Actual Acceleration Research Articles

Electronic mechanical braking system executive mechanism design, calculation, and modeling based on dynamic control

Introduction: As science and technology develop, automobiles are moving toward intelligence and electrification and need better braking systems.Methods: To improve the braking system’s response speed and braking effect, a longitudinal dynamics control system for automobiles based on the electronic mechanical braking system was proposed, and the electronic mechanical braking system was improved through automatic disturbance rejection control.Results: The experimental results show that the time required for achieving the target clamping force in the electronic mechanical braking system using self-disturbance rejection control and proportional integral differential control is only 0.01 s, but there is an issue of excessive control in the proportional integral differential system between 0.12 s and 0.2 s, while the self-disturbance rejection controller does not have this problem. Meanwhile, regardless of the interference applied, the electronic mechanical braking system with automatic disturbance rejection control can ensure that the clamping force does not fluctuate. In the joint simulation experiment, the expected acceleration and actual acceleration can remain consistent, and if the expected braking force is 9000 N, then the actual braking force of the electronic mechanical brake (EMB) is also 9000 N.Discussion: The above results indicate that the vehicle longitudinal dynamics control system using the electronic mechanical braking system not only responds fast but also has a good braking effect, avoiding the problem of excessive control and improving the driving experience.

Longitudinal Hierarchical Control of Autonomous Vehicle Based on Deep Reinforcement Learning and PID Algorithm

Longitudinal control of autonomous vehicles (AVs) has long been a prominent subject and challenge. A hierarchical longitudinal control system that integrates deep deterministic policy gradient (DDPG) and proportional–integral–derivative (PID) control algorithms was proposed in this paper to ensure safe and efficient vehicle operation. First, a hierarchical control structure was employed to devise the longitudinal control algorithm, utilizing a Carsim‐based model of the vehicle’s longitudinal dynamics. Subsequently, an upper controller algorithm was developed, combining DDPG and PID, wherein perceptual information such as leading vehicle speed and distance served as input state for the DDPG algorithm to determine PID parameters and output the desired acceleration of the vehicle. Following this, a lower controller was designed employing a PID‐based driving and braking switching strategy. The disparity between the desired and actual accelerations was fed into the PID, which calculated the control acceleration to enact the driving and braking switching strategy. Finally, the effectiveness of the designed control algorithm was validated through simulation scenarios using Carsim and Simulink. Results demonstrate that the longitudinal control method proposed herein adeptly manages vehicle speed and following distance, thus satisfying the safety requirements of AVs.

A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network

According to the International Union of Railways, railway networks count more than one million kilometers of tracks worldwide, a number that is to rise further as the goal is to promote rail transportation as a sustainable means to face the challenge of increased mobility. However, such a vast expansion further necessitates efficient and reliable infrastructure monitoring schemes able to guarantee the quality and safety of rail transportation. Traditional monitoring approaches, relying on visual inspection and portable measuring devices, cannot rise to the task as they do not allow for continuous inspection of extended portions of rail infrastructure. Therefore, mobile monitoring methodologies based on dedicated diagnostic vehicles have emerged as an alternative. Despite revolutionizing traditional monitoring methods, such vehicles are usually expensive and can only operate under the suspension of regular rail service. In this work, we propose an alternative approach for mobile sensing of railway infrastructure based on on-board monitoring data collected from low-cost vibration sensors, e.g., accelerometers, which can be mounted on in-service trains. Specifically, we focus on identifying the roughness profile of the tracks and propose a fusion of reduced-order vehicle models with a Bayesian inference approach for joint input-state estimation. To enhance the inference, we opt for a prior updating of the vehicle model parameters on the basis of an unscented Kalman filter and available measurements from a diagnostic vehicle. The key contributions of this work are (i) the consideration of the dynamic interaction between trains and tracks, which is usually ignored in rail roughness estimation, (ii) the adoption of reduced train vehicle models that decrease the computational effort of the identification task, (iii) the updating of the vehicle parameters to account for inconsistencies in the model used, and (iv) the application of the proposed methodology to actual acceleration measurements collected from a diagnostic vehicle of the Swiss Federal Railways network.

