Type Of Discharge Research Articles

Sewage-and fertilizer-derived nutrients alter the intensity, diversity, and toxicity of harmful cyanobacterial blooms in eutrophic lakes

Cyanobacterial harmful algal blooms (CHABs) are promoted by excessive nutrient loading and, while fertilizers and sewage are the most prevalent external nutrient sources in most watersheds, the differential effects of these nutrient sources on CHABs are unknown. Here, we tracked CHABs and performed experiments in five distinct lakes across the Northern US including Lake Erie. Fertilizers with ammonium and orthophosphate, membrane (0.2 μm)-filtered sewage (dominated by reduced forms of nitrogen) sand-and membrane-filtered sewage (dominated by nitrate), and an inorganic nutrient solution of ammonium and orthophosphate were used as experimental nutrient sources for CHABs at N-equivalent, environmentally realistic concentrations. Phytoplankton communities were evaluated fluorometrically, microscopically, and via high throughput sequencing of the 16S rRNA gene, and levels of microcystin and the δ15N content of particulate organic nitrogen (δPO15N) were quantified. Fertilizer and both sources of wastewater increased the abundance of cyanobacteria in all experiments across all five lakes (p < 0.05 for all) whereas effects on eukaryotic phytoplankton were limited. Sand-filtered sewage contained less P, organic matter, and ammonium but more nitrate and had a 25% less potent stimulatory effect on cyanobacteria than membrane-filtered sewage, suggesting nitrification may play a role in reducing CHABs. Fertilizer increased microcystin levels and decreased the δPO15N whereas wastewater increased δPO15N (p < 0.05 for all). Microcystis was the genus most consistently promoted by nutrient sources (p < 0.05 in all experiments), followed by Cyanobium (p < 0.05 in 50% of experiments), with increases in Microcystis biomass consistently elicited by membrane-filtered wastewater. Collectively, results demonstrate that differing types of sewage discharge and fertilizers can promote CHAB intensity and toxicity, while concurrently altering CHAB diversity and δPO15N. While membrane-filtered sewage consistently favored Microcystis, the discharge of sewage through sands muted bloom intensity suggesting sand-beds may represent a tool to remove key nutrients and partially mitigate CHABs.

A collisional-radiative model for atmospheric-pressure low-temperature air discharges

Plasma-assisted combustion technology has been a hot spot in aero-engines and scramjet-engines. The electron density is a key discharge parameter related to the active-particle density. The latter has been considered playing an important role in the above applications by the kinetic effect. In this work, an atmospheric pressure air plasma collisional-radiative model considering the excited states of atomic nitrogen and oxygen is built based on previous widely kinetic investigations of molecules and radicals, as well as their excited states. The excited states, especially the atomic nitrogen and oxygen states were less investigated in previous works. The emission intensity distributions from the model have a good agreement with those measured in the glide arc plasma with two discharge modes, as well as the microwave plasma. Based on the kinetics of molecular and atomic emitting states, the line-ratio method is presented to determine the electron density. The N2(337 nm) / O(844 nm) and N2(337 nm) / NO(γ) line ratios are used for the glide arc plasma and microwave plasma torch, respectively. Besides, the kinetics of the excited states involved with two line-ratios are also investigated in the two types of discharges. Combined with the atmospheric pressure actinometry method, the kinetic effect of the plasma-assisted combustion can be revealed quantitatively in the future.

Investigation of highly efficient CO2 hydrogenation at ambient conditions using dielectric barrier discharge plasma

