Amplitude And Frequency Modulated Research Articles

Short-term cooling load prediction for central air conditioning systems with small sample based on permutation entropy and temporal convolutional network

In air-conditioning load predicting, the mainstream methods mainly include mechanism model based methods and data-driven prediction methods. The mechanism model can achieve accurate prediction, but are normally used to predict non-existent buildings and by default have to make several assumptions regarding post occupancy. This study proposes a novel data-driven short-term cooling load prediction method based on historical data of the existing buildings to improve the prediction performance when the size of the training sample datasets, including load data and air conditioning internal operation data, is small. Firstly, an improved version of variational mode decomposition (IVMD) technique is proposed to deconstruct complex historical cooling load signals. Then, the permutation entropy and Savitzky-Golay (PE-SG) smoothing method is developed to extract singles with distinct characteristics and high regularity, which comprises multiple single-component amplitude and frequency modulation (AFM) signals, thus mitigating the interference of noise in predictive accuracy. Subsequently, a data-driven model based on temporal convolutional network (TCN) is constructed, which effectively alleviates the risks of over-fitting and under-fitting. Finally, the efficacy of the proposed method is validated through two case studies, and the obtained results show that the issues confronted by small sample datasets are adeptly solved by this method. The precision of prediction is significantly enhanced according to the comparison to other traditional models, showcasing greater stability and generalization ability.

AFM signal model for dysarthric speech classification using speech biomarkers.

Neurological disorders include various conditions affecting the brain, spinal cord, and nervous system which results in reduced performance in different organs and muscles throughout the human body. Dysarthia is a neurological disorder that significantly impairs an individual's ability to effectively communicate through speech. Individuals with dysarthria are characterized by muscle weakness that results in slow, slurred, and less intelligible speech production. An efficient identification of speech disorders at the beginning stages helps doctors suggest proper medications. The classification of dysarthric speech assumes a pivotal role as a diagnostic tool, enabling accurate differentiation between healthy speech patterns and those affected by dysarthria. Achieving a clear distinction between dysarthric speech and the speech of healthy individuals is made possible through the application of advanced machine learning techniques. In this work, we conducted feature extraction by utilizing the Amplitude and frequency modulated (AFM) signal model, resulting in the generation of a comprehensive array of unique features. A method involving Fourier-Bessel series expansion is employed to separate various components within a complex speech signal into distinct elements. Subsequently, the Discrete Energy Separation Algorithm is utilized to extract essential parameters, namely the Amplitude envelope and Instantaneous frequency, from each component within the speech signal. To ensure the robustness and applicability of our findings, we harnessed data from various sources, including TORGO, UA Speech, and Parkinson datasets. Furthermore, the classifier's performance was evaluated based on multiple measures such as the area under the curve, F1-Score, sensitivity, and accuracy, encompassing KNN, SVM, LDA, NB, and Boosted Tree. Our analyses resulted in classification accuracies ranging from 85 to 97.8% and the F1-score ranging between 0.90 and 0.97.

Multiphysics and comparative analysis on failure detection in planetary gears

Gearbox monitoring is considered an important scientific focus for predicting preventative maintenance plans. In these systems, mechanical defects such as loading problems, eccentricity and torsional vibrations lead to shaft fatigue and other damage to various other mechanical components. Various methods have been developed to detect and identify the presence of defects in gearboxes. In this paper, we investigate the effect of gearbox faults on the current signal by defining an analytical correlation between the physical presence of the fault and the stator current. The theoretical development is supported by experimental measurements taken on a back-to-back planetary gearbox. Planetary gearbox faults result in the motor’s input torque oscillations, generating amplitude and frequency modulation (AM-FM). These modulations have an effect on stator current signals. The study of the stator current was followed by that of vibration signal and acoustic pressure taken simultaneously under the same operating conditions. This comparative investigation aims to present the differences between different techniques and highlight the efficiency of each.

