The Application of the Modified Sage-Husa Adaptive Kalman Filter in the Excitation Force Identification of Under-Chassis Active Equipment for Railway Vehicles

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Jiangxue Chen ◽  
Jinsong Zhou ◽  
Dao Gong ◽  
Wenjing Sun ◽  
Yu Sun ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Experimental Result ◽  
Engineering Practice ◽  
Adaptive Kalman Filter ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Force Identification ◽  
Railway Vehicles ◽  
Noise Characteristics ◽  
Excitation Force

Abstract Excitation force of under-chassis active equipment of railway vehicles has a significant impact on the floor vibration of the car body. In order to improve the accuracy of the excitation force identification of active equipment in engineering practice, a new excitation force identification method was proposed by applying modified Sage-Husa adaptive Kalman filter (MSHAKF). First, the advantages of the MSHAKF over conventional Kalman filter (CKF) are introduced. Simulation shows that the MSHAKF has excellent exactness and robustness for active equipment excitation force identification. Finally, a test device for identifying excitation force was established. The infinite impulse response (IIR) low-pass filter is designed by using the bilinear transformation method to eliminate the identification error caused by the frequency multiplication components in the response signal. The experimental result shows that the proposed method is very effective in engineering practice without mastering the noise characteristics of the system.

scholarly journals Excitation Force Identification of Multiple Devices in Railway Vehicles Based on Modal Superposition Method and Kalman Filter

2021 ◽  
Author(s):  
Jiangxue Chen ◽  
Jinsong Zhou ◽  
Dao Gong
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Superposition Method ◽  
Force Identification ◽  
Railway Vehicles ◽  
Active Devices ◽  
Modal Superposition ◽  
Modal Superposition Method ◽  
Multiple Devices ◽  
Excitation Force

Abstract The excitation force identification of multiple devices in railway vehicles is studied. The vertical dynamic coupling model between the flexible car body of high-speed train and the under-chassis active device with excitation itself is established based on the modal superposition method. The excitation force from device is identified based on Kalman filter. A modal order selection method is developed for improving the identification accuracy based on tolerance index. The identification effects of single and multiple active devices including single-frequency steady-state, multi-frequency steady-state, impact, sawtooth wave and square wave excitation forces are analyzed. An error limit range of 5% is defined to evaluate the identification results. The results show that the method is suitable for the identification of various steady-state and transient-state excitation forces, and the identification results of excitation forces of single and multiple active devices have good accuracy.

scholarly journals A Four-Channel Low-Noise Readout IC for Air Flow Measurement Using Hot Wire Anemometer in 0.18 μm CMOS Technology

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4694
Author(s):  
Kyeongsik Nam ◽  
Hyungseup Kim ◽  
Yongsu Kwon ◽  
Gyuri Choi ◽  
Taeyup Kim ◽  
...  
Keyword(s):  
Supply Voltage ◽  
Air Flow ◽  
Low Noise ◽  
Cmos Process ◽  
Significant Information ◽  
Flow Measurements ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Op Amp ◽  
Noise Characteristics

Air flow measurements provide significant information required for understanding the characteristics of insect movement. This study proposes a four-channel low-noise readout integrated circuit (IC) in order to measure air flow (air velocity), which can be beneficial to insect biomimetic robot systems that have been studied recently. Instrumentation amplifiers (IAs) with low-noise characteristics in readout ICs are essential because the air flow of an insect’s movement, which is electrically converted using a microelectromechanical systems (MEMS) sensor, generally produces a small signal. The fundamental architecture employed in the readout IC is a three op amp IA, and it accomplishes low-noise characteristics by chopping. Moreover, the readout IC has a four-channel input structure and implements an automatic offset calibration loop (AOCL) for input offset correction. The AOCL based on the binary search logic adjusts the output offset by controlling the input voltage bias generated by the R-2R digital-to-analog converter (DAC). The electrically converted air flow signal is amplified using a three op amp IA, which is passed through a low-pass filter (LPF) for ripple rejection that is generated by chopping, and converted to a digital code by a 12-bit successive approximation register (SAR) analog-to-digital converter (ADC). Furthermore, the readout IC contains a low-dropout (LDO) regulator that enables the supply voltage to drive digital circuits, and a serial peripheral interface (SPI) for digital communication. The readout IC is designed with a 0.18 μm CMOS process and the current consumption is 1.886 mA at 3.3 V supply voltage. The IC has an active area of 6.78 mm2 and input-referred noise (IRN) characteristics of 95.4 nV/√Hz at 1 Hz.

scholarly journals Noise Modeling and Simulation of Giant Magnetic Impedance (GMI) Magnetic Sensor