The increasing utilization of CO2 for synthesizing high-value fuels or essential chemicals is a potentially effective approach to mitigating global warming and climate change. Compared to thermal catalytic CO2 conversion under hash operating conditions (400∼500°C, 10 MPa), non-thermal plasma can overcome kinetic barriers and trigger reactions beyond thermal equilibrium at ambient temperature and pressure. In this study, the effects of operating conditions (discharge frequency, input power, and gas flow rate) and geometrical parameters (discharge length, discharge gap, and dielectric materials) have been extensively analyzed using typical cylindrical dielectric barrier discharge (DBD) plasma. The discharge characteristics changed by operating conditions (including waveforms of applied voltage and current) are compared, indicating higher applied voltage and lower gas flow rate can strengthen the filamentary discharges. The results demonstrate CO2 conversion rate increases with the increase of applied voltage and the decrease of CO2/H2 ratio, achieving its maximum value of 43.0% at 20 mL/min. The highest energy efficiency of 3771.9 μg/kJ for CO generation is obtained at the applied voltage of 5.5 kV and gas flow rate of 40 mL/min, respectively. Besides, the constructure of plasma reactor also impacts the performance of CO2 conversion. On the one hand, the discharge gap has a significant role in the variation of CO2 conversion and product selectivity, which is attributed to the electric field density and corresponding electron-induced reaction. On the other hand, the circulating water-cooling jacket was used to find out the influence of reaction temperature, which switched the product from CO to CH4. This work will pave the way for a sustainable alternative towards future CO2 conversion and utilization.

Discharge modes evolution characteristics of metal particle on the spacer surface in AC gas insulated switchgear

Abstract Flashover faults on gas insulated switchgear (GIS) insulators induced by metal particles occur frequently. Previous studies have obtained the characteristics of partial discharges (PDs) induced by metal particles on spacer surfaces, but these characteristics cannot explain the detection failure of ultra-high frequency (UHF) online monitoring. To enable further study of the PD characteristics induced by surface metal particles, the space electric field and a very-high-sensitivity pulse current (PC) measurement system were established. The electric field and PC characteristics of a PD induced by metal particle on the spacer surface of a 126 kV GIS were obtained. Two PD modes were found to be induced by surface metal particles. In addition to the typical pulse discharge (TP), there is a micro-discharge group (MG) mode with low apparent charge and long duration. The average apparent charge of the MG mode is approximately one-tenth of that of the TP at 0.4 pC. Its duration may extend to the millisecond level, causing significant distortion of the spatial electric field while hardly producing UHF signals. Moreover, increasing the applied voltage will increase the proportion of the MG within the total discharge, where this proportion can reach more than 90% before flashover, and the proportion of the discharge pulses that generates UHF signals is as low as 1%. The MG generation mechanism is analysed, the ion group stranded on the spacer surface by the TP changes the local electric field at the tip of the metal particle, which reduces the development length and apparent charge of the MG. Low apparent charge is cleared easily by the background electric field and thus the discharge interval is very short. This paper can provide an important basis for revealing the mechanism of GIS spacer surface discharge induced by metal particles and solving the effectiveness of PD monitoring devices.

Investigation into particle flow patterns during powder spreading in SLM process

Particle flow patterns and their variation mechanism during powder spreading in selective laser melting (SLM) are comprehensively investigated based on discrete element method. Two typical particle flow patterns, circulation and discharge, are identified. The patterns are found to alternate during powder spreading, and the underlying dynamical mechanism are clarified. Variations in the particle flow state across the gap induce variations in vertical velocities of particles in front of the blade, affecting the particle flow pattern. Effect of process parameters on flow pattern is studied, and the dynamical mechanism of the effect is analyzed in depth by two newly proposed indexes. Decreasing gap height, and increasing spreading speed and blade inclination, reduce the proportion of particles moving downward in shear band and enhance inter-particle forces near the gap, thus weakening discharge and intensifying circulation. This study provides a beneficial insight into the powder flow during powder spreading in SLM process.

Pulse dynamic regulation of electrochemical discharge milling by utilizing the slotted tube electrode

Electrochemical discharge machining (ECDM) has a splendid application potential for machining difficult-to-cut materials. It is a challenge to limit and control the discharge energy and area of DC power supply in ECDM. Owing to the uncertainty and randomness of the position and range of the single discharge, the unpredictable discharges deteriorate the surface quality of the workpiece, alter the size of the inter-electrode gap (IEG), and influence the distribution of the multi-physical fields. Therefore, to regulate the machining state and energy, the ideology of pulse dynamic machining is introduced, and a method of pulse dynamic ECDM utilizing the slotted electrodes is proposed. With the tool electrode rotating, the tube electrode transforms the pure electrochemical machining (pure-ECM) stage and the electrochemical discharge machining (ECDM) stage periodically through the slots at the bottom of it. The machining current waveform, surface roughness and sidewall taper of machined grooves, material removal rate (MRR), and relative tool wear rate (RTWR) are investigated. Additionally, the discharge types of the ECDM are explicitly defined and statistically classified. The experimental results show that the pulse dynamic regulation of hybrid machining using the slotted electrodes is beneficial to regularize the machining current waveform and optimize the machining quality.

