Online identification of impact loads of multi-degree-of-freedom system based on Kalman filter

2020 ◽  
Vol 64 (1-4) ◽  
pp. 359-367
Author(s):  
Jinhui Jiang ◽  
Shuyi Luo ◽  
Zhongzai Liang
Keyword(s):  
Kalman Filter ◽  
Dynamic Load ◽  
Impact Load ◽  
Structural Response ◽  
Degree Of Freedom ◽  
Load Identification ◽  
Theoretical Derivation ◽  
Structural Dynamic ◽  
Online Identification ◽  
Multiple Impact

Dynamic load identification is the second kind of inverse problem in structural dynamics. It is a process of reconstructing load applied to structure in case of structural dynamic model and information of structural response. Online identification is one of the frontier problems in dynamic load identification, which has high difficulty and broad application prospects. In this paper, an online identification of dynamic load of the multi-degree-of-freedom system based on Kalman filter in modal space is proposed. Since the Kalman filter has excellent real-time performance and robustness, it is possible to be used in dynamic load online identification. We start from the theoretical derivation in detail for the multi-degree-of-freedom system, then the feasibility and effectiveness of the method is verified by numerical simulation of three-degree-of-freedom system with the single impact load and continuous multiple impact load.

An Algorithm of Dynamic Load Identification Based on FBG Sensor and Kalman Filter

2018 ◽  
Vol 38 (3) ◽  
pp. 0328012
Author(s):  
宋雪刚 Song Xuegang ◽  
刘鹏 Liu Peng ◽  
程竹明 Cheng Zhuming ◽  
魏真 Wei Zhen ◽  
喻俊松 Yu Junsong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Dynamic Load ◽  
Load Identification ◽  
Fbg Sensor

scholarly journals A Recurrent Neural Network-Based Method for Dynamic Load Identification of Beam Structures

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7846
Author(s):  
Hongji Yang ◽  
Jinhui Jiang ◽  
Guoping Chen ◽  
M Shadi Mohamed ◽  
Fan Lu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dynamic Load ◽  
Short Term Memory ◽  
Load Identification ◽  
Short Term ◽  
Structural Dynamic ◽  
Term Memory ◽  
Structural Dynamic Characteristics ◽  
Long Short Term Memory

The determination of structural dynamic characteristics can be challenging, especially for complex cases. This can be a major impediment for dynamic load identification in many engineering applications. Hence, avoiding the need to find numerous solutions for structural dynamic characteristics can significantly simplify dynamic load identification. To achieve this, we rely on machine learning. The recent developments in machine learning have fundamentally changed the way we approach problems in numerous fields. Machine learning models can be more easily established to solve inverse problems compared to standard approaches. Here, we propose a novel method for dynamic load identification, exploiting deep learning. The proposed algorithm is a time-domain solution for beam structures based on the recurrent neural network theory and the long short-term memory. A deep learning model, which contains one bidirectional long short-term memory layer, one long short-term memory layer and two full connection layers, is constructed to identify the typical dynamic loads of a simply supported beam. The dynamic inverse model based on the proposed algorithm is then used to identify a sinusoidal, an impulsive and a random excitation. The accuracy, the robustness and the adaptability of the model are analyzed. Moreover, the effects of different architectures and hyperparameters on the identification results are evaluated. We show that the model can identify multi-points excitations well. Ultimately, the impact of the number and the position of the measuring points is discussed, and it is confirmed that the identification errors are not sensitive to the layout of the measuring points. All the presented results indicate the advantages of the proposed method, which can be beneficial for many applications.

Dynamic Load Identification Theoretical Summary and the Application on Mining Machinery

2013 ◽  
Vol 330 ◽  
pp. 811-814
Author(s):  
Peng Wang ◽  
Guo Lai Yang ◽  
Hui Xiao
Keyword(s):  
Dynamic Load ◽  
Frequency Domain Method ◽  
Load Identification ◽  
Engineering Structures ◽  
Structural Dynamic ◽  
New Methods ◽  
Identification Method ◽  
Domain Method ◽  
Mining Machinery ◽  
Identification Theory

This paper is devoted to describe a new dynamic load identification method about mining machinery structural; Frist, reviews the background of structural dynamic load identification theory; Then, introduce some familiar dynamic load identification methods, including frequency domain method, time domain method and some other new methods; Describe the steps about dynamic load identification method used in mining machinery engineering structures; Last, practice shows that the method is profitable.