Removal of AM-FM harmonics using VMD technology for operational modal analysis of milling robot

The dynamics of a milling robot have a significant impact on machining efficiency and quality. Traditional experimental modal analysis (EMA) is used to study the dynamics of milling robots in the static state. However, the dynamic properties of milling robots change when they are in the operational state. To investigate the dynamics of milling robots during cutting, operational modal analysis (OMA) is used. During normal cutting, milling robots are primarily excited by cutting forces that are dominated by amplitude and frequency-modulated (AM-FM) harmonic components that are caused by the fluctuation of cutting loads and spindle speeds. Such cutting forces make the dynamic parameters identified by OMA inaccurate. To address this issue, the variational mode decomposition-OMA (VMD-OMA) method is proposed herein, which involves removing AM-FM harmonic components from the response signal using the VMD technology and subsequently using the stochastic subspace identification (SSI) algorithm to identify dynamic parameters. To verify the effectiveness of this method, simulations and experimental research have been conducted. The simulation results demonstrate that the dynamic parameters identified by VMD-OMA are generally closer to the theoretical values. Experimental research conducted on a milling robot named TriMule shows that VMD-OMA can effectively eliminate AM-FM harmonics in response signals and identify the dynamic parameters of TriMule during cutting.

Generalized Synchroextracting-Based Stepwise Demodulation Transform and Its Application to Fault Diagnosis of Rotating Machinery

The nonstationary characteristics of time-varying signals can be characterized by time–frequency analysis (TFA) accurately, which has been widely used in fault diagnosis of rotating machinery. However, some practical signals contain complex multicomponent modes and noise interference, which will pose challenges to traditional TFA methods. In this article, a novel technique called generalized synchroextracting-based stepwise demodulation (GSET-SD) transform is proposed. GSET-SD introduces a strategy that combines synchroextracting transform (SET) with a stepwise demodulation procedure, thereby solving the problem of detecting and retrieving the amplitude- and frequency-modulated (AM-FM) components of a multicomponent signal from the time–frequency representation (TFR), while allowing the signal to be reconstructed from the TFR. Furthermore, to increase the estimation accuracy of the initial instantaneous frequency (IF) in processing the strong modulation signals, SET is extended to the second-order or higher order domain, and the IF is accurately estimated by the higher order polynomial method, which can obtain higher resolution TFR. The proposed approach has been applied to numerical simulations and application research. The experimental results verify the effectiveness and superiority of GSET-SD in processing strong modulation signals and demonstrate its promise in the field of fault diagnosis of rotating machinery under time-varying speeds.

A novel PRFB decomposition for non-stationary time-series and image analysis

This work presents a novel perfect reconstruction filterbank decomposition (PRFBD) method for nonlinear and non-stationary time-series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach based on Fourier representation (FR), is shown to be a special case of the proposed PRFBD. In addition, adaptive Fourier–Gauss decomposition (FGD) based on FR and Gaussian filters, and adaptive Fourier–Butterworth decomposition (FBD) based on Butterworth filters are developed as the other special cases of the proposed PRFBD method. The proposed theory of PRFBD can decompose any signal (time-series, image, or other data) into a set of desired number of Fourier intrinsic band functions (FIBFs) that follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters, is also presented. We performed an extensive analysis on both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches.

Modal Parameter Identification of Time-Varying and Weakly Nonlinear Systems Based on an Improved Empirical Envelope Method

The empirical envelope (EE) method based on the amplitude-modulation and frequency-modulation (AMFM) decomposition is effective for identifying the modal parameters of time-varying and weakly nonlinear systems. However, the identification accuracy of the EE method is sensitive to noises which often exist in vibration measurements of real structures. In this study, an improved empirical envelope (IEE) method is proposed to achieve robust modal parameter identification from noisy measurements. Specifically, the idea of sliding window threshold denoising is introduced to reduce the error in the instantaneous envelope induced by abnormal extreme points, and a moving average filter is utilized to reduce the error in the instantaneous frequency induced by high-frequency noises. Two numerical examples and an experimental example of a full-bridge aeroelastic model are analyzed to validate the accuracy of the IEE method and highlight the superiority of the IEE method relative to the original EE method. It is concluded that the IEE method is robust to measurement noises (the considered signal-to-noise ratios include 5–90[Formula: see text]dB) and that the IEE method is more accurate than the EE method. Hence, the IEE method serves as a promising alternative for modal parameter identification of time-varying and weakly nonlinear systems.