Analysis on the relationship between coastal tourism and marine pollution: an empirical analysis of China’s 11 coastal regions

IntroductionCoastal tourism has become an important pillar of economic growth in China's coastal regions, yet no quantitative research has analyzed the relationship between coastal tourism and marine pollution.MethodsThis study, within a multivariate framework, comprehensively examines the impact of coastal tourism on marine pollution by employing various econometric techniques and focusing on four different types of marine pollutant discharges: chemical oxygen demand (COD), petroleum (PET), ammonia nitrogen (NHN), and total phosphorus (TP).Results and discussionPanel cointegration tests confirm a long-term relationship between coastal tourism and these four types of marine pollutant discharges. In the long run, coastal tourism has a significantly negative impact on COD, NHN, and TP. The results of Pooled Mean Group (PMG), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS) estimators show that for every 1% increase in coastal tourism revenue (TOUR), COD decreases by 0.734%, 0.536%, and 0.952% respectively; NHN decreases by 0.746%, 0.340%, and 1.633%; and TP decreases by 5.169%, 0.899%, and 0.334% respectively. However, the impact of coastal tourism on PET is not significant. The Dumitrescu-Hurlin (D-H) panel causality test results indicate different causality patterns between coastal tourism and various marine pollutant discharges. Specifically, there is a bidirectional causality between coastal tourism and COD, NHN, and a unidirectional causality between coastal tourism and PET, TP. Moreover, heterogeneity analysis reveals that coastal tourism does not significantly reduce all marine pollutant discharges in low-and middle-income coastal regions. Furthermore, compared to the central and southern coastal regions, the coastal tourism of northern regions has not significantly reduced marine pollution. This study can provide policymakers with references for developing coastal tourism and reducing marine pollutant discharges.

Study on medical dispute prediction model and its clinical-application effectiveness based on machine learning

BackgroundMedical dispute is a global public health issue, which has been garnering increasing attention. In this study, we used machine learning (ML) method to establish a dispute prediction model and explored the clinical-application efficiency of this model in effectively reducing the occurrence of medical disputes.MethodsRetrospective study of All disputes filed by Gansu Medical Mediation Committee from 2019 to 2021 and patients with the same hospital level as that of the dispute group and hospitalization year were randomly selected as the control group in 1:1 ratio. SPSS software was used for univariate feature selection of the 14 factors that may cause disputes, and factors with statistical differences were selected. The data were divided into training and test sets in a 7:3 ratio. Six ML models were selected, and Python was used to establish a dispute prediction model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, specificity, accuracy, precision, average precision (AP), and F1 score were used to characterize the fitting and accuracy of the models, while decision curve analysis (DCA) was used to evaluate their clinical utility.ResultsA total of 1189 patients in the dispute and control groups were extracted. Following 11 influencing factors were selected: the inpatient department, doctor title, patient age, patient gender, patient occupation, payment method, hospitalization days, hospitalization times, discharge method, blood transfusion volume, and hospitalization espenses. Compared to other models, the AUC (0.945, 95% CI 0.913–0.981), Sensitivity (0.887), Accuracy (0.887), AP (0.834), and F1 score (0.880) of the random forest model were higher than those of other models, while the DCA curve indicated its high clinical benefits.ConclusionsInpatient department, hospitalization expenses, and discharge type are the primary influencing factors of dispute. Random forest exhibited high dispute prediction and clinical-application value and is expected to be promoted for offline dispute prediction.

Modification of the Surface Crystallinity of Polyphenylene Sulfide and Polyphthalamide Treated by a Pulsed-Arc Atmospheric Pressure Plasma Jet.

