantages compared with the others. It has a low cost, easy to setup, and high automation. It also can be used for full-field measurement. Many tests have been accomplished to prove the validation of this method. The verification test has been accomplished by using the machinery faults simulator. It presented a reasonable validation that the operation deflection shapes (ODS) and the phase difference of any object can be successfully measured by using a high-speed camera. The mode shape tests have been accomplished by using the whirling of shaft apparatus device to extract the time domain, frequency domain, ODS, and phase differences for many points on the shaft at the first two critical speeds. The results proved that the high-speed camera can be used to detect the vibration signal in many different fault cases. It also proved that the high-speed camera can be used to detect the ODS and the phase angle difference. That gives the proposed method more robust and acceptance.

scholarly journals Experimental Wavelet Analysis of Rotor-Rub Vibration Signal

2019 ◽  
Vol 8 (12) ◽  
pp. 1756-1759
Keyword(s):  
Frequency Analysis ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Time Frequency Analysis ◽  
Signature Extraction ◽  
Time Frequency ◽  
Vibration Data ◽  
Vibration Sensors ◽  
Frequency Domains ◽  
Frequency Components

A common defective phenomenon in rotating machinery is rotor-casing rub that generates impacts when the rotor rubs against the stator. Vibration sensors and data analysis techniques are commonly used for fault signature extraction and mechanical systems diagnosis. In this paper, an experimental characterization of rotor-rub is made by time-frequency analysis by means of the wavelet transform. A rotor kit, equipped with a variable speed DC motor, an accelerometer and a data acquisition system are used to acquire the mechanical vibration data. Vibration signal in frequency and time-frequency domains are shown for no-rubbing, light, and severe rubbing cases. Results show that FFT is unable to report where in time particular components of rubbing appear. However, the time-frequency analysis is able to give location information in time to differentiate light from severe rubbing, and extract the main spectral components showing a spectrum rich in high frequency components, characteristic of this phenomenon.

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