A Scoping Review of the Validity and Reliability of Smartphone Accelerometers When Collecting Kinematic Gait Data.

The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.

Attitude measurement of permanent magnet spherical motors based on adaptive mahony complementary filtering

An adaptive attitude measurement algorithm called adaptive mahony complementary filter(AMCF) is proposed to address the issues of drift and noise in gyroscope measurements, as well as the high computational complexity, error accumulation, and sensitivity to noise in the quaternion extended kalman filter(QEKF) within the permanent magnet spherical motor(PMSM) inertial measurement unit. Firstly, this method corrects the gyroscope’s angular velocity bias by utilizing the error between theoretical and actual accelerations. Subsequently, the covariance is adjusted by comparing dynamic accelerations to mitigate disturbances. The fourth-order Runge-Kutta method is employed to continuously update the quaternion, thereby achieving higher accuracy in attitude measurement. Finally, static and dynamic experimental investigations are conducted on the PMSM. The experimental results demonstrate that the AMCF offers faster computation speed compared to the QEKF and higher accuracy than the fast complementary filtering(FCF). This indicates that utilizing the AMCF enables better real-time attitude measurement for three-degree-of-freedom mechanical structures.

Duration and damping for ground motion characterization in frequency‐domain damage estimation of SDOF structures

AbstractIn estimating the damage to a single‐degree‐of‐freedom (SDOF) structure due to multiple earthquake events, it may be convenient to use a frequency‐based approach, in which the anticipated ground motions are characterized through power spectral density functions (PSDFs) compatible with the specified design spectra for those motions. Since a spectrum‐compatible PSDF corresponds to a fictitious stationary ground motion process of a specified duration, the duration of this process should preferably be same as the length of the stationary segment in the actual ground acceleration process for a meaningful computation of the stochastic response. On assuming the strong‐motion segment of a given ground motion to represent its stationary segment, the nonstationarity of the strong‐motion segment is evaluated for the existing strong‐motion duration definitions. It is found that the Trifunac‐Brady definition is the most reasonable strong‐motion duration definition in respect of consistently giving the longest strong‐motion segment with least nonstationarity. Further, a spectrum‐compatible PSDF can be used for estimating the stochastic response of a linear system, provided the system has the same damping ratio as that of the design spectrum from which the PSDF has been computed. This causes errors in estimating the peak response of an equivalent linear system due to its damping ratio being higher than the initial damping ratio of the nonlinear system. To address this, an empirical expression is developed for the modified damping ratio of a linear system, which would lead to the correct peak response of the system on using the PSDF obtained from the 5%‐damping design spectrum.

Nonlinear longitudinal cooperative control of heterogeneous connected vehicle platoon considering car-following interactions and communication delay

This article designs a novel controller for heterogeneous connected vehicles (CVs) platoon subject to communication delay. Firstly, third-order vehicle dynamics is used to capture the heterogeneity of vehicles. A new nonlinear controller for the CV platoon is proposed in the presence of car-following interactions, the acceleration difference and communication delays. Then, the internal stability of the CV platoon and the upper bound of communication delay are deduced by using the Lyapunov theorem. Also, the string stability of the linearized CV platoon system is proved by using the infinite-norm method. Additionally, a hierarchical control strategy suitable for co-simulation is designed to overcome the nonlinearity of vehicles and achieve consistency between the desired and actual acceleration. Finally, the superiority and effectiveness of the developed controller are verified by extensive simulation and cosimulation.

PMSM의 고속 운전 제어 을 위한 증분형 제어기의 적용 검증

In this paper, applies an incremental controller for high speed operation control in permanent magnet synchronous motors. Speed control performance using voltage-based flux-weakening control is not affected by motor parameters, so it is used in various motors for high-speed operation. In this paper, an incremental controller is applied to voltage-based flux-weakening control to reduce feedback errors. The incremental controller can reduce speed overshoot and ripple when applications with relatively constant acceleration. As a result of applying the incremental controller to the voltage-based flux-weakening control, the overshoot of the motor speed could be reduced, and the motor speed error could be minimized by reducing the difference between the actual motor acceleration and the target acceleration. It is considered that this method can be applied to devices with little change in acceleration to increase the effect of motor control.