Integrated early fault diagnosis method based on direct fast iterative filtering decomposition and effective weighted sparseness kurtosis to rolling bearings

The fault vibration signal of rolling bearing is typical non-stationary and non-linear signals. It usually exhibits the characteristics of amplitude modulation and frequency modulation (AM-FM) due to the interaction between the various components within the bearing and modulation of the rotor speed. In addition, the incipient weak fault feature information is inevitably masked by the strong background noise and other vibration interference because of the influence of the working environment of mechanical equipment. Therefore, new high efficiency and effective processing methods are indispensable for feature extraction of bearing fault diagnosis signals. Direct fast iterative filtering (dFIF) is a new kind of adaptive mode decomposition method proposed by Antonio Cicone based on iterative filtering (IF) and fast iterative filtering (FIF). dFIF is used to quickly decompose multi-component signals into a set of intrinsic mode functions (IMFs) by means of fast Fourier transform (FFT). In order to study the decomposition performance and examine the feature extraction ability of the dFIF method, a group of harmonic signals with simple linear superposition, multi-components AM-FM signals with different data length and different signal-to-noise ratio (SNR) are simulated and analyzed. For comparison, other mode decomposition methods were also applied to the simulated signals, such as adaptive local iterative filtering (ALIF), local mean decomposition (LMD), and empirical mode decomposition (EMD). The results show that dFIF decomposition methods have faster decomposition speed and better accuracy than other methods. Furthermore, the results also demonstrate excellent performance in AM-FM feature extraction and anti-mode mixing. On the other hand, the effective weighted sparseness kurtosis (EWSK) index integrates the periodicity and intensity of impact in each mode, which can effectively identify the effective mode. Therefore, considering the above merits, EWSK is integrated on the dFIF to effectively accomplish the fault diagnosis for rolling bearing under strong background noise. Firstly, the raw vibration signals of rolling bearing fault are decomposed into a set of IMFs by using dFIF. The EWSK indicator is then utilized to select IMFs with rich bearing fault impulse information for reconstruction. Finally, the Hilbert envelope demodulation analysis is used to analyze the reconstructed signal, extract the bearing fault feature and then judge the fault type. This proposed method is applied to analyze the simulated and field measured vibration signals of rolling bearing faults. Simultaneously, the other above-mentioned adaptive TFA methods are also adopted to analyze the signals. The results compared show that the proposed method has excellent performance in the aspects of decomposition speed and accuracy under the influence of strong background noise. It demonstrates the effectiveness and applicability of the proposed method for signal processing and fault pattern recognition of rolling bearings.

Reconstruction of gapped missing samples based on instantaneous frequency and instantaneous amplitude estimation

The recovery of missing samples in amplitude-modulation and frequency-modulation (AM-FM) signals is a challenging problem. This study proposes an effective approach to reconstruct missing samples from AM-FM signals through accurate estimation of instantaneous frequencies (IFs) and instantaneous amplitudes (IA) of signal components. Experimental results indicate that the proposed reconstruction method achieves better performance, in the terms of mean absolute error between the reconstructed signal and the original signal, as compared to the state of art methods including time-frequency filtering method and modified orthogonal matching pursuit algorithm.

Characteristic ERK1/2 signaling dynamics distinguishes necroptosis from apoptosis

SummaryERK1/2 involvement in cell death remains unclear, although many studies have demonstrated the importance of ERK1/2 dynamics in determining cellular responses. To untangle how ERK1/2 contributes to two cell death programs, we investigated ERK1/2 signaling dynamics during hFasL-induced apoptosis and TNF-induced necroptosis in L929 cells. We observed that ERK1/2 inhibition sensitizes cells to apoptosis while delaying necroptosis. By monitoring ERK1/2 activity by live-cell imaging using an improved ERK1/2 biosensor (EKAR4.0), we reported differential ERK1/2 signaling dynamics between cell survival, apoptosis, and necroptosis. We also decrypted a temporally shifted amplitude- and frequency-modulated (AM/FM) ERK1/2 activity profile in necroptosis versus apoptosis. ERK1/2 inhibition, which disrupted ERK1/2 signaling dynamics, prevented TNF and IL-6 gene expression increase during TNF-induced necroptosis. Using an inducible cell line for activated MLKL, the final executioner of necroptosis, we showed ERK1/2 and its distinctive necroptotic ERK1/2 activity dynamics to be positioned downstream of MLKL.

Development of crack induced impulse-based condition indicators for early tooth crack severity assessment

Diagnosis of gearbox tooth cracks at an early stage is important to prevent catastrophic failures. The time synchronous average (TSA) of vibration signals of gearboxes with a tooth crack mainly consists of the gear meshing frequency (GMF) and its harmonics, the crack-related amplitude modulation and frequency modulation (AM-FM) and the crack induced impulse (CII). To conduct tooth crack diagnosis, many diagnostic techniques have been developed based on the TSA signal. However, most of them achieved poor performance in terms of assessing tooth crack severity in the early crack stage. The main problem is that they are not developed using the signal components that can well indicate early tooth crack severity progression. To overcome this problem, this paper proposes a method to conduct a thorough analysis on the CII since it provides more valuable information on tooth cracks than other components. In the proposed method, the GMF and its harmonics and the crack-related AM-FM are first removed from the TSA signal using comb notch filters. The notch filtered signal mainly contains components related to the CII. A modal model is adopted to fit the dominant resonances in the notch filtered signal and modal parameters are obtained using the matrix pencil method. With the modal parameter estimates, the dominant resonances, which are single degree-of-freedom impulse responses (SDOF IRs), are identified and used to reconstruct the CII. Besides, the SDOF IRs are grouped according to their carrier frequencies. The energy of the reconstructed CII and the sum of the energy of the SDOF IRs with carrier frequencies in a specific frequency band are proposed as two new condition indicators (CIs) for tooth crack diagnosis. The effectiveness of the proposed method and the new CIs for early tooth crack severity assessment are demonstrated using simulated gearbox vibration signals and experimental gearbox vibration datasets.

A Non-Intrusive Signal-Based Fault Diagnosis Method for Proton Exchange Membrane Water Electrolyzer Using Empirical Mode Decomposition

This work focuses on a signal-based diagnosis approach dedicated to proton exchange membrane water electrolyzer (PEM WE) anode pump fault. The PEM WE cell measurements are performed with an experimental test bench to highlight the impact of water flow rate in the anode compartment. This approach is non-intrusive, and it can detect anode flow rate variation during the electrolysis and is designed to fulfill online diagnosis requirements. Contrary to electrochemical impedance spectroscopy-based approaches (EIS), this method stands out from existing procedures as a result of its few requirements, excluding any signal with perturbing amplitude. Therefore, the electrolyzer remains continuously available, even while the analysis is performed. The empirical mode decomposition (EMD) is used to decompose the signal variation into a sum of amplitude modulation and frequency modulation (AM-FM) components, called intrinsic mode functions (IMFs). In this work, the PEM WE current signal is decomposed into several IMFs using EMD. Then, the energetic contribution of each IMF is calculated. Experimental results exhibited that the energetic contribution of IMFs can be used as relevant criteria for fault diagnosis in PEM WE systems. This process only requires monitoring of the PEM WE current and has a low computational cost, which is a significant economic and technical advantage.

Modulation Model of the Photoplethysmography Signal for Vital Sign Extraction.

This paper introduces an amplitude and frequency modulation (AM-FM) model to characterize the photoplethysmography (PPG) signal. The model indicates that the PPG signal spectrum contains one dominant frequency component - the heart rate (HR), which is guarded by two weaker frequency components on both sides; the distance from the dominant component to the guard components represents the respiratory rate (RR). Based on this model, an efficient algorithm is proposed to estimate both HR and RR by searching for the dominant frequency component and two guard components. The proposed method is performed in the frequency domain to estimate RR, which is more robust to additive noise than the prior art based on temporal features. Experiments were conducted on two types of PPG signals collected with a contact sensor (an oximeter) and a contactless visible imaging sensor (a color camera), respectively. The PPG signal from the contactless sensor is much noisier than the signal from the contact sensor. The experimental results demonstrate the effectiveness of the proposed algorithm, including under relatively noisy scenarios.

