Statistical Damage Detection Approach in SHM Based on Error Prediction Model

This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.

Download Full-text

Dimensional surface error prediction model in five-axis flank milling for thin-walled parts

2020 10th Institute of Electrical and Electronics Engineers International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) ◽

10.1109/cyber50695.2020.9279151 ◽

2020 ◽

Author(s):

Liping Wang ◽

Shuyi Ge

Keyword(s):

Prediction Model ◽

Flank Milling ◽

Error Prediction ◽

Surface Error ◽

Thin Walled ◽

Five Axis ◽

Dimensional Surface

Download Full-text

An intelligent structural damage detection approach based on self-powered wireless sensor data

Automation in Construction ◽

10.1016/j.autcon.2015.10.001 ◽

2016 ◽

Vol 62 ◽

pp. 24-44 ◽

Cited By ~ 58

Author(s):

Amir H. Alavi ◽

Hassene Hasni ◽

Nizar Lajnef ◽

Karim Chatti ◽

Fred Faridazar

Keyword(s):

Damage Detection ◽

Structural Damage ◽

Sensor Data ◽

Wireless Sensor ◽

Structural Damage Detection ◽

Detection Approach ◽

Self Powered

Download Full-text

Decentralized Bayesian substructuring damage detection approach using nonstationary response measurements

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures ◽

10.1201/b16387-301 ◽

2014 ◽

pp. 2065-2070

Author(s):

Wang-Ji Yan ◽

Lambros Katafygiotis

Keyword(s):

Damage Detection ◽

Nonstationary Response ◽

Detection Approach

Download Full-text

2-D Statistical Damage Detection of Concrete Structures Combining Smart Piezoelectric Materials and Scanning Laser Doppler Vibrometry

Structural Durability & Health Monitoring ◽

10.32604/sdhm.2018.04607 ◽

2018 ◽

Vol 12 (4) ◽

pp. 257-279

Author(s):

Costas P. Providakis ◽

Stavros E. Tsistrakis ◽

Evangelos V. Liarakos

Keyword(s):

Damage Detection ◽

Piezoelectric Materials ◽

Concrete Structures ◽

Laser Doppler ◽

Scanning Laser ◽

Laser Doppler Vibrometry ◽

Statistical Damage

Download Full-text

The Thermal Error Prediction of NC Machine Tool Based on LS-SVM and Grey Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.16-19.410 ◽

2009 ◽

Vol 16-19 ◽

pp. 410-414 ◽

Cited By ~ 3

Author(s):

Chang Long Zhao ◽

Yi Qiang Wang ◽

Xue Song Guan

Keyword(s):

Support Vector Machine ◽

Correlation Analysis ◽

Prediction Model ◽

Machine Tool ◽

Least Squares ◽

Thermal Error ◽

Support Vector ◽

Error Prediction ◽

Nc Machine Tool ◽

Nc Machine

In this paper, a hybrid method of correlation analysis based on the gray theory and the least squares support vector machine is proposed to model the thermal error of spindle of NC machine tool and predict the thermal error. The gray correlation analysis is used to optimize the measuring points of spindle. The optimum measuring points and the measured thermal error of spindle are regarded as the data to be trained to build the thermal error prediction model based on the least squares support vector machine (LS-SVM). The results show that the thermal error prediction model based on LS-SVM of NC machine tool has advantages of high precision and good generalization performance. The prediction model can be used in real-time compensation of NC machine tool and can prove the process precision and reduce cost.

Download Full-text