Atmospheric plasma jets generated from air or nitrogen using commercial sources with relatively high energy densities are commonly used for industrial applications related to surface treatments, especially to increase the wettability of polymers or to deposit thin films. The heat fluxes to which the substrates are subjected are typically in the order of 100-300 W/cm2, depending on the treatment conditions. The temperature rise in the treated polymer substrates can have critical consequences, such as a change in the surface crystallinity or even the surface degradation of the materials. In this work, we report the phase transitions of two semicrystalline industrial-grade polymer resins reinforced with glass fibers, namely polyphenylene sulfide (PPS) and polyphthalamide (PPA), subjected to plasma treatments, as well as the modeling of the associated heat transfer phenomena using COMSOL Multiphysics. Depending on the treatment time, the surface of PPS becomes more amorphous, while PPA becomes more crystalline. These results show that the thermal history of the materials must be considered when implementing surface engineering by this type of plasma discharge.

Underwater surface discharge characteristics and multi-physical effects with conductive coating load under fast electric pulse

Abstract Underwater pulsed discharges, where intense reactions between ionized gas and condensed-state water exist, can be a joint problem of both physics and chemistry. The study tries to build a comprehensive visualization of nanosecond-risetime discharge initiated by a conductive coating and its successive multi-physical effects. The scenario is established via a pair of thin-plate electrodes positioned on both sides of the coating, and diagnosed using high-speed backlight photography synchronized with electrical and optical measurements. For the sprayed Cu/Ag composite coating, the current density can achieve 20 A/mm2 which is high enough to induce the surface “electrical explosion” and breakup the conductive matrix within 500 ns. By increasing the discharge energy from 0.5 to 10 J, the explosion of coating can exhibit different discharge types as exploding wires. Adopting a thicker carbon foil or cermet sheet can reduce the current density and energy deposition rate, which converts the global explosion to partial ones, significantly increasing the lifetime. With the aid of the conductive coating, the breakdown delay diminishes, and hot plasma spots form in 100 ns due to non-uniform Joule heating of the pulsed current, which gradually evolve to a plasma bubble cluster above the lower-conductive coating (bypassing branch). Once the high-conductive plasma channel bridges two electrodes, it will be intensively heated (MW-level energy deposition rate) and rapidly expand, accompanied by underwater shock wave (102 kPa @30 cm) and bubble/cavity generation (20 mm maximum). Finally, microscopic characterization has been made, and the erosion morphology suggests typical arc erosion features (pits, cracks, etc.) and nanoparticles condensation from evaporated materials.

Typical Partial Discharge Signal Acquisition and Feature Extraction in High Voltage Switchgear

Typical Partial Discharge Signal Acquisition and Feature Extraction in High Voltage Switchgear

Identification of hidden danger discharges in transmission lines based on multi-scale convolutional neural network

Abstract The presence of hidden dangers in transmission lines poses a significant threat to the safe and stable operation of the power grid. Timely identification of these hidden dangers is crucial to simplify maintenance tasks for the operation department. This paper introduces a model based on a Multi-Scale Convolutional Neural Network (MSCNN) to identify the discharge types of hidden dangers in transmission lines. Initially, the Intrinsic Mode Functions (IMFs) of the hidden danger discharge signal measured from the transmission line are obtained through Variational Mode Decomposition (VMD). Subsequently, multi-scale time-frequency features within the IMFs are extracted by MSCNN. Finally, a Softmax classifier completes the task of identifying the type of hidden danger discharge. The training and testing results of the model demonstrate that its accuracy in identifying the four types of hidden dangers researched in this paper can reach 90.38%.

Cathode cooling effects on the neutron production rate in the glow discharge type of fusion neutron source

Fusion reactions on the cathode surface of glow discharge deuterium–deuterium fusion neutron sources contribute significantly to the neutron production rate (NPR). While the NPR shows a linear relationship with current in the low current regime, a rise in cathode temperature in the high-current regime causes stagnation of the NPR. This tendency may be caused by high-temperature-induced desorption of deuterium on the cathode. This study aims to clarify the relationship between NPR and deuterium desorption. The present study utilized a water-cooling system to prevent deuterium desorption on the cathode. A stainless-steel 304 cathode and a diamond-like carbon (DLC)-coated cathode were tested. The cooling system kept the cathode temperature below 315 K throughout the experiment. In the case of the DLC-coated cathode, the water-cooling system improves the NPR in a high-current regime (30 mA or more in the present study). At 50 kV and 60 mA, the NPRs were 1.87 × 106 and 8.39 × 105 (n/s), with and without water cooling, respectively. Furthermore, without the cooling system, the NPR correlation with the cathode temperature indicates good agreement with the estimation model of deuterium desorption on the DLC-coated cathode. This study demonstrates that suppression of deuterium desorption in the cathode improves NPR, especially in the high-current regime.

