Study of laser displacement measurement data abnormal correction algorithm
Abstract
Purpose
This paper aims to reduce and eliminate the abnormal peaks which, because of the reflection in the process of laser detection, make it easier to proceed with further analysis.
Design/methodology/approach
To solve the above problem, an abnormal data correction algorithm based on histogram, K-Means clustering and improved robust locally weighted scatter plot smoothing (LOWESS) is put forward. The proposed algorithm does section leveling for shear plant first and then applies histogram to define the abnormal fluctuation data between the neighboring points and utilizes a K-Means clustering to eliminate the abnormal data. After that, the improved robust LOWESS method, which is based on Euclidean distance, is used to remove the noise interference and finally obtain the waveform characteristics for next data processing.
Findings
The experiment result of liner tool mark laser test data correction demonstrates the accuracy and reliability of the proposed algorithm.
Originality/value
The study enables the following points: the detection signal automatic leveling; abnormal data identification and demarcation using K-Means clustering and histogram; and data smoothing using LOWESS.
Keywords
Acknowledgements
This article was supported by the key project of the Ministry of public security technology research program (approved grants 2014JSYJA020) and key project of Kunming science and technology plan (approved grants 2015-1-S-00284).
Citation
Yi, Z., Pan, N., Liu, Y. and Guo, Y. (2017), "Study of laser displacement measurement data abnormal correction algorithm", Engineering Computations, Vol. 34 No. 1, pp. 123-133. https://doi.org/10.1108/EC-10-2015-0325
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited