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CN-122015730-A - Data processing method and system for phase difference method thickness gauge

CN122015730ACN 122015730 ACN122015730 ACN 122015730ACN-122015730-A

Abstract

The invention discloses a data processing method and a data processing system for a coating thickness gauge by a phase difference method, and belongs to the technical field of nondestructive testing. The method aims to solve the problems of scattered frequency-phase difference data, low linear fitting degree and poor measurement repeatability caused by Gaussian random noise, periodic electromagnetic interference and outlier sporadic noise. The method comprises the steps of obtaining original frequency-phase difference data, preprocessing through filtering and self-adaptive outlier detection, performing core straight line fitting on the preprocessed data by utilizing robust regression algorithms such as random sampling consistency (RANSAC) and the like, and extracting a stable linear relation. The method can inhibit three types of typical noise pertinently, and compared with a direct linear fitting method, the standard deviation of the repeated thickness measurement of the same standard component can be reduced by about 60-80%, so that the repeatability and reliability of the measurement are greatly improved, and an effective solution is provided for accurate and stable measurement of the thickness of the coating.

Inventors

  • PENG HEXI

Assignees

  • 天津大学

Dates

Publication Date
20260512
Application Date
20260313

Claims (7)

  1. 1. The data processing method for the phase difference method thickness gauge is characterized by comprising the following steps of: S1, acquiring an original phase difference data sequence acquired by a thickness gauge at a plurality of detection frequencies; s2, preprocessing the original data sequence to reduce the influence of Gaussian random noise, periodic interference noise and outlier sporadic noise; s3, performing straight line fitting on the preprocessed data sequence by adopting a robust regression algorithm to obtain a slope parameter for calculating thickness; And S4, outputting a thickness measurement value according to the fitting result, wherein after the thickness measurement value is processed by the method, the standard deviation of repeated thickness measurement on the same standard component is obviously reduced compared with that of the direct linear fitting method.
  2. 2. The method of claim 1, wherein the preprocessing step includes first frequency domain filtering the original data sequence to suppress periodic interference noise, and then identifying and correcting outliers using an adaptive method based on moving window statistics.
  3. 3. The method of claim 2, wherein the adaptive method based on moving window statistics is specifically that the absolute deviation between the median of the data in the window is calculated, the point where the deviation from the median exceeds a preset threshold is determined as an outlier, and the weighted average of the valid data in the window is used for replacement.
  4. 4. The method of claim 1, wherein the robust regression algorithm is a random sample consensus algorithm.
  5. 5. The method of claim 1, further comprising the step of model optimization after obtaining the slope parameters, calculating residuals of data points relative to a fitted line, and optimizing the model by introducing a compensation term or segment fitting if the residual distribution exhibits regularity.
  6. 6. A data processing system for a phase difference thickness gauge, comprising: the data acquisition module is used for acquiring an original frequency-phase difference data sequence; A noise processing module for performing the preprocessing steps of any one of claims 1 to 3; the robust fitting module is used for performing straight line fitting by adopting a robust regression algorithm and acquiring slope parameters; And the thickness calculating and outputting module is used for calculating and outputting a thickness value according to the slope parameter.
  7. 7. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 5.

Description

Data processing method and system for phase difference method thickness gauge 1. Technical field The invention relates to the technical field of nondestructive testing, in particular to a data processing method and system for improving the measurement precision of a coating thickness gauge by a phase difference method. 2. Background art A phase difference thickness gauge (e.g., coatPro series) calculates thickness by measuring the phase difference at different frequency excitations. In an ideal state, the frequency and the phase difference are in a linear relation, and the thickness can be obtained through the slope of linear fitting. However, in actual measurement, due to the common influence of gaussian random noise (such as sensor thermal noise), periodic electromagnetic interference, sporadic outliers (such as transient electromagnetic pulse or surface defect reflection) and other factors, collected frequency-phase difference data points often are seriously scattered, so that the value of a determination coefficient (R 2) of direct linear fitting is very low, the slope of a fitting straight line is unstable, and finally, the thickness calculation error is large and the measurement repeatability is poor. Currently, general-purpose digital filters (e.g., low-pass filtering) are commonly used in the industry to smooth the original signal, but such methods are mainly directed to time-domain waveforms and are insensitive to non-gaussian, local outliers (Outliers). The linear optimization effect of the frequency-phase difference data existing as the two-dimensional scatter set is limited, and it is difficult to robustly extract the linear trend reflecting the real thickness information while suppressing various composite noises. 3. Summary of the invention The invention aims to overcome the defects of the prior art, and provides a data processing method and a data processing system for a thickness gauge by a phase difference method, which can effectively inhibit Gaussian random noise, periodic interference, sporadic outliers and other noise types, and remarkably improve the goodness (R 2 value) and stability of frequency-phase difference data linear fitting, so that the accuracy, repeatability and reliability of thickness measurement are fundamentally improved. Technical proposal In order to achieve the above object, the data processing method for a phase difference method thickness gauge provided by the invention aims at random noise and outlier interference, and comprises the following steps: S1, acquiring data, namely acquiring an original frequency-phase difference data set obtained by measuring the same sample by a thickness gauge under a plurality of detection frequencies; S2, data preprocessing, namely performing frequency domain filtering on the original data set to inhibit periodic interference, and identifying and correcting outliers by adopting a self-adaptive method based on moving window statistics; S3, robust core fitting, namely performing straight line fitting on the preprocessed data points by adopting a robust regression algorithm such as random sampling consistency (RANSAC) to obtain slope parameters reflecting thickness information; and S4, model optimization and output, namely calculating and outputting a thickness value according to the slope parameter. In addition, the method can be further applied to a measurement scene with local systematic frequency response abnormality. In the application mode, through initial fitting and residual analysis of the data, a continuous frequency response abnormal region is intelligently identified, abnormal region data is eliminated in a robust fitting process, and a thickness value and related abnormal frequency band diagnosis information are finally output. Advantageous effects Compared with the prior art, the invention has the following beneficial effects: i. The preprocessing and robust fitting combined scheme provided by the invention is aimed at designing frequency domain filtering, self-adaptive outlier processing and robust regression algorithm, can systematically process three main noise sources which lead to data scattering, namely Gaussian random noise, periodic interference and sporadic outliers, and overcomes the defect of a general filter in solving the linearization problem of the two-dimensional scatter diagram. The linearity and the accuracy are improved remarkably, that is, the linear fitting goodness (R 2 value) of the frequency-phase difference data can be improved from about 0.70 to about 0.97 of the traditional direct fitting after the method is applied. The inversion error of single measurement on standard thickness samples is significantly reduced. Measurement repeatability and stability span improvement the most critical is that the invention greatly improves the measurement repeatability by extracting the robust linear trend in the data. The standard deviation of the thickness calculation results can be reduced by 60% -80% (for