Research Progress on Aging Detection of Composite Insulators Based on Spectroscopy

Abstract

The safety of composite insulators in high-voltage transmission lines is directly related to the stable operation of the power system, which is a fundamental condition for the normal functioning of people’s lives and industrial production. Composite insulators are exposed to outdoor conditions for extended periods of time, and with the increase in service life, they are subjected to aging due to external environmental factors and electrical stresses. This aging leads to a decline in their electrical insulation, mechanical properties, and other performance, which, in severe cases, may result in power system failures. Therefore, accurate assessment and detection of the aging status of composite insulators are particularly important. Traditional detection methods such as visual inspection, hardness testing, and hydrophobicity testing have limitations, including single functionality and susceptibility to environmental interference, which cannot comprehensively and accurately reflect the aging condition of the insulators. In recent years, spectroscopy-based detection technologies have been increasingly applied for the rapid detection of composite insulators due to their advantages, such as high sensitivity, non-contact measurement, and multi-dimensional information extraction. Common spectroscopic detection methods include Ultraviolet Discharge (UV Discharge), Fourier Transform Infrared (FTIR) Spectroscopy, Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Laser-Induced Breakdown Spectroscopy (LIBS), and Terahertz (THz) Spectroscopy. These methods offer non-contact, remote, and rapid capabilities, enabling detailed analysis of the insulator’s surface microstructure, chemical composition, and aging characteristics. This paper introduces = spectroscopy-based methods for detecting the aging status of composite insulators, analyzing the advantages and limitations of these methods, and discussing the challenges of their industrial application. Furthermore, the paper reviews the research progress and practical applications of spectroscopic techniques in the evaluation of insulator aging status, systematically summarizing important achievements in the field and providing an outlook for future developments.

1. Introduction

Electrical power systems are a lifeline in modern society. Insulators, as critical components within power transmission networks, perform essential functions. They ensure the stable operation of electrical equipment and underpin efficient, reliable energy transmission [1]. An insulator is a key device designed to isolate conductors at different electrical potentials or from grounded structures. It withstands both voltage stress and mechanical loads, finding extensive application in overhead transmission lines [2]. Structurally, it comprises an insulating body and connection hardware. Based on the insulating material, insulators are primarily classified into ceramic, glass, and composite types [3]. Ceramic insulators offer excellent thermal resistance and chemical stability, making them suitable for high-voltage transmission lines (10–500 kV). However, they are heavy and brittle [4]. Glass insulators exhibit strong pollution resistance and are used in 35–500 kV lines, yet they are also relatively heavy and less adaptable for long spans [5]. In contrast, composite insulators are significantly lighter than traditional counterparts of the same rating, which greatly reduces transportation and installation costs, making them particularly advantageous in seismically active regions. Under identical pollution conditions, they demonstrate higher flashover voltages and markedly enhance grid reliability in humid environments such as rain, snow, and fog, thereby reducing outage risks. Unlike the rigidity and brittleness of ceramic and glass materials, composite insulators feature a flexible design that localizes damage under impact and prevents catastrophic failure, thus substantially improving safety and reliability. Owing to these advantages, composite insulators have become the preferred option in many applications. With their light weight, high seismic resistance, and superior aging performance, they are increasingly emerging as the mainstream choice. These differences also shape the applicability of spectroscopic detection: traditional insulators mainly show surface defects, while composite insulators exhibit aging-related chemical changes that can be effectively monitored by FTIR and LIBS. A composite insulator consists of silicone rubber weather sheds, a glass fiber-reinforced core rod, and metal end fittings [6], as illustrated in Figure 1. The weather sheds provide electrical insulation and pollution flashover resistance, the core rod bears mechanical loads, and the metal end fittings ensure mechanical strength and stable electrical connection [7]. Driven by the power industry’s demand for high-performance transmission equipment, composite insulator materials have evolved from ethylene propylene diene monomer (EPDM) to silicone rubber [8]. This transition significantly enhanced pollution flashover resistance and aging performance, enabling their application across a wider voltage range (10–1000 kV). Consequently, they are now widely deployed in transmission lines operating under severe pollution, high humidity, and long-term conditions [9]. However, long-term exposure to external factors during operation—including mechanical stress, thermal aging, ultraviolet radiation, and environmental pollution [10]—inevitably causes surface material aging in composite insulators [11]. This degradation compromises insulator performance, shortens service life, and can trigger issues like partial discharge, leading to significant economic losses [12]. Therefore, regular assessment of the aging state of in-service composite insulators and their sheath materials has become a critical focus for transmission line operators.

