scholarly journals Real-time chatter detection via iterative Vold-Kalman filter and energy entropy

2021 ◽  
Author(s):  
Xingjian Dong ◽  
Guowei Tu ◽  
Xiaoshan Wang ◽  
Shiqian Chen
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Chatter Detection ◽  
Energy Entropy

scholarly journals Real-Time Chatter Detection via Iterative Vold-Kalman Filter and Energy Entropy

2021 ◽  
Author(s):  
Xingjian Dong ◽  
Guowei Tu ◽  
Xiaoshan Wang ◽  
Shiqian Chen
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Frequency Estimation ◽  
Chatter Detection ◽  
Filter Parameter ◽  
Energy Entropy ◽  
Mode Decomposition ◽  
Novel Method ◽  
Priori Information

Abstract Real-time chatter detection is important in improving the surface quality of workpieces in milling. Since the process from stable cutting to chatter is characterized by the progressive variation of the vibration energy distribution, entropy has been utilized to capture the decreasing randomness of vibration signals when chatter occurs. To make such an index more sensitive to transitions of the cutting state, the entropy can be computed based on signal components obtained through signal decomposition techniques. However, the classic empirical mode decomposition (EMD) is difficult to put into practice due to its weak robustness to noises. The up-to-date variational mode decomposition (VMD) has strict requirements on priori information of the signal and thus is not applicable either. In this paper, a novel method named the iterative Vold-Kalman filter (I-VKF) is proposed under the framework of the greedy algorithm, where the Vold-Kalman filter (VKF), a classic order-tracker for rotating machinery, is improved to recursively extract each signal component. In the meantime, a spectrum concentration index-based technique is developed for the instantaneous chatter frequency estimation to adaptively determine the filter parameter. Numerical examples demonstrate the superiority of the I-VKF over the original VKF, EMD, and VMD, especially in the presence of strong noises. Combined with the energy entropy of extracted components and an automatically calculated threshold, the proposed strategy greatly helps in timely chatter detection, which has been verified by dynamic simulation and experiments.

scholarly journals Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 750
Author(s):  
Wenkang Wan ◽  
Jingan Feng ◽  
Bao Song ◽  
Xinxin Li
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
State Parameter ◽  
State Observation ◽  
Straight Line ◽  
Distributed Drive Electric Vehicle ◽  
Huber Method ◽  
Yaw Moment

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

Real-Time Identification and Tracking of Infrared Markers Based on Kalman Filter

2012 ◽  
Vol 249-250 ◽  
pp. 1147-1153
Author(s):  
Qiao Na Xing ◽  
Da Yuan Yan ◽  
Xiao Ming Hu ◽  
Jun Qin Lin ◽  
Bo Yang
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
Complex Terrain ◽  
Experimental Results ◽  
Identification Method ◽  
In The Wild ◽  
Real Time Identification

Automatic equipmenttransportation in the wild complex terrain circumstances is very important in rescue or military. In this paper, an accompanying system based on the identification and tracking of infrared LEDmarkers is proposed. This system avoidsthe defect that visible-light identification method has. In addition, this paper presents a Kalman filter to predict where infraredmarkers may appear in the nextframe imageto reduce the searchingarea of infrared markers, which remarkablyimproves the identificationspeed of infrared markers. The experimental results show that the algorithm proposed in this paper is effective and feasible.

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