scholarly journals A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4247
Author(s):  
Seong-Min Jeong ◽  
Seokmoo Hong ◽  
Jong-Seok Oh
Keyword(s):  
Acoustic Emission ◽  
Kalman Filter ◽  
Noise Signal ◽  
High Frequency Component ◽  
Fracture Detection ◽  
Forming Process ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Voltage Signal ◽  
Ae Signal

In this study, an acoustic emission (AE) sensor was utilized to predict fractures that occur in a product during the sheet metal forming process. An AE activity was analyzed, presuming that AE occurs when plastic deformation and fracturing of metallic materials occur. For the analysis, a threshold voltage is set to distinguish the AE signal from the ripple voltage signal and noise. If the amplitude of the AE signal is small, it is difficult to distinguish the AE signal from the ripple voltage signal and the noise signal. Hence, there is a limitation in predicting fractures using the AE sensor. To overcome this limitation, the Kalman filter was used in this study to remove the ripple voltage signal and noise signal and then analyze the activity. However, it was difficult to filter out the ripple voltage signal using a conventional low-pass filter or Kalman filter because the ripple voltage signal is a high-frequency component governed by the switch-mode of the power supply. Therefore, a Kalman filter that has a low Kalman gain was designed to extract only the ripple voltage signal. Based on the KF-RV algorithm, the measured ripple voltage and noise signal were reduced by 97.3% on average. Subsequently, the AE signal was extracted appropriately using the difference between the measured value and the extracted ripple voltage signal. The activity of the extracted AE signal was analyzed using the ring-down count among various AE parameters to determine if there was a fracture in the test specimen.

The Human Controller as an Adaptive, Low Pass Filter

1967 ◽  
Vol 9 (2) ◽  
pp. 141-147 ◽  
Author(s):  
Gordon H. Robinson
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Manual Control ◽  
Future Research ◽  
Research Needs ◽  
Descriptive Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Independent Variables ◽  
Low Pass

Data is presented on the ability of a human controller to track a signal contaminated with noise. Signal frequencies and signal-to-noise ratio are the independent variables. An optimal, adaptive filter is presented for comparison. A descriptive model is derived based on known human characteristics in manual control. Future research needs are discussed.

Motion Estimation Using Point Cluster Method and Kalman Filter

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
M. Senesh ◽  
A. Wolf
Keyword(s):  
Kalman Filter ◽  
Rigid Body ◽  
Rigid Body Motion ◽  
Body Motion ◽  
Cluster Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
The Real ◽  
Low Pass ◽  
Instantaneous Frequencies

The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures—PCT, Kalman filter followed by PCT, and low pass filter followed by PCT—enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal instantaneous frequencies.

scholarly journals Acoustic Emission Wave Velocity Measurement of Asphalt Mixture by Arbitrary Wave Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8505
Author(s):  
Jianfeng Li ◽  
Huifang Liu ◽  
Wentao Wang ◽  
Kang Zhao ◽  
Zhoujing Ye ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Wave Velocity ◽  
Velocity Measurement ◽  
Measurement Method ◽  
Asphalt Mixture ◽  
Low Frequency ◽  
Noise Signal ◽  
New Wave ◽  
Ae Signal ◽  
First Arrival

The wave velocity of acoustic emission (AE) can reflect the properties of materials, the types of AE sources and the propagation characteristics of AE in materials. At the same time, the wave velocity of AE is also an important parameter in source location calculation by the time-difference method. In this paper, a new AE wave velocity measurement method, the arbitrary wave (AW) method, is proposed and designed to measure the AE wave velocity of an asphalt mixture. This method is compared with the pencil lead break (PLB) method and the automatic sensor test (AST) method. Through comparison and analysis, as a new wave velocity measurement method of AE, the AW method shows the following advantages: A continuous AE signal with small attenuation, no crosstalk and a fixed waveform can be obtained by the AW method, which is more advantageous to distinguish the first arrival time of the acoustic wave and calculate the wave velocity of AE more accurately; the AE signal measured by the AW method has the characteristics of a high frequency and large amplitude, which is easy to distinguish from the noise signal with the characteristics of a low frequency and small amplitude; and the dispersion of the AE wave velocity measured by the AW method is smaller, which is more suitable for the measurement of the AE wave velocity of an asphalt mixture.