Current aging detection methods for composite insulators include visual inspection, hydrophobicity assessment [13], thermally stimulated current (TSC) measurement [14], and electric field distribution measurement [15]. Visual inspection identifies surface physical characteristics but cannot detect chemical changes induced by aging [16]. Hydrophobicity assessment primarily employs the spray method (HC classification) or static contact angle measurement. The spray method suffers from subjectivity [17], while static contact angle measurement demands stringent environmental conditions and involves complex procedures [18]. Voltage- and current-based methods utilize sensors to measure leakage current or electric field distribution along the insulator [19]. A data acquisition system records and analyzes these characteristics to assess insulator condition. While relatively straightforward to implement and effective for detecting early-stage faults, these techniques are susceptible to environmental factors like temperature and humidity [20]. These conventional detection methods exhibit limitations, including functional narrowness, environmental sensitivity, and high operator dependency. Consequently, they are generally unsuitable for industrial online monitoring requirements.

Spectroscopy serves as a vital technique for investigating material properties. Its fundamental principle relies on the interaction between matter and electromagnetic waves of varying frequencies (or wavelengths) to extract information such as electronic energy levels, molecular vibrational and rotational states, particle structure and symmetry, and transition probabilities [21]. Detection methods based on spectroscopy offer unique advantages, including rapidity, efficiency, and high sensitivity, leading to their widespread application in fields such as biomedical diagnostics and environmental monitoring [22]. Spectroscopic detection methods for composite insulators are emerging, primarily focusing on measuring and analyzing key changes during aging, such as micro-morphology, component mass, and chemical groups within the silicone rubber weather sheds. Common spectroscopic techniques include ultraviolet discharge imaging (UV Discharge) [23], Fourier transform infrared spectroscopy (FTIR) [24], hyperspectral imaging (HSI) [25], Raman spectroscopy (RS) [26], laser-induced breakdown spectroscopy (LIBS) [27], and terahertz spectroscopy (THz) [28]. We compare the advantages and disadvantages of common traditional methods and spectral methods for testing composite insulators in Figure 2.

This review begins by introducing the structure, applications, and development of composite insulators. It then elucidates the fundamental principles of spectroscopy and the specific working mechanisms of various spectroscopic techniques for insulator aging detection. Furthermore, it summarizes recent research advances in spectroscopic aging detection for composite insulators and discusses the application potential of each technique. Building on this foundation, the review thoroughly examines the limitations of these methods and provides forward-looking insights. Finally, the advantages and disadvantages of the various methods are summarized and analyzed, and the future development of spectroscopic technologies in the field of electrical equipment health monitoring is discussed.

2. Ultraviolet Discharge Detection Method

Under high-voltage operating conditions, insulator surfaces exhibit physical phenomena such as partial pulse discharge and corona discharge as aging progresses. These discharge processes involve plasma formation. Leveraging the radiative properties of plasma, engineers typically capture radiation signals within the solar-blind ultraviolet (UV) band (200–280 nm) to characterize discharge activity [29]. By analyzing the correlation between parameters like radiation intensity, pulse frequency, and the degree of insulator aging, assessment models are established [30]. A detection system initially employs solar-blind UV sensors to acquire characteristic parameters from the insulator surface, including radiation location, pulse count, and intensity distribution. Subsequently, by leveraging the mapping relationship between these parameters and aging severity, the system enables insulator condition diagnosis, defect localization, and overall state assessment of composite insulators [31].