Incremental proportion integration differentiation control of all-terrain vehicle magnetorheological suspension system under low-frequency disturbances

In intelligent control systems of magnetorheological (MR) suspension of all-terrain vehicle (ATV), low-frequency disturbance (LFD) in the measured feedback signal (acceleration) makes the controller unable to calculate the theoretical control force accurately. In particular, when the frequency of the LFD signal is close to that of the actual acceleration signal, the LFD cannot be filtered by designing a traditional filter. Based on the above questions, this paper proposes an incremental proportion integration differentiation (IPID) strategy to address the issue of LFD in the measured feedback acceleration signal of the MR suspension system of ATV. First of all, the model of 1/4 vehicle suspension in consideration of LFD is established, the source of LFD is analyzed which is due to the transformation of Coriolis acceleration under the condition of vehicle body pitch and roll. Next, a semi-active IPID controller is designed by utilizing differential derivation of discrete PID and semi-active principle to eliminate the LFD component mixed in the acceleration signal by making a difference. The particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the controller, which is then verified through numerical simulation. Subsequently, a real vehicle control experiment is carried out based on a 4 × 4 ATV equipped with the MR suspension system and implemented by DSP controller with the designed IPID algorithm. The effectiveness of the proposed method is evaluated under the speed 10 km h−1 and E road, and the designed method is compared with the traditional proportion integration differentiation (PID) control algorithm through simulation and experimentation to demonstrate the superiority and rationality of ‘filtering out’ LFD signal and improving control effectiveness.

Vibration response analysis of footbridge based on pedestrian perception

This article aims to study the influence of random crowd loading on the perceived vibration response of pedestrians. Firstly, a vertical vibration response analysis method considering pedestrian perception was established based on the random crowd walking model. Secondly, change rules of maximum vibration response of pedestrians, occurrence time and position interval under different random walk models were compared and analyzed. Finally, the vibration response reduction factor was defined by studying the correlation between the maximum vibration response of pedestrians and the peak acceleration of the structure, and the approximate calculation method of the maximum vibration response of pedestrians was proposed. The results show that the maximum acceleration perceived by pedestrians obeys the normal distribution under the four crowd walking models, the response distribution of ordered arrangement model (OAM) is larger than that of the other three models; The location and occurrence time of the maximum response depend on the distribution of pedestrian locations on the footbridge, and there is no significant change with the increase of population density. In addition, the distribution of OAM and stochastic arrival model (SAM) are consistent, which is concentrated in the middle of the total time-history. In contrast, the distribution of stochastic distribution model (SDM) and dynamic equilibrium model (DEM) are relatively uniform. The maximum error between the calculated acceleration maximum value and the actual acceleration value felt by the pedestrian is less than 5%. These results can provide reference for quantitative evaluation of pedestrian-induced vibration comfort.

Economic model predictive control with terminal set dynamic programming for tracking control

AbstractThis paper focuses on vehicle tracking control issues with consideration of energy efficiency, ride comfort and tracking performance. To address this issue, an economic model predictive control (EMPC) framework is proposed by taking average performance into account and introducing a time‐varying terminal set. We integrate an economic model predictive controller and a terminal controller with a feedback gain related to the time‐varying terminal set into the proposed EMPC framework. An acceleration‐dependent average constraint is designed and incorporated into the MPC optimization problem to facilitate the convergence of the actual acceleration to the desired one, which enables the vehicle to implement a smooth driving style that boosts energy economy. A tuning factor in the average constraint has the capacity to balance energy consumption and control performance. Under the premise of invariance constraints, a dynamic terminal set is constructed by solving a dynamic programming problem online, which enables an arbitrary location and scale of the terminal set at each iteration by taking the current and predicted tracking errors into account. This allows a reduction in the conservativeness of an economic model predictive controller in the sense of both the region of attraction and the cost bound. Recursive feasibility of the MPC optimization problem and the terminal set dynamic programming is ensured, and average performance is not worse than the performance with an optimal steady‐state operation. Moreover, the convergence of the closed‐loop tracking error to the optimal steady‐state is guaranteed by using the dissipativity theory. The effectiveness of the proposed algorithm is verified by numerical comparisons with different controller parameters and standard EMPC.