Analytical vibration signal model and signature analysis in resonance region for planetary gearbox fault diagnosis

Complex components and multi-modulations characterize planetary gearbox vibration signals. As the vibration signals become time-varying under nonstationary conditions, it is difficult to extract fault features around meshing frequency harmonics, since they are all dependent on speed, and change under time-varying speed conditions. To address this issue, we turn to the resonance region and exploit the time-invariability of resonance frequency and the sideband symmetry around resonance frequency for planetary gearbox fault diagnosis under both constant and time-varying speed conditions. Gear fault generates impulses and thereby excites resonance of the planetary gearbox and even the measurement system. As such, gear fault information can be explored in the resonance region. To thoroughly understand gear fault vibration features in resonance region, the vibration signal is modeled as an amplitude modulation and frequency modulation (AM-FM) process, considering the multi-modulations due to gear fault, time-varying vibration transmission path, and time-varying angle between mesh line of action and measurement axis of vibration sensor. Furthermore, explicit time-varying Fourier spectra under nonstationary conditions are derived. Sideband symmetry features around resonance frequency can be utilized to detect gear fault. This is a major progress and contribution in contrast to reported researches that focus on meshing frequency or its harmonics only. Resonance frequency identification is the key to gear fault feature extraction in resonance region. An on-line resonance frequency identification method under time-varying speeds is proposed by exploiting the nature of resonance independence of running conditions. To effectively pinpoint the time-varying sidebands in the time-frequency domain, the iterative generalized demodulation (IGD) method is used to achieve high time-frequency resolution and to avoid both outer and inner interferences at the same time. The theoretical derivations and proposed method are validated through numerical simulation and lab experiments. Gear fault features are extracted in the resonance region of the planetary gearbox and the accelerometer under time-varying speed conditions.

Characteristic ERK Signaling Dynamics Distinguishes Necroptosis from Apoptosis

ERK involvement in cell death remains unclear, although many studies have demonstrated the importance of ERK dynamics in determining cellular responses. To untangle ERK’s contribution in two cell death programs, we investigated ERK signaling dynamics during hFasL-induced apoptosis and TNF-induced necroptosis in L929sAhFas cells. We observed that ERK inhibition sensitizes cells to apoptosis while delaying necroptosis. By monitoring ERK activity by live-cell imaging using an improved ERK biosensor (EKAR4.0), we reported differential ERK signaling dynamics between cell survival, apoptosis, and necroptosis. We also decrypted a temporally shifted amplitude- and frequency-modulated (AM/FM) ERK activity profile in necroptosis versus apoptosis. ERK inhibition disrupted these ERK signaling dynamics and prevented TNF and IL-6 gene expression increase during TNF-induced necroptosis. Using inducible cell line for activated MLKL, the final executioner of necroptosis, we propose ERK and its distinctive necroptotic ERK activity dynamics to be positioned downstream of MLKL.

Permanent magnet synchronous generator stator current AM-FM model and joint signature analysis for planetary gearbox fault diagnosis

Wind energy is renewable and wind power generation facilities have been growing rapidly around the world. Permanent magnet synchronous generators (PMSGs) and planetary gearboxes are commonly used in wind turbines (WTs). For planetary gearbox fault diagnosis, PMSG stator current signal analysis provides a potential alternative approach because stator current signals contain fault signatures with easier accessibility comparing to conventional vibration signals. Planetary gearbox faults result in PMSG input torque oscillations, leading to both amplitude modulation and frequency modulation (AM-FM) effects on stator current signals. To better reveal gear fault features in PMSG stator current signals, this paper proposes to diagnose planetary gearbox faults from three complementary analysis results of the PMSG stator current, i.e., Fourier spectrum, amplitude demodulated spectrum and frequency demodulated spectrum. For this purpose, an AM-FM current signal model is derived through mechanical-magnetic-electric interaction analysis. Explicit equation of Fourier spectrum is derived with its sideband characteristics investigated. Both amplitude and frequency demodulation analyses are proposed to compensate for or mitigate the difficulty in the intricate sideband analysis of Fourier spectrum. Due to its capability in signal mono-component decomposition for accurate estimation of instantaneous frequency, adaptive local iterative filtering is utilized in frequency demodulation analysis. Explicit equations of corresponding demodulated spectra are derived, and gear fault signatures in PMSG stator current signals are revealed. The theoretical derivations and proposed methods are validated through lab experiments by diagnosing localized faults on the sun, planet and ring gears.