Research on UV pulse detection for surface discharge on electrical equipment commenced in the early 1990s. Subsequent studies have conducted in-depth analyses of the link between UV spectra and insulator aging. Yang Ji, Xu Tao et al. (2007) [32] successfully captured abnormal discharge pulses caused by insulator aging, laying the groundwork for early-warning aging detection. Dai Rijun’s team (2011) [33] investigated spark discharge and partial arc discharge phenomena in composite insulators induced by aging. Their results demonstrated an approximate linear relationship between optical signals and current signals, as shown in Figure 3, providing a solid foundation for subsequent research on measuring discharge intensity via light intensity. Zhu Jinrun et al. (2018) [34] validated the feasibility of UV imaging through finite element simulation experiments. They determined discharge locations by correlating electric field distribution with UV radiation field distribution, enabling the identification of aging and contamination sites on insulators Zunshou Li et al. (2022) [35] designed a drone-based UV inspection system for insulator aging, equipped with solar-blind UV sensors, achieving precise detection of corona discharge locations. The same year, Saiful Mohammad et al. [36] measured surface discharge on insulators of varying service ages (0–20 years) installed on the same 132 kV transmission line using UV pulse sensors. Their experimental results revealed that the DC component of the UV signal increases with insulator age, and discharge intensity exhibits a positive correlation with both the DC and harmonic components of the UV pulse signal across different service durations. Addressing the limitations of traditional ground-based or fixed detection methods—namely limited coverage and low efficiency—Xiang Lin et al. (2023) [37] proposed a UAV-mounted solar-blind UV sensor solution for distribution line discharge monitoring. Compared to ground-based handheld equipment, this approach captured more discharge signals and precisely located discharge points, demonstrating the feasibility of remote technology for field applications. Abdul Rahim et al. (2023) [38] adopted a UV pulse sensor (UVTRON) to monitor surface discharges on glass and porcelain pin-type insulators, and the results showed that the peak-to-peak value of the UV signal varies with discharge intensity, enabling continuous detection of discharge activities under both dry and wet conditions. Subsequent research focused on algorithmic improvements. JinXiu He et al. (2024) [39] proposed a quantitative characterization method for UV-imaged hotspots on insulator surfaces. By integrating noise-suppressed UV imaging with radar imaging technology and introducing dual feature parameters (shape coefficient and pulse width), they addressed the distortion in electric field distribution characterization caused by single-frame detection Gustavo’s team (2025) [40] further developed an automatic UV image processing framework for insulator corona discharge monitoring. Utilizing a specialized Convolutional Neural Network (CNN) for object detection, their model achieved a precision of 85.5%.

In terms of degradation phenomena, UV-based detection mainly corresponds to surface discharge processes caused by insulation deterioration, which are often manifested as corona or arc activities on aged insulator surfaces. UV detection methods capture partial discharges triggered by insulator aging, offering advantages of high sensitivity and non-contact measurement. However, their primary limitation is that detection is only possible after discharge events occur. These methods cannot assess aging states through proactive data collection and analysis, rendering them more suitable for rapid post-fault localization. Furthermore, they face bottlenecks such as poor environmental adaptability, weak electromagnetic interference resistance, and insufficient long-term characteristic data. Critically, they are incapable of detecting internal aging. Consequently, UV detection methods are typically combined with other diagnostic techniques to comprehensively assess insulator condition.

Looking forward, future research should focus on enhancing environmental robustness, integrating real-time monitoring systems, and establishing standardized discharge–aging correlation models. In addition, coupling UV detection with other optical or electrical diagnostic tools, as well as incorporating artificial intelligence for discharge pattern recognition, will be essential for enabling proactive and predictive condition assessment of composite insulators.

3. Fourier Transform Infrared Spectroscopy

The silicone rubber material used in composite insulator weather sheds primarily consists of polydimethylsiloxane (PDMS), a high-molecular-weight polymer. With prolonged service life, scission of Si-O and Si-CH₃ bonds in the surface molecular structure alters the material’s microstructure. This degradation compromises key properties such as hydrophobicity, manifesting as aging. Therefore, changes in surface chemical groups (e.g., Si-CH₃ and Si-O) can be analyzed via infrared spectroscopy to assess insulator aging [41]. Fourier Transform Infrared (FTIR) spectroscopy represents the most prevalent infrared analysis technique. Its system schematic is illustrated in Figure 4. The system generates interferometric light through a Michelson interferometer. After interaction with the sample, the transmitted light—carrying molecular vibrational information is captured by a detector. Following signal amplification, filtering, and analog-to-digital conversion, a Fast Fourier Transform (FFT) algorithm converts the time-domain interferogram into a frequency-domain infrared spectrum [24].