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 960
Author(s):  
Fang Jin ◽  
Xin Tu ◽  
JinChao Wang ◽  
Biao Yang ◽  
KaiFeng Dong ◽  
...  
Keyword(s):  
Modeling And Simulation ◽  
Noise Spectrum ◽  
Low Noise ◽  
Noise Model ◽  
Magnetic Noise ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Modeling ◽  
Noise Characteristics ◽  
Spectrum Curve

The detection resolution of a giant magneto-impedance (GMI) sensor is mainly limited by its equivalent input magnetic noise. The noise characteristics of a GMI sensor are evaluated by noise modeling and simulation, which can further optimize the circuit design. This paper first analyzes the noise source of the GMI sensor. It discusses the noise model of the circuit, the output sensitivity model and the modeling process of equivalent input magnetic noise. The noise characteristics of three modules that have the greatest impact on the output noise are then simulated. Finally, the simulation results are verified by experiments. By comparing the simulated noise spectrum curve and the experimental noise spectrum curve, it is demonstrated that the preamplifier and the multiplier contribute the most to the output white noise, and the low-pass filter plays a major role in the output 1/f noise. These modules should be given priority in the optimization of the noise of the conditioning circuit. The above results provide technical support for the practical application of low-noise GMI magnetometers.

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.

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 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>

scholarly journals Real-time digital signal recovery for a low-pass transfer function system with multiple complex poles

2018 ◽  
Author(s):  
Jhinhwan Lee
Keyword(s):  
Transfer Function ◽  
Real Time ◽  
Digital Signal ◽  
Signal Recovery ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Recovery Method ◽  
Noise Characteristics ◽  
Resonance Frequencies ◽  
Low Pass

In order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with linear low-pass transfer characteristics with multiple complex poles, a general digital-signal-processing (DSP)-based method of real-time recovery of the original source waveform from the distorted output waveform is proposed. From the convolution kernel representation of a multiple-pole low-pass transfer function with an arbitrary denominator polynomial with real valued coefficients, it is shown that the source waveform can be accurately recovered in real time using a particular moving average algorithm with real-valued DSP computations only, even though some or all of the poles are complex. The proposed digital signal recovery method is DC-accurate and unaffected by initial conditions, transient signals, and resonant amplitude enhancement. The noise characteristics of the data recovery shows inverse of the low-pass filter characteristics. This method can be applied to most sensors and amplifiers operating close to their frequency response limits or around their resonance frequencies to accurately deconvolute the multiple-pole characteristics and to improve the overall performances of data acquisition systems and digital feedback control systems.

LEAST SQUARES SMOOTHING OPERATORS

Geophysics ◽  
1970 ◽  
Vol 35 (6) ◽  
pp. 1005-1019 ◽  
Author(s):  
Lawrence C. Wood ◽  
Sidney N. Hockens
Keyword(s):  
Least Squares ◽  
Unit Circle ◽  
Complex Conjugate ◽  
Engineering Practice ◽  
Positive Real ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Real Zeros ◽  
Conventional Procedure ◽  
Low Pass

Smoothing data to extract desired trends has been standard scientific and engineering practice for many years. Use of polynomials in a least squares sense to accomplish this end has also been conventional procedure, and it is well known that smoothing acts as a low‐pass filter. However, detailed analysis of filtering behavior is lacking in the literature and should be useful to geological and geophysical data processors. This paper has two objectives: to review least squares polynomial smoothing and to discuss some z‐transform properties of the convolution operator that implements the smoothing. These operators have real, symmetrical coefficients that lead to z‐polynomials having roots lying in unique patterns. Zeros occur in complex conjugate pairs that are also inverse points with respect to the unit circle. Polynomials of order 2M and 2M+1 produce identical operators; thus, no differences exist in smoothing between polynomials of order 0 or 1, 2 or 3, 4 or 5, . . , 2M or 2M+1. A result of fitting a polynomial of order 2M to n+1 data points in a least squares sense is that exactly n−2M roots lie on the unit circle, whereas 2M zeros have magnitudes other than unity. Of the 2M roots lying off the circle, polynomials of orders, 2, 6, 10, 14… have exactly two positive, real roots while those of orders 4, 8, 12, 16… have no roots that are positive and real. Zeros lying on the unit circle influence principally the passband, reject band, and reject level. Roots lying off the circle, on the other hand, mainly control the rolloff rate. Several figures illustrate how an interpreter can use this knowledge to help in choosing the number of points and orders of polynomials required to smooth data of various kinds: gravity, magnetics, electrical, well log, stratigraphic. The least squares weights also apply to array design.

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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