Planar Acceleration Sensor for UAV in Cruise State Based on Single-Electrode Triboelectric Nanogenerator

The unmanned aerial vehicle (UAV) accelerometer is a sensor that measures the acceleration of the UAV by detecting the components of gravity along the three axes. The use of self-powered technology in UAV accelerometers will become inevitable. This article proposes an asymmetric electrode arc triboelectric nanogenerator (ASA-TENG) based on the single-electrode mode. It is a potential self-powered acceleration sensor that is installed on the UAV to detect the acceleration in the cruise state. It is characterized by a simple structure, low quality, high sensitivity, large detection range, low cost, and environmental friendliness. Two ASA-TENG structures are stacked into a cross along the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula> -axis and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${y}$ </tex-math></inline-formula> -axis to detect the components of actual acceleration along the two axes to detect the acceleration of random directions. Finally, the actual acceleration is obtained through the principle of acceleration synthesis, which can accurately detect the direction of acceleration. The ASA-TENG acceleration sensor can detect accelerations in the range of 0.1–40 m/s2. It exhibits a high sensitivity, and its response time has a good fitting relationship with acceleration ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> 2 = 0.997). In addition, the voltage signal amplitude generated by the ASA-TENG sensor shows negligible fluctuations after 6 h of continuous operation. Comparison experiments with commercial acceleration sensor show a maximum relative error of only 2.9%. Therefore, the ASA-TENG acceleration sensors can be potentially used under cruise conditions of UAV as it presents numerous advantages.

차량 내부 소음 예측을 위한 멀티 모달 자기 지도 학습 네트워크

Predicting in-vehicle noise levels is an important issue in automobile industry. In most previous studies, various supervised learning methods that use both the input and output(labeled) variables are used to predict automobile noise levels. However, collecting labeled data for in-vehicle noise prediction is time consuming and expensive. In this study, we propose a multimodal self-supervised learning framework that can predict in-vehicle noise levels with only a small amount of labeled data, so that resources required to collect labeled data can be saved. In our framework, both original acceleration signals and spectrograms converted from the original data are used as the input to predict in-vehicle noise levels. In the first stage, we pretrain the features of the input data based on the relationship between the signal and spectrogram data using only unlabeled data, which is much easier to collect than labeled data. In the second stage, we use a small amount of data to construct the in-vehicle noise prediction model with the pretrained feature extractor. The effectiveness and applicability of the proposed framework are demonstrated using the actual acceleration signal data collected from various locations of electric power steering vehicle noise levels. The proposed framework outperforms the existing supervised learning method especially when a few labeled data are available.

Method of estimating the time required to ensure the uniform motion of vehicle platoon progres-sion on the coordinated section of the city arterial road