In 2005, Ehsani et al. [42] employed Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy to monitor composite insulators during simulated UV aging. Their results demonstrated that UV radiation primarily causes Si-O bond scission, while high-voltage electric fields degrade Si-C bonds. This aging process is accompanied by the generation of -OH groups. Zhang, Tu et al. (2012) [43] revealed a nonlinear progression of aging rates with service time: initial slow degradation accelerates before decelerating. Surface aging progresses faster than bulk degradation, with more pronounced cleavage of organic side chains. Liang Ying et al. (2015) [44] established that the absorption peak area of Si-CH₃ groups effectively assesses early-stage aging, while Si-O-Si peaks better indicate advanced degradation. Cheng Li et al. (2016) [45] developed a statistically based lifetime prediction model, achieving prediction errors below 5.2% in experimental validation. Zhijin Zhang et al. (2022) [46] quantitatively characterized aging in harsh environments (high-altitude, salt-fog, acid-etching) using the -CH3/Si-O-Si absorption peak height ratio (H-factor). They confirmed an inverse correlation between the H-factor and aging severity, directly linking functional group changes to degradation levels. Yang et al. (2024) [47] employed ATR-FTIR spectroscopy and correlation spectroscopy to analyze electrical tracking aging in silicone rubber. Quantitative analysis of surface functional group changes and contact angle reduction indicated significant effects on the main polymer chain and enhanced surface oxidation. Liang et al. (2007) [14] correlated hydrophobicity loss in aged insulators with chemical changes: decreased Si-CH₃ (1259 cm⁻¹) content and increased -OH groups, directly explaining the material’s reduced water repellency. Wang Yixin et al. (2024) [48] identified that prolonged corona and UV exposure reduces Si-CH₃ peaks while generating oxidative products (C=O at 1700 cm⁻¹, -OH). Accumulation of these products increases hardness and degrades mechanical properties. Zhenan Zhou et al. (2025) [49] proposed a Multi-scale Depthwise Separable Convolution Attention (MDSC-Attention) network. This architecture automatically extracts critical features from FTIR data, achieving 99.68% accuracy in aging classification. FTIR spectroscopy elucidates the molecular-level mechanisms underlying surface degradation in aging composite insulators, providing a vital theoretical foundation for insulation condition assessment.

In terms of degradation manifestations, FTIR spectroscopy is primarily sensitive to chemical bond breakage, molecular chain scission, and the formation of new functional groups in the polymer matrix, which correspond to morphological degradation and surface oxidation processes commonly observed in aged insulators. As a complementary technique, FTIR-based aging assessment offers significant advantages: simplicity, non-destructiveness, speed, efficiency, and non-contact operation. It provides robust support for early warning, condition assessment, and aging mechanism studies. However, limitations persist in quantitative accuracy and detection of deep bulk aging.

Future research directions should emphasize improving quantitative calibration models, enhancing penetration depth for bulk aging detection, and combining FTIR with complementary spectroscopic or thermal techniques. Additionally, integrating FTIR analysis with advanced data-driven methods, such as machine learning for spectral feature extraction, could enable more accurate prediction of long-term aging behavior and provide stronger support for intelligent life-cycle management of composite insulators.

4. Hyperspectral Imaging Technology

Hyperspectral imaging (HSI) is an integrated imaging and spectroscopy technique. Its core principle lies in simultaneously acquiring spatial information and spectral signatures from a target sample. Different chemical constituents on the sample surface exhibit distinct reflectance characteristics across various electromagnetic wavebands, endowing HSI with a unique “spectral fingerprint” capability [50]. By recording the surface reflectance across numerous wavelengths, HSI constructs a three-dimensional data cube (comprising two spatial dimensions and one spectral dimension). This enables the simultaneous characterization of both physical morphology and chemical composition [51]. A schematic of the HSI system for insulator inspection is shown in Figure 5. The experimental platform comprises a hyperspectral camera, a standard calibration white reference panel, an illumination source, a computer, and associated data acquisition/processing software [52]. During operation, the sample and reference panel must reside on the same plane to ensure accurate reflectance calculation.