Reasons for Discharge in a National Network of Early Psychosis Intervention Programs.

Discharge from early psychosis intervention is a critical stage of treatment that may occur for a variety of reasons. This study characterizes reasons for discharge among participants in early psychosis intervention programs participating in the Early Psychosis Intervention Network (EPINET) which comprises >100 programs in the United Statesorganized under 8 academic hubs. We analyzed 1787 discharges, focusing on program completion, unilateral termination by the client/family, and lost contact with the client/family. We performed exploratory analyses of demographic, clinical, and functional predictors of discharge reason. Variables predictive of discharge type were included in multilevel logistic regressions, allowing for the estimation of predictors of discharge reason and variability in rates by program and hub. An estimated 20%-30% of enrolled patients completed the program. Program completion rates were higher among participants who were older on admission, had lower negative symptoms severity, spent more time in education, employment, or training, and who were covered by private insurance (a close proxy for socioeconomic status). Programs were more likely to lose contact with male participants, Black participants, and participants who were never covered by private insurance. After accounting for patient-level factors, there was substantial program-level variation in all 3 discharge outcomes, and hub-level variability in the proportion of participants who completed the program. The impact of race on program completion varied substantially by program. Participants were discharged from early psychosis intervention services for diverse reasons, some of which were associated with sociocultural factors. Disengagement is a widespread problem affecting all hubs.

Impact of non‐thermal plasma generated using air and nitrogen on functional properties of milk protein dispersions

AbstractThis study focuses on improving the functionalities of milk protein concentrate (MPC) using a nonthermal plasma jet and plasma‐activated water (PAW) with atmospheric air and nitrogen as source gases. The 5% and 10% MPC dispersions were directly treated with a plasma jet, and PAW was used to make the MPC dispersions. The dispersions were analyzed for changes in protein structure and functional properties. The treatment altered the secondary structure of MPC protein structure by increasing β‐components and changing the order of random coils. The solubility of the PAW‐treated 5% protein dispersions doubled due to plasma‐induced modification of hydrophilic and covalent bonds of the protein, but this increase was not significant for the 10% dispersions due to less hydration. Emulsifying capacity increases by around 7% for plasma jet and PAW with air, owing to hydrophobicity on the particle surface. The gelation capacity and heat coagulation time rise by almost twice; however, foaming capacity decreases, indicating protein structural modifications and aggregations caused by plasma exposure. The viscosity of the 5% dispersions decreased due to high solubility, while that of the 10% dispersions increased due to less hydration. Principle component analysis was used to correlate the change in functionality with different operating parameters. In conclusion, this study illustrates that the functional properties of MPC can be significantly modified through plasma treatment. The observed changes depend on several factors, including the mode of plasma exposure, the source gas used to generate the plasma, and the concentration of the protein solution.Practical applicationsMilk protein concentrate (MPC) is a promising ingredient in food products such as cheese, cultured dairy items, nutritional goods, infant milk formulas, ice cream, dairy‐based drinks, sports beverages, and a variety of health‐focused products. However, the application is profoundly hindered due to its poor solubility and solubility‐related functional properties. MPC powders lose functionalities during manufacturing and gradually lose them again during processing and storage. This study explores the potential of non‐thermal plasma to improve the functional properties of proteins. This study explores the potential of two different discharges: plasma jet and plasma‐activated water (PAW), using atmospheric air and nitrogen as source gases. The findings of this study would also help to understand the types of plasma discharge (jet vs PAW) and types of sources of gases that can be used in industrial applications. Some optimization parameters of this study can be used for scaling up the plasma processing of milk or dairy products. In addition, PAW is being very well studied to improve food safety, and this study would provide information on the physicochemical properties of MPC. Therefore, it would also help to understand how to apply non‐thermal plasma to achieve both microbial and physical effects.