A Breakage-Stop System for Linen Wet Spinning Frame

2014 ◽  
Vol 8 (1) ◽  
pp. 159-169
Author(s):  
Bo Jin ◽  
Lijun Zhao ◽  
Shiqiang Zhu
Keyword(s):  
Circuit Board ◽  
Wet Spinning ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Spun Yarn ◽  
Red Led ◽  
Spun Yarns ◽  
Gated Channel ◽  
Voltage Signal ◽  
Low Pass

A multi-way control system for spun yarn breakage detecting and tow stopping is presented for linen wet spinning frame upgrade in medium-sized businesses of southeastern China. The signals from periodic rotations of spun yarns are picked by piezoelectric sensors. After input protection and multiplexers, the weak voltage signal from gated channel is processed through links including voltage following, passive high-pass filter, active low-pass filter, in-phase and reversedphase proportional amplifying, precision full-wave rectification, retardation voltage comparison, etc.. After regulated at a zener diode link, the final electrical level is sent to LPC2132 processor which gives trigger signals to actuators when yarn breakage is detected. In the corresponding actuator channel, armature of electromagnet will be pushed off to stop tow feed and the red LED will be lightened to warn spinner. Circuit board units for signal amplifying, conditioning and actuator driving were designed for controlling 24 yarn channels simultaneously. The simulation result and field test verified the circuit accuracy and programmed effectiveness.

Comparison of Estimated Torques Using Low Pass Filter and Extended Kalman Filter for Induction Motor Drives

2015 ◽  
Vol 6 (1) ◽  
pp. 92
Author(s):  
Ibrahim Mohd Alsofyani ◽  
Nik Rumzi Nik Idris ◽  
Yahya A. Alamri ◽  
Tole Sutikno ◽  
Aree Wangsupphaphol ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Nonlinear Models ◽  
Motor Drives ◽  
Computational Time ◽  
Induction Motor Drives ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Calculation Process ◽  
Low Pass

<span lang="EN-US">Torque calculation process is one of the major concerns for controlling induction motors in industry, which requires very accurate state estimation of unmeasurable variables of nonlinear models. This can be solved if the variables used for torque calculation is accurately estimated.  This paper presents a torque calculation based on a voltage model represented with a low-pass filter (LPF), and an extended Kalman filter (EKF). The experimental results showed that the estimated torque at low speed based on EKF is more accurate in the expense of more complicated and larger computational time. </span>

scholarly journals Minimizing Noise in Sinusiodal Function Signal using Wavelet Transform

2019 ◽  
Vol 9 (2) ◽  
pp. 1254-1260
Keyword(s):  
Wavelet Transforms ◽  
Wavelet Coefficient ◽  
Noise Signal ◽  
Filter Method ◽  
Fast Fourier Transformation ◽  
Original Signal ◽  
Wavelet Coefficients ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Data Signal

Resistances that occur in retrieving and processing signal is caused by the interference (noise) on the data signal measurement results. The resistance will raise uncertainties in determining the value of the frequency. This is due to the signal which is mixed with the noise in the original signal. In general, the process of signal analysis uses Fast Fourier Transformation (FFT). However, by using FFT in analyzing and reconstructing there are still doubts in determining the real frequency due to the still visible noise in the signal. In this study the signal function used is a sinusiodal function, Y = 2 sinπf1 t + 2 sin πf2 t, with a given noise value of 2 DB. The specified frequency value of f1 and f2 equal to 0.25 Hz and 5 Hz, respectively. This research proposed wavelet transforms to analyze and in filtering original signal with noise. By using the transformation wavelet, signal with noise filtered with the high pass and low pass filter method and also using the Haar wavelet function in analyzing. Once the signal is decomposed using wavelet transformation, the wavelet coefficients value will be obtained. The wavelet coefficient values will then threshold within a range of 5-50%. The purposed in determining the treshold value is to reduce the signal data identified as a noise signal data. If the value of wavelet coefficient below the treshold percentage value multiplied by the maximum wavelet coefficient, it is identified as a noise signal data, and the value of coefficient wavelet will be zero. The wavelet coefficient will then be reconstructed in order to obtain the data signal with the new sinusoidal function. In determining the value of the reconstructed frequency signal, the Fast Fourier Transform (FTT) method is used. The results of the study is signals with noise can be analyzed and filtered using wavelet transforms, by changing the signal into wavelet coefficients. Furthermore, the threshold of 5% is capable in reducing of noise in signal so that the graph of frequency and amplitude showed a clearer value of frequency.