Hyperspectral technology for insulator detection primarily focuses on efficient feature extraction from spectral-spatial data and advanced modeling approaches. In spectral-spatial feature extraction, Runming Gao et al. (2019) [53] processed spectral data using flat-field correction and image enhancement. They identified an increasing peak at 750 nm and decreasing near-infrared reflectance with aging time, significantly improving the visualization of aging states. Changjie Xia et al. (2021) [54] combined the Canny operator for region detection with Multiplicative Scatter Correction (MSC) and Principal Component Analysis (PCA) to enhance spectral separability. Using Successive Projections Algorithm (SPA), they extracted six target wavebands, then integrated Linear Discriminant Analysis (LDA) to boost computational efficiency and accuracy—critical for HSI-based rapid remote insulator diagnostics. Yiming Zhao et al. (2021) [55] extracted crack edges from hyperspectral data and implemented an EfficientNet model for ceramic insulator crack classification, achieving 96.9% accuracy. This validated HSI’s effectiveness in crack detection and expanded aging assessment capabilities. For data modeling, Kexin Lin (2023) [56] applied Savitzky–Golay filtering to reduce spectral noise. By implementing Direct Standardization algorithms, they successfully transferred laboratory models to field conditions, improving outdoor detection accuracy by approximately 50%. Yihan Fan (2019) [27] analyzed hyperspectral profiles across six aging levels. Using preprocessing techniques including Segmented Principal Component Analysis (SPCA), 14 critical features were extracted. Among evaluated models, Random Forest (RF) achieved superior performance (96.81% accuracy) and enabled pixel-level aging state mapping of composite insulators, as illustrated in Figure 6. Hapreet et al. (2025) [57] demonstrated that spectral–spatial feature fusion can effectively identify samples at different aging levels, achieving an overall accuracy of 98.3%. Yujun Guo et al. (2025) [58] simulated coastal humid-hot environments to prepare six aging-grade samples. They developed a multi-strategy Improved Dung Beetle Optimizer (IDBO) to enhance Bidirectional Gated Recurrent Unit (BiGRU) classification, attaining 95.56% Overall Accuracy (OA)—establishing an effective method for complex-environment aging assessment.

In engineering applications, Chaoqun Shi (2020) [59] conducted hyperspectral imaging on insulators operating in sandy environments. They developed a Support Vector Machine (SVM) model for surface roughness discrimination that achieved 98% accuracy on test samples, enabling non-contact roughness detection for roof-mounted insulators on high-speed trains. Chengfeng Yin (2021) [60] acquired in situ surface images and spectral features of grid insulators. Integrated spectral-spatial analysis achieved 95% accuracy in pollution classification—an 18.75% improvement over spectral-only models—effectively supporting grid operators in planning precise maintenance or replacement schedules. That same year, Lincong Chen [61] created a portable hyperspectral camera (400–1000 nm range) with companion mobile application. Its embedded SVM prediction model attained 96.09% accuracy, delivering a cost-effective field solution for on-site aging assessment of composite insulators. Yihan Fan (2019) [27] integrated HSI with deep learning, developing an RF-PCA-CNN aging assessment model that achieved 94.44% classification accuracy for rapid evaluation of insulators in acidic environments.

In terms of degradation manifestations, hyperspectral imaging is particularly sensitive to surface morphological changes, contamination, and thermal radiation differences that occur during the aging of composite insulators. These spectral signatures often correspond to micro-crack formation, surface erosion, and hydrophobicity loss. Collectively, hyperspectral-based insulator aging detection demonstrates significant potential with advantages including non-contact operation, long-range capability, and high crack identification accuracy. Nevertheless, the high dimensionality of hyperspectral data demands more efficient noise reduction, dimensionality reduction, and analytical algorithms to advance processing and modeling capabilities.

Looking ahead, future research should focus on developing advanced feature extraction and fusion algorithms, optimizing spectral band selection for aging-related indicators, and improving robustness under varying outdoor environmental conditions. Furthermore, combining hyperspectral imaging with real-time monitoring frameworks and artificial intelligence will be crucial for enabling automatic defect recognition and predictive evaluation of insulator health status.

5. Laser-Induced Breakdown Spectroscopy

Aging in composite insulators involves physicochemical changes within the silicone rubber matrix, such as polymer chain scission. These alterations modify elemental composition and distribution characteristics [62]. Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a rapid elemental characterization tool for composite insulator aging assessment. A typical LIBS-based aging detection system is illustrated in Figure 7. The LIBS technique focuses a laser beam to ablate the sample surface, generating plasma whose emitted spectrum is analyzed to identify elemental composition and quantify concentration changes [63]. By probing elemental migration across depths and mapping surface distribution, LIBS enables investigation of elemental redistribution and microchemical transformations during aging, facilitating comprehensive condition assessment [64].