Problem. The problem of distributing competing flows of road users in time is prevalent worldwide, and there is no fully justified and intelligible solution, even if only traffic flows can be considered. At the busiest street intersections, the primary means of traffic management is traffic signal control. If the traffic lights are close to each other, then it is a good practice to coordinate the operation of such traffic lights to organise the most unhampered movement of vehicles in one or several directions; that is the progression. Goal. The goal is estimation of the time required to ensure the uniform motion of vehicle platoon progression on the coordinated artery of the city arterial. Methodology. The developed approach is based on the use of analytical research methods and allows to formalize the interaction of the platoon progression with additional vehicles. Results. The results of the estimation of the minimum time required to ensure the uniform motion of the platoon progression allow us to state that for actual speeds and vehicle accelerations when starting from the stop-line of the intersection, the requirement for the uniform motion of the platoon progression is too strict, especially if we take into account that advance time will increase significantly as the number of additional vehicles in the queue increases. Originality. Analytical models created using formulas of the kinematics of rectilinear uniform accelerated motion of a point make it possible to estimate the minimum advance time of the start of the traffic light signal due to the deliberate speed of traffic and the average acceleration of one additional vehicle when starting from the stop-line of the intersection. Practical value. The developed models reflect the minimum value of the advance time, which ensures uniform motion of the platoon progression, provided that there is one additional vehicle at the stop-line of the intersection.

Post‐earthquake system identification and response estimation of an elastic compound building using a simplified numerical model

The post-earthquake system identification and response estimation of an elastic 13-story compound building during four seismic events were effectively conducted using a simplified numerical model: the generalized building model (GBM). The compound building examined was the recently remolded office building of Taiwan's National Center for Research on Earthquake Engineering. The 7–13th stories of the building are extended steel structures seated atop six older reinforced-concrete stories. In addition to the vertical extension, the building was horizontally extended. The extension incorporated several types of seismic dampers. The GBM, which reflects the dynamic characteristics of the elastic compound building, was constructed using only the acceleration records of the roof and the 7th story. The effectiveness of the GBM was then verified by comparing the estimated and actual acceleration responses of the other stories. From the verified GBM, the modal parameters and drift responses of the compound building were estimated accordingly.

Affordable Earthquake Building

Abstract: Nowadays springs are the main component in the structures. A new low-cost seismic isolation system which is related on spring tube bracings has been proposed and studied, and according to that study when springs are performed their designated jobs there is very little reason to spare them much thought. One area that you might not have considered the worth of springs within is the science of architecture, but actually springs are being used in buildings all around the world. As per the study, multiple compression-type springs are positioned in a special cylindrical tube to obtain a symmetrical response in tension and compression-type axial loading. An isolation floor, which consists of pin-ended steel columns and spring tube bracings, is constructed at the foundation level or any intermediate level of the building. An experimental campaign with three stages was completed to evaluate the capability of the system. First, the behavior of the spring tubes subjected to axial displacement reversals with varying frequencies was determined. In the second phase, the isolation floor was assessed in the quasi-static tests. Finally, a ¼ scaled 3D steel frame was tested on the shake table using actual acceleration records. The transmitted acceleration to the floor levels is greatly diminished because of the isolation story, which effects longer period and higher damping. There are no stability and self-centering problems in the isolation floor.

StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs

Weight pruning methods of deep neural networks (DNNs) have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration. However, in prior work, the pruning rate (degree of sparsity) and GPU acceleration are limited (to less than 50%) when accuracy needs to be maintained. In this work, we overcome these limitations by proposing a unified, systematic framework of structured weight pruning for DNNs. It is a framework that can be used to induce different types of structured sparsity, such as filterwise, channelwise, and shapewise sparsity, as well as nonstructured sparsity. The proposed framework incorporates stochastic gradient descent (SGD; or ADAM) with alternating direction method of multipliers (ADMM) and can be understood as a dynamic regularization method in which the regularization target is analytically updated in each iteration. Leveraging special characteristics of ADMM, we further propose a progressive, multistep weight pruning framework and a network purification and unused path removal procedure, in order to achieve higher pruning rate without accuracy loss. Without loss of accuracy on the AlexNet model, we achieve 2.58× and 3.65× average measured speedup on two GPUs, clearly outperforming the prior work. The average speedups reach 3.15× and 8.52× when allowing a moderate accuracy loss of 2%. In this case, the model compression for convolutional layers is 15.0× , corresponding to 11.93× measured CPU speedup. As another example, for the ResNet-18 model on the CIFAR-10 data set, we achieve an unprecedented 54.2× structured pruning rate on CONV layers. This is 32× higher pruning rate compared with recent work and can further translate into 7.6× inference time speedup on the Adreno 640 mobile GPU compared with the original, unpruned DNN model. We share our codes and models at the link http://bit.ly/2M0V7DO.