scholarly journals State estimation for dynamic weighing using Kalman filter

2021 ◽  
Author(s):  
◽  
Sunethra Pitawala
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
High Speed ◽  
Sensor Signal ◽  
Pass Filter ◽  
Diverse Range ◽  
Low Pass Filter ◽  
Low Pass ◽  
Using Data ◽  
Dynamic Weighing

<p>Dynamic weighing has become an essential requirement in a diverse range of industries. Dynamic weighing is different from static weighing in that static weighing involves determining the weight while the product being weighed is stationary whereas dynamic weighing weighs the products while they are moving. Force sensors are commonly used in these weighing systems. In static weighing, the weighed object is placed stationary on the platform and the steady state of the sensor signal is used to assess the weight. However, in dynamic weighing the sensor signal may not reach the steady state during the brief time of weighing, hence the weight is assessed for example, by averaging the tail end of the signal after it has been through a low-pass filter. The resulting mass estimates can be inaccurate for faster heavier items. It is useful to consider better ways of estimating the true weight, in high speed weighing applications.  The proposed method is to employ the 1-D Kalman filter algorithm to estimate the optimal state of the signal. The improved steady state signal is then used in weight estimation. The proposed method has been tested using data collected from a loadcell when different masses pass over the loadcell. The results show a significant improvement in the filtered signal quality which is then used to improve the weight assessment.</p>

In-Process Tool Fracture Detection

1984 ◽  
Vol 106 (2) ◽  
pp. 111-118 ◽  
Author(s):  
M. S. Lan ◽  
D. A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Fracture Surface ◽  
Signal Analysis ◽  
Cutting Tool ◽  
Fracture Detection ◽  
Acoustic Emission Energy ◽  
Emission Energy ◽  
Ae Signal ◽  
The Relationship ◽  
Carbide Insert

This paper investigates the feasibility of using acoustic emission (AE) signal analysis for the detection of the breakage as well as the chipping of a cutting tool during machining. Experiments were conducted on a lathe using conventional carbide insert tools under realistic cutting conditions. The tangential and feed forces were also measured for comparisons. The sensitivities of the AE signal and cutting forces to insert chipping and fracture are illustrated. The relationship between acoustic emission energy and the combined effect of fracture surface area and cutting load is discussed.

Application of Kalman Filtering and Smoothing for Airborne Gravimetry

2014 ◽  
Vol 548-549 ◽  
pp. 1192-1195
Author(s):  
Wei Zheng ◽  
Gui Bin Zhang ◽  
Rui Li
Keyword(s):  
Kalman Filter ◽  
Gravity Anomaly ◽  
Kalman Filtering ◽  
Accurate Result ◽  
System Model ◽  
Noise Filtering ◽  
Airborne Gravimetry ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Due to the interference of noise, filtering technology is applied to achieve gravity anomaly for airborne gravimetry. Kalman filtering and smoothing are discussed and implemented for data processing of airborne gravimetry in this paper. Firstly, the algorithms of Kalman filtering and smoothing are introduced. Then, the system model for solving the gravity anomaly is established which is based on the dynamic equation and the hardware design equations. Finally, the result of Kalman filtering and smoothing would be compared with digital FIR low pass filter, and it is proved that Kalman filter and smoother could obtain more accurate result than FIR low pass filter as that the solving error of Kalman filter and smoother is improved within 1 mGal compared with the theory standard obtained by GT-1A software.

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