Research and Simulation of Partial Discharge Phenomenon Sensing Technology for Substation Inspection Robots

Abstract This paper thoroughly investigates the phenomenon of partial discharge in substations and proposes a set of simulation experimental apparatus to replicate various types of partial discharge defects in switchgear. By modifying the switchgear, incorporating models for different types of partial discharge, and equipping various standard partial discharge sensors, accurate simulations of phenomena such as point discharge, suspended discharge, gap discharge, and particle discharge are achieved. Through this system, the effectiveness of different detection methods, such as ultrasonic, high-frequency, and transient voltage methods, is validated for various defect models. Furthermore, utilizing an indoor inspection robot guide rail, the paper simulates the environment of partial discharge in substations, enabling the validation of the robot’s partial discharge detection performance and typical patterns under different conditions. This work is dedicated to providing situational awareness technology for partial discharge phenomenon detection for substation inspection robots, so that the inspection robot can detect various abnormal partial discharge phenomena in transformers or other substation equipment through micro sensors to promote the stable operation of the substation.

Classification of Multi-Source Partial Discharge in GIS

Abstract Partial discharge (PD) analysis is an important method to identify electrical equipment faults. However, due to the influence of environmental electromagnetic interference and other equipment discharges in the power supply circuit during the on-site PD test, the collected PD signal is the combination of various PD and noise, which makes it challenging to identify the type or source of on-site partial discharge. A multi-source PD classification method based on the multiple threshold and K-means clustering algorithm is pro-posed to solve this problem. First, wavelet transform with an improved threshold is used to initially denoise the original signal. Then the multiple threshold method is used to filter the residual ripple between every two pulses. Finally, spectrum analysis is carried out for each pulse, and the maximum value of the spectrum is extracted as the feature quantity. Then, the K-means algorithm is used to classify the PD pulses, and the PRPD images of PD pulses in different frequency domains are obtained. The results show that the method adopted in this paper can denoise, extract and classify the PD signals collected on the site, and the PRPD diagram of PD pulse after classification is more obvious, which provides a basis for identifying the PD source on the site.

Gap distance of UHV substation in areas with an altitude of 5000 m and below

Abstract In view of the increasing energy consumption and power demand, the construction of UHV AC is growing rapidly, and the construction of the project is gradually advancing to higher altitudes. It is urgent to conduct research on typical gap discharge in substations at high altitudes. Combined with the engineering requirements, this paper corrects the test data at an altitude of 0 m according to GB/T 24842 standard through altitude correction and combines the discharge voltage requirements of various typical gaps in substations to obtain the minimum air gap distance in UHV substations at an altitude of 5000 m and below. The calculation results of the two altitude correction methods are compared and analyzed, and the applicability of the two methods is analyzed.

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Discharge plasma parameter measurement is a key focus in low-temperature plasma research. Traditional diagnostics often require costly equipment, whereas electro-acoustic signals provide a rich, non-invasive, and less complex source of discharge information. This study harnesses machine learning to decode these signals. It establishes links between electro-acoustic signals and gas discharge parameters, such as power and distance, thus streamlining the prediction process. By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques, the Mel-Frequency Cepstral Coefficients (MFCCs) of the acoustic signals are extracted to construct the predictors. Three machine learning models (Linear Regression, k-Nearest Neighbors, and Random Forest) are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance. All models display impressive performance in prediction precision and fitting abilities. Among them, the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error (MSE = 0.00571) and the highest -squared value ( ). The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm, which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

GIS Partial Discharge Pattern Recognition Based on IWOA-SVM Algorithm

Abstract To effectively recognize partial discharge (PD) types in gas-insulated switchgear (GIS) equipment, a novel PD pattern recognition method is proposed. This method leverages an improved whale optimization algorithm (IWOA) to optimize the support vector machine (SVM). Firstly, typical PD defects were simulated in the GIS chamber. The PRPS results demonstrated significant differences among four PD defects. Secondly, multiple feature values of PD results were calculated for feature extraction. Finally, the IWOA-SVM algorithm was employed for the recognition of PD patterns in GIS. Experimental results indicate that the IWOA-SVM algorithm can effectively recognize different PD types and the predicted accuracy rate can reach 99.1667%, which is much higher than SVM and WOA-SVM algorithms, providing information to construction units for maintenance.