LIBS applications in insulator aging have evolved from rapid elemental characterization toward systematic studies correlating elemental variation patterns with degradation mechanisms. In 2019, Li Yuansheng et al. [65] established correlations between surface elemental migration and aging severity. By integrating Principal Component Analysis (PCA), they achieved aging classification, demonstrating LIBS’s diagnostic potential. That year, Xilin Wang et al. [66] employed LIBS for elemental analysis of aging silicone rubber, detecting primary elements (C, O, Al, Si, Fe, Zn). They identified a linear relationship between laser pulses and ablation depth, advancing in situ analysis through combined surface and depth-resolved profiling. Subsequent research deepened investigations into elemental change patterns during aging. The same team [67] later validated linear intensity-concentration relationships for C, Si, and Al (R² > 0.948). Li Yun (2019) [68] observed increased surface Ca/K, variable Al/Fe, and decreased Si in aged insulators. An SVM-based assessment model achieved 93.94% classification accuracy. Yuansheng Li (2019) [69] attributed Ca/K/Cl detection to contaminant infiltration in long-term aging, noting significant Al/Fe/Ca variations and Si depletion. The difference in the emission spectra of plasma aged for three years and unaged plasma can be seen in Figure 8. Wang Yixin et al. (2020) [64] introduced a surface-to-core Si/Al ratio metric for aging quantification, with results consistent with FTIR spectroscopy. While early LIBS studies focused on laboratory settings, field deployment remained challenging. Kokkinaki (2020) [70] addressed this by developing a remote LIBS system using off-axis Newtonian optics, enabling detection at 10 m distances. Taisei Homma (2021) [71] advanced remote LIBS technology, reporting a 30% decrease in Si/Al intensity ratios in degraded zones. Lu Shan et al. (2021) [72] implemented Calibration-Free LIBS (CF-LIBS) [73], reducing Na/Ca quantification errors below 20% for precise elemental change characterization. Xu et al. (2024) [74] employed laser-induced breakdown spectroscopy (LIBS) combined with random forest classification to evaluate the aging state of composite insulators. The method achieved a testing accuracy of 95.46% for defects such as cracks, holes, and wear.

With respect to degradation manifestations, LIBS is particularly effective in identifying elemental composition changes, such as the loss of hydrophobic additives, surface carbonization, or contamination deposits, which are strongly associated with chemical and morphological aging of composite insulators. In summary, LIBS offers significant advantages for composite insulator aging assessment through rapid, in situ, multi-element analysis [75]. Remote LIBS advancements enable non-contact, efficient field monitoring [76]. However, limitations persist, including matrix effects [77] and the need for improved quantitative accuracy [78].

Looking forward, future research should concentrate on improving calibration strategies to mitigate matrix effects, enhancing quantitative analysis through advanced chemometric methods, and extending LIBS sensitivity to subtle compositional shifts indicative of early-stage aging. Concurrently, the development of compact and remote-operated devices should be advanced to meet the demands of engineering applications in complex environments.

Figure 7. Principle Diagram of Insulator Detection System Based on LIBS [75].

Figure 7. Principle Diagram of Insulator Detection System Based on LIBS [75].

Figure 8. Plasma emission spectra of composite insulator samples (a) before aging and (b) after aging for three years [69].

Figure 8. Plasma emission spectra of composite insulator samples (a) before aging and (b) after aging for three years [69].

6. Other Spectroscopic Detection Techniques

Recent advances in optical technologies have facilitated the application of Raman and Terahertz (THz) spectroscopy to transmission line insulator diagnostics. THz spectroscopy operates by detecting changes in conductivity and permittivity within aged insulator dielectrics, analyzing reflection and transmission characteristics of THz waves [79]. These alterations affect the amplitude and phase of reflected versus incident waves, yielding critical insights into internal insulator condition [80]. Additionally, THz radiation readily penetrates non-polar materials [81], enabling internal defect detection. Combined with surface morphology analysis, this permits comprehensive condition assessment [82]. Although still emergent, THz-based insulator diagnostics have yielded notable progress. For instance, Zhang Xuemin et al. (2021) [83] established an aging detection model using a THz vector network analyzer, validating non-contact THz assessment feasibility. He Jie et al. (2023) [84] subsequently analyzed 11 insulators (10–110 kV) from North China using a THz platform (shown in Figure 9). By applying wavelet denoising and BP neural networks to high-frequency THz signals, they significantly enhanced aging detection accuracy. Mei et al. (2024) [85] employed terahertz spectroscopy to detect the main components of high-temperature vulcanized silicone rubber, enabling rapid and non-destructive evaluation of PDMS, ATH, and silica content, with the LS-SVM model achieving a maximum prediction correlation coefficient of 0.9915. Cao et al. (2024) [86] utilized terahertz frequency-domain spectroscopy to non-destructively analyze the physical, chemical, and electrical property changes in materials, allowing for quantitative assessment of insulation aging. Tengyi Zhang et al. (2025) [87] analyzed silicone rubber filler vibrations via THz spectroscopy. Integrating theoretical predictions with elemental testing, they identified characteristic frequency bands and established quantitative filler–content relationships using SPA and MLR. This achieved rapid non-destructive quantification of ATH, PDMS, and SiO₂ with <0.24% error, outperforming TGA precision. Nevertheless, practical implementation faces challenges including environmental interference susceptibility, complex data processing, and limited sensor probe accuracy [74], necessitating improved robustness and stability.