An efficient method to reduce ill-posedness for structural dynamic load identification

2017 ◽  
Vol 95 ◽  
pp. 273-285 ◽  
Author(s):  
Jie Liu ◽  
Xianghua Meng ◽  
Dequan Zhang ◽  
Chao Jiang ◽  
Xu Han
Keyword(s):  
Efficient Method ◽  
Dynamic Load ◽  
Load Identification ◽  
Structural Dynamic

scholarly journals Online Dynamic Load Identification Based on Extended Kalman Filter for Structures with Varying Parameters

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1372
Author(s):  
Hongqiu Li ◽  
Jinhui Jiang ◽  
M Shadi Mohamed
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dynamic Load ◽  
Degrees Of Freedom ◽  
Symmetric System ◽  
Identification Algorithm ◽  
Structure Parameters ◽  
Load Identification ◽  
Unknown Parameters ◽  
Varying Parameters

Dynamic load identification is an inverse problem concerned with finding the load applied on a structure when the dynamic characteristics and the response of the structure are known. In engineering applications, some of the structure parameters such as the mass or the stiffness may be unknown and/or may change in time. In this paper, an online dynamic load identification algorithm based on an extended Kalman filter is proposed. The algorithm not only identifies the load by measuring the structural response but also identifies the unknown structure parameters and tracks their changes. We discuss the proposed algorithm for the cases when the unknown parameters are the stiffness or the mass coefficients. Furthermore, for a system with many degrees of freedom and to achieve online computations, we implement the model reduction theory. Thus, we reduce the number of degrees of freedom in the resulting symmetric system before applying the proposed extended Kalman filter algorithm. The algorithm is used to recover the dynamic loads in three numerical examples. It is also used to identify the dynamic load in a lab experiment for a structure with varying parameters. The simulations and the experimental results show that the proposed algorithm is effective and can simultaneously identify the parameters and any changes in them as well as the applied dynamic load.

Impact Load Identification Based on Local Measurements

2005 ◽  
Vol 293-294 ◽  
pp. 159-166 ◽  
Author(s):  
Marcin Wiklo ◽  
Jan Holnicki-Szulc
Keyword(s):  
Impact Load ◽  
Structural Response ◽  
Theoretical Background ◽  
Dynamic Responses ◽  
Local Dynamic ◽  
Load Identification ◽  
Influence Matrix ◽  
Global Dynamic ◽  
Distance Minimization ◽  
The Impact

A new methodology for load identification is proposed. The global dynamic structural response is modeled using only pre-computed, time dependent, dynamic influence matrix, describing structural response to locally generated unit impulses. Then, the impact load identification procedure is based on distance minimization between the modeled and measured local dynamic responses in sensor locations. The theoretical background as well as numerical examples is presented.

A fast technique using output only to localize and quantify multiple damages for multi-degree-of-freedom systems

2020 ◽  
pp. 147592172093281
Author(s):  
Liangliang Cheng ◽  
Wenshan Fang ◽  
Yunpeng Zhu
Keyword(s):  
Experimental Studies ◽  
Low Frequency ◽  
Degree Of Freedom ◽  
Theoretical Derivation ◽  
Structural Dynamic ◽  
Damage Indicators ◽  
Frequency Components ◽  
Output Only ◽  
Mass Spring ◽  
Local Stiffness

Vibration-based methods for identifying and evaluating structural damages have been widely studied in the last decades. However, in the state-of-art methods, there are some practical limitations owing to the complicated calculation process and low damage sensitivities. A new method for localizing and quantifying the damages of a beam-like structure based on low-frequency components, including direct current component, of the output signal subject to an arbitrary input excitation, is proposed in this article. The relationship between the low-frequency components of any two adjacent measurement points is investigated, in order to help to understand the link between damages and low-frequency components of outputs. The theoretical derivation of this method begins with a multi-degree-of-freedom structure of the mass–spring–damper chain, and the damage is considered to be a linear combination of local stiffness losses, resulting in changes in structural dynamic behaviors. The validity and feasibility of the proposed damage indicators are proved by numerical and experimental studies. It is further shown that the severity of the damage can be properly identified using the proposed damage indicator. Moreover, a discussion concerning potential detection on multiple nonlinear damages is presented at the end of this article.

scholarly journals Real-Time Identification of Dynamic Loads Using Inverse Solution and Kalman Filter

2020 ◽  
Vol 10 (19) ◽  
pp. 6767
Author(s):  
Jinhui Jiang ◽  
Shuyi Luo ◽  
M. Shadi Mohamed ◽  
Zhongzai Liang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Earthquake Engineering ◽  
State Vector ◽  
The State ◽  
Dynamic Loads ◽  
Unbiased Estimation ◽  
System Response ◽  
Structural Dynamic ◽  
Online Identification

Evaluating dynamic loads in real time is crucial for health monitoring, fault diagnosis and fatigue analysis in aerospace, automotive and earthquake engineering among other vibration related applications. Developing such algorithms can be vital for several safety and performance functionalities. Therefore, over the past few years the identification of dynamic loads has attracted a lot of attention; however, little literature on the online identification can be found. In this paper, we propose an online-identification method of structural dynamic loads so that the dynamic load is evaluated in real time and while the system response is still being measured. This is achieved by significantly improving the identification efficiency while retaining a high accuracy. The proposed method which is based on Kalman filter, is introduced in detail for a finite as well as an infinite number of degrees of freedom. Starting from an initial guess of the state vector we evaluate the error covariance, which then helps to identify the value of the excitation force using a weighted least square method and minimizing the covariance unbiased estimation. This is repeated at certain time intervals i.e., time steps where the state vector is updated in real time as acceleration measurements are updated. The feasibility of the method is validated using numerical simulations and an experimental verification where a detailed LabVIEW (National Instruments Ltd.) implementation is provided.

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