Combination of Amplitude and Frequency Modulation Features for Presentation Attack Detection

In this paper, we propose the combination of Amplitude Modulation and Frequency Modulation (AM-FM) features for replay Spoof Speech Detection (SSD) task. The AM components are known to be affected by noise (in this case, due to replay mechanism). In particular, we exploit this damage in AM component to corresponding Instantaneous Frequency (IF) for SSD task. Thus, the novelty of proposed Amplitude Weighted Frequency Cepstral Coefficients (AWFCC) feature set lies in using frequency components along with squared weighted amplitude components that are degraded due to replay noise. The AWFCC feature set contains the information of both AM and FM components together and hence, gave discriminatory information in the spectral characteristics. The experiments were performed on publicly available ASVspoof 2017 challenge version 1.0 and 2.0 databases using AWFCC feature set. We have compared results of proposed feature set with the other state-of-the-art feature set, such as Constant Q Cepstral Coefficients (CQCC), Linear Frequency Cepstral Coefficients (LFCC), Mel Frequency Cepstral Coefficients (MFCC) and using a simple Gaussian Mixture Model (GMM) classifier. The individual performance of AWFCC feature set obtained lower % EER than the other feature sets on both version 1.0 and 2.0 databases. Furthermore, we used score-level fusion in order to obtain the possible complementary information of two feature sets to reduce the % EER further. To that effect, the score-level fusion of CQCC and AWFCC feature sets gave 5.75 % and 10.42 % EER on development and evaluation sets, respectively, of ASVspoof 2017 version 2.0 database. Moreover, for evaluation dataset, we have also studied the performance of proposed feature set on different Replay Configurations (RC), namely, acoustic environments, playback, and recording devices. For all the levels of threat conditions (i.e., low, medium, and high) to the ASV system, the proposed feature set performed better compared to the existing state-of-the-art feature sets.

Induction Motor Stator Current AM-FM Model and Demodulation Analysis for Planetary Gearbox Fault Diagnosis

Induction motor-planetary gearbox drivetrains are widely used for industrial productions, including machine tools in manufacturing systems. For fault diagnosis of planetary gearboxes in such electromechanical systems, motor current signal analysis provides an effective alternative approach, because motor current signals have easier accessibility and are free from time-varying transfer path effects. Planetary gearbox faults generate load torque oscillations, leading to both amplitude modulation and frequency modulation (AM-FM) effects on induction motor current signals. To thoroughly understand gear fault features in current signals, an AM-FM current signal model is derived through mechanical–magnetic–electric interaction analysis, explicit equation of Fourier spectrum is derived, and sidebands characteristics are summarized. To avoid an intricate sideband analysis, amplitude and frequency demodulation analyses are proposed, explicit equations of corresponding demodulated spectra are derived, and gear fault features are summarized. The theoretical derivations are validated through lab experiments. Localized fault on the sun, planet, and ring gears are all successfully diagnosed using the proposed method.

Low bit-rate speech coding based on multicomponent AFM signal model

In this paper, we propose a novel multicomponent amplitude and frequency modulated (AFM) signal model for parametric representation of speech phonemes. An efficient technique is developed for parameter estimation of the proposed model. The Fourier–Bessel series expansion is used to separate a multicomponent speech signal into a set of individual components. The discrete energy separation algorithm is used to extract the amplitude envelope (AE) and the instantaneous frequency (IF) of each component of the speech signal. Then, the parameter estimation of the proposed AFM signal model is carried out by analysing the AE and IF parts of the signal component. The developed model is found to be suitable for representation of an entire speech phoneme (voiced or unvoiced) irrespective of its time duration, and the model is shown to be applicable for low bit-rate speech coding. The symmetric Itakura–Saito and the root-mean-square log-spectral distance measures are used for comparison of the original and reconstructed speech signals.

Detection of Residual Oil Film on Polished KDP Crystal by Atomic Force Microscope

In this paper, the bimodal amplitude-and frequency modulation method of atomic force microscopy (AFM) is applied to investigate the residual oil film on KDP crystal surface after polishing and surface cleaning. The thickness map of residual oil film is obtained based on the relationship between the cantilever resonant frequency shift and the surface stiffness. The thickness maps measured by AFM and spectroscopic imaging ellipsometry methods are compared, and the results obtained by two methods are consistent. The measurement scheme extends the application of AFM technology to characterize the residual oil on the KDP surface quality quantitatively after polishing and surface cleaning.