Similarly to infrared spectroscopy, Raman-based detection analyzes spectral shifts and intensity changes in molecular groups (e.g., Si-O-Si, Si-CH₃, Si-OH) via Raman scattering. Aged insulators exhibit peak attenuation or displacement correlating with degradation severity [88]. The Raman system generates surface molecular information through the interaction between the excitation source and the sample. The scattered light is then collected by a spectral probe and transmitted to the spectrometer, where the degree of insulator aging can be characterized by analyzing the frequency shifts in characteristic Raman peaks and the attenuation in their intensities [89]. Moreover, owing to its micron-scale spatial resolution, Raman spectroscopy enables precise analysis of localized regions that may experience more severe aging (e.g., areas near electrical erosion pits), thereby facilitating investigations into the spatial distribution of degradation. Ghunem et al. (2016) [90] compared Si-OH and Si-O-Si Raman band intensities in surface oxides from 200 and 400 kV Gulf-region insulators, enabling surface oxidation detection. Chen Xingang et al. (2023) [91] employed Raman spectroscopy combined with the AA-KNN algorithm to identify the aging state of oil-paper insulation. The results showed that this method can rapidly distinguish samples at different aging stages, achieving an average classification accuracy of 98.32%. However, challenges persist including fluorescence interference, weak signal intensity, and complex data analysis. Consequently, Raman-based composite insulator diagnostics remain predominantly confined to laboratory research.

In terms of degradation phenomena, Raman spectroscopy primarily reflects surface molecular-level changes and oxidation, while THz spectroscopy is capable of probing deeper structural alterations, such as internal delamination, moisture ingress, and dielectric property variations, which are difficult to detect with surface-sensitive methods. Currently, terahertz technology is best suited for precise material analysis in laboratory settings or for the offline inspection of critical equipment, particularly in studies investigating the aging mechanisms of composite insulators. Future breakthroughs are needed in three key areas: designing sensors with improved anti-interference capabilities, streamlining data processing algorithms for ease of use, and developing coupled multi-physics field models.

7. Integrated Multi-Spectroscopy Approaches: Case Studies

Single-spectrum detection methods exhibit inherent limitations in meeting industrial field requirements. Consequently, researchers are actively developing multi-spectral fusion technologies to enhance detection accuracy. In 2014, Liu Hui et al. [92] pioneered an integrated multi-spectral technique combining infrared, ultraviolet, and visible light, successfully implementing it on 500 kV transmission line insulators in Shandong Province. Jin Lijun (2018) [93] developed a variable-weight fusion method using Fisher criterion weighting. By integrating visible, IR, and UV image data for artificially aged insulator assessment (Figure 10), this approach reduced average ∆ESDD error to 0.0125 mg/cm², significantly improving evaluation accuracy. Zhang Xueqin et al. (2021) [94] fused infrared and hyperspectral data, using spectral parameters as machine learning inputs. Their deep extreme learning machine model achieved 96.67% aging classification accuracy using full-spectrum data. Liu Yicen et al. (2022) [95] combined hyperspectral and IR imaging to establish a novel contamination detection model. Concurrently, Bin Wang et al. [96] implemented feature band selection from hyperspectral data and spatial clustering of multispectral grayscale values, achieving 83.1% accuracy with enhanced efficiency.