Sensitivity Identification of Low-Frequency Cantilever Fibre Bragg Grating Accelerometer using Cascade-Forward Backpropagation Neural Network

The frequency-dependent issues and instrumentation requirement for FBG sensors necessitate the identification of the sensitivity of the cantilever FBG accelerometer using machine learning. As result, this article presents a cascade-forward backpropagation (CFB) neural network with an orthogonally-phase chirp signal with a range of constant forcing frequency and steadily increasing base acceleration amplitude as its input. This input/output data set was numerically calculated by integrating modal model and Euler-Bernoulli beam approach (FBG-MM). The maximum amplitude of the base acceleration was 200 m/s2 and the forcing frequencies and location of the FBG sensor mounted on the beam measured from the fixed end were 1 to 90 Hz and 0.03 m, respectively. The trained CFB predicted the wavelength shift very well, but it was restricted to one-half of the forcing frequencies of those used in the CFB training process, whereas the base acceleration is not an important element in determining the sensitivity of the FBG accelerometer. In terms of the FBG sensor’s location on the beam, considering a few positions will greatly expand the CFB’s capabilities. Future work will include the use of the trained CFB as “black-box sensitivity” for actual acceleration measurement, as well as the use of empirical data to replace the numerical FBG-MM as the input/output training data set.

On the response of eccentric non-structures subjected to horizontal oscillation

ABSTRACTIn contrast to building structures, little attention has been paid to the seismic performance of non-structures in buildings, especially of eccentric non-structures. This paper attempts to explore the rocking and overturning of eccentric blocks, which could be the equipment or non-structural components in buildings, subjected to horizontal vibration. In the present study, a non-structure is regarded as a rigid block with tunable mass eccentricity, and the contact friction is assumed to be large enough to prevent the rigid block from sliding. A free shaking test is first carried out to determine the value of the coefficient of restitution. Force oscillations are then investigated to determine the critical time and acceleration when the eccentric blocks fall down under the swept-frequency oscillation. Finally, an actual acceleration from the seismic records is executed on each of the eccentric blocks to measure and to explore the corresponding toppling probability. It is found that the toppling of blocks is not necessarily linked to the magnitude of the peak ground acceleration.

ADOPTION OF VIBRATION OF FUZZY PARTIAL FRACTIONAL-ORDER EQUATION IN MONITORING OF NOISE BIG DATA UNDER INFINITE IMPULSE RESPONSE DIGITAL FILTER ALGORITHM

In order to study the application of fuzzy partial fractional-order equation based on infinite impulse response (IIR) digital filter in noise big data monitoring, in this study, first, the noise level is calculated through IIR filter to obtain the sound signal sequence after C weight and A weight; the weight equivalent continuous sound level of C, A, and Z is calculated. Then, the vibration condition of fuzzy partial fractional-order equation is studied; the vibration acceleration signal is converted into the vibration velocity and displacement signal; the corresponding effective values are calculated according to the root mean square (RMS), and the noise monitoring platform is designed from hardware and software aspects. Finally, the calibrated sound level value is compared with the standard signal value generated by the digital signal generator; the sequence of sound signal after weight of the original signal, the actual acceleration signal, velocity signal, and displacement signal are calculated; the acceleration signal, velocity signal, and theoretical signal values are compared at different frequencies. The results show that the error between the calculated and calibrated sound level and the standard signal value is very small, and the results of weight counting of C and A are good and meet the requirement of calculation; the error between the measured value of actual acceleration, velocity, and displacement signal and the theoretical value is small, which can realize the accurate acquisition of vibration signal; the error between the actual measured value and the theoretical value of acceleration and velocity at different frequencies is small, about 1% and 10%, respectively. Therefore, the fuzzy partial fractional-order equation based on IIR digital filter can effectively monitor the big noise data.