Field deployment of multi-spectral fusion is advancing. Dai et al. (2015) [17] integrated IR/UV imaging onto UAVs for efficient inspection, while Chen Lincong et al. [17] combined FTIR and hyperspectral features with SVM algorithms, attaining 97.3% aging classification accuracy. These studies demonstrate multi-spectral fusion’s efficacy in enhancing detection precision and efficiency. Future efforts should prioritize intelligent fusion methodologies to further optimize inspection effectiveness.

8. Conclusions and Future Perspectives

This review systematically analyzes spectral technologies for composite insulator aging detection, delineating their respective applications in physicochemical characterization, defect diagnosis, and degradation mechanism analysis. Hyperspectral, Raman, and FTIR techniques primarily track surface functional group changes and molecular structures. The principles and advantages and disadvantages of each technology are shown in

Table 1. Comparison of Advantages and Disadvantages of Spectral Technologies for Insulator.

Spectroscopic Technique	Detection Principle	Advantages	Limitations
Ultraviolet Pulse Spectrum	By collecting ultraviolet light emission signals from the insulator surface, the intensity and location of partial discharge phenomena are analyzed to assess aging degree and localized damage positions [31].	Simple setup	Susceptible to environmental interference; requires long-term monitoring.
Fourier Transform Infrared (FTIR) Spectroscopy	Assesses material aging by analyzing variations in functional groups (e.g., Si-O, Si-CH3) in silicone rubber [41].	Non-destructive; fast spectral acquisition	Limited quantitative evaluation abilities
Hyperspectral Imaging	Utilizes the “spectral fingerprint effect” to detect differences in surface reflectance across a wide range of wavelengths, thereby evaluating aging [51].	Suitable for in situ detection; high sensitivity	Requires supplemental illumination; relatively low spectral resolution
Laser-Induced Breakdown Spectroscopy (LIBS)	Determines aging and material state by analyzing elemental composition and concentration changes in surface and subsurface layers [64].	Minimally invasive; high detection speed and accuracy	Influenced by long-term laser source stability
Terahertz Spectroscopy	Probes internal structural anomalies and assesses material state by analyzing changes in conductivity, permittivity, and associated reflection/transmission characteristics [80].	Capable of penetrating non-conductive materials	Limited spatial resolution and signal-to-noise ratio
Raman Spectrum	Raman spectroscopy evaluates the aging state of the chemical structure by reading and analyzing the vibrational fingerprints of molecular bonds in silicone rubber and examining changes in characteristic peaks [88].	Non-destructive; fast spectral acquisition	Fluorescence interference; high demand for surface cleanliness

below.

From the perspective of aging manifestations, different spectroscopic methods are intrinsically correlated with specific degradation phenomena: UV detection mainly reflects surface discharges, FTIR and Raman reveal molecular bond scission and oxidation, hyperspectral imaging captures features such as cracks and hydrophobicity loss, while LIBS and THz spectroscopy highlight elemental migration, carbonization, and internal structural changes. At the same time, LIBS and THz spectroscopy possess the capability to probe deep-layer alterations, such as elemental migration and structural distortions, thereby offering penetration advantages. These correspondences underscore the complementarity of various spectroscopic techniques and the necessity of integrated approaches for comprehensive condition assessment of insulators. With increasing demands for detection accuracy, multi-spectral fusion is gradually emerging as a promising solution to the limitations of single methods. Preliminary studies—such as FTIR-hyperspectral and IR-UV fusion—have demonstrated synergistic advantages in multi-scale feature recognition. Nevertheless, current applications remain at an early stage, lacking standardized frameworks. Critical bottlenecks include effective feature band selection from massive datasets, challenges in complex system integration, and the limited generalizability of fusion methods—all of which significantly constrain the reliability of field deployment.

Collectively, spectral detection offers non-contact, non-destructive, and information-rich advantages for aging assessment. However, engineering challenges persist—insufficient device integration, environmental interference during acquisition, and limited data interpretability in complex conditions—hindering efficient, precise field evaluation. Future development should prioritize: (1) Establishing high-quality spectral databases under representative conditions, (2) creating integrated handheld devices for harsh environments, (3) implementing intelligent fusion algorithms to enable real-time field translation of laboratory techniques, and (4) integrating UAV-based and IoT-enabled spectroscopic platforms for wide-area, real-time monitoring of transmission line insulators. As multi-spectral fusion and intelligent analytics mature, spectral-based health assessment systems will become critical technological pillars for power equipment condition monitoring.