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Intelligent Sensors for Fault Diagnosis in Power Equipment

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A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Physical Sensors".

Deadline for manuscript submissions: closed (25 August 2026) | Viewed by 2177

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Editors

Prof. Dr. Hui Ding

E-Mail Website
Guest Editor

State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: application of new optical fiber sensing technology in state detection of electric power equipment; intelligent sensing system and instrument design

Dr. Shudong Wang

E-Mail Website
Guest Editor Assistant

School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: MEMS acoustic sensing; photoacoustic MEMS gas sensing; integration of MEMS sensors

Special Issue Information

Dear Colleagues,

Power equipment, including transformers, circuit breakers, motors, and generators, forms the backbone of modern electrical grids. Unexpected failures of such assets can lead to severe economic losses, long outage durations, and safety hazards. Early detection, accurate fault diagnosis, and predictive maintenance have therefore become critical to ensure reliability, stability, and efficiency in power systems.

Recent advancements in intelligent sensing technologies—integrating high-precision data acquisition with AI-driven analytics—offer new opportunities for real-time monitoring and diagnosis of electrical equipment. Multi-modal sensor systems, including temperature, vibration, acoustic, partial discharge, and electrical parameter sensors, along with modern IoT-based architectures, enable continuous condition monitoring and fault prediction. Novel algorithms exploiting signal processing, machine learning, and deep learning can analyze complex data streams, support root cause analysis, and optimize asset management strategies.

This Special Issue on "Intelligent Sensors for Fault Diagnosis in Power Equipment" invites contributions presenting innovative sensor designs, integrated monitoring systems, and intelligent diagnostic methods in real-world power system applications. Topics include hardware, algorithms, communication protocols, and case studies from laboratories and industry.

Topics of interest include, but are not limited to, the following:

Intelligent sensor design for electrical machinery and equipment;
Multi-parameter sensing (temperature, vibration, acoustic, partial discharge, etc.);
IoT-enabled monitoring platforms for power systems;
Machine learning and deep learning for fault diagnosis;
Data fusion and advanced signal processing in sensor systems;
Sensor-based predictive maintenance for transformers, motors, switchgear;
Real-time monitoring and protection systems in smart grids;
Edge computing and embedded AI for condition monitoring;
Wireless sensor networks for distributed asset management;
Cybersecurity in sensor-based monitoring systems.

Prof. Dr. Hui Ding
Guest Editor

Dr. Shudong Wang
Guest Editor Assistant

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Keywords

intelligent sensor design for electrical machinery and equipment
multi-parameter sensing (temperature, vibration, acoustic, partial discharge, etc.)
IoT-enabled monitoring platforms for power systems
machine learning and deep learning for fault diagnosis
data fusion and advanced signal processing in sensor systems
sensor-based predictive maintenance for transformers, motors, switchgear
real-time monitoring and protection systems in smart grids
edge computing and embedded ai for condition monitoring
wireless sensor networks for distributed asset management
cybersecurity in sensor-based monitoring systems

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Published Papers (4 papers)

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Research

17 pages, 1375 KB

Open AccessArticle

Intelligent UHF Sensor-Based Partial Discharge Fault Diagnosis in GIS Using a Temporal-Frequency Dual-Branch Stochastic Configuration Network

by Mingyuan Hu, Jingwen Liu, Baolong Yu, Ying-Ren Chien and Lei Zhang

Sensors 2026, 26(18), 5739; https://doi.org/10.3390/s26185739 - 9 Sep 2026

Abstract

Gas-insulated switchgear (GIS) is an important component of power transmission systems. Accurate partial discharge (PD) pattern recognition is a key requirement for identifying internal insulation defects within the equipment. However, ultra-high-frequency (UHF) PD pulse sequences produced by different insulation defects usually contain complex nonlinear temporal structures and multi-scale periodic variations. These coupled characteristics are difficult to describe adequately via a single feature-mapping strategy. Thus, this paper proposes a temporal-frequency dual-branch stochastic configuration network (TF-SCN), which consists of two heterogeneous hidden-layer branches, for GIS PD pattern recognition. Specifically, in the temporal branch, the model uses a non-periodic, nonlinear activation function similar to that used in a conventional SCN to capture the nonlinear temporal characteristics. The frequency-sensitive branch introduces paired sine–cosine harmonic nodes with shared random projection parameters to capture frequency-sensitive features. The hidden outputs of the two branches are concatenated into a joint temporal-harmonic feature space, and the output weights are solved under the residual inequality constraints for GIS PD classification. To verify the superiority of the proposed model, comparative experiments are conducted on a dataset containing four PD patterns collected from the GIS PD experimental platform. Several baseline models, including 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, are selected for performance comparison. The results show that, compared to 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, TF-SCN effectively extracts distinguishable features in both the time and frequency domains, thereby achieving the best overall performance. Furthermore, its recognition performance remains consistently superior even on noisy data with signal-to-noise ratios ranging from 50 dB to 20 dB. By integrating highly sensitive UHF sensors with the proposed TF-SCN, this study presents a robust, AI-enhanced intelligent sensing and fault diagnosis system for continuous condition monitoring of power equipment. Full article

(This article belongs to the Special Issue Intelligent Sensors for Fault Diagnosis in Power Equipment)

23 pages, 4619 KB

Open AccessArticle

Leakage Current Analysis of Glass, Porcelain, and Silicone Insulators Under Icing Conditions Using Spectrogram-Based Deep Convolutional Neural Networks

by Muhammed Buğracan Özküçük, Ömer Faruk Alçin and Muhsin Tunay Gençoğlu

Sensors 2026, 26(13), 4121; https://doi.org/10.3390/s26134121 - 30 Jun 2026

Viewed by 471

Abstract

Insulators are essential for the secure and uninterrupted functioning of high-voltage transmission lines. However, since insulators are exposed to the outdoor environment, they are inevitably affected by environmental conditions such as icing. Accumulation of ice on insulator surfaces adversely impacts insulation efficacy and elevates surface leakage currents, resulting in power outages. This research presents a spectrogram-based convolutional neural network (CNN) model for identifying icing conditions on the surfaces of glass, porcelain, and silicone insulators. Insulators are labeled in three classes under laboratory conditions: ice-free, slightly iced (t < 12 mm), and iced (t > 20 mm). High voltage was applied at three distinct levels ranging from 10 to 50 kV, considering the icing conditions of each insulator, and leakage current signals were recorded. The Butterworth and smoothing filters were first applied to the leakage current signals, which were then transformed into spectrogram images using the Fourier transform and used as input for the created CNN architecture. Additionally, spectrogram images were also applied to AlexNet, GoogLeNet, and ResNet-50 architectures. The suggested CNN architecture attained an accuracy of 97.78% to 100% across all operating situations for glass and silicone insulators while demonstrating a classification success rate of 82.22% to 100% for porcelain insulators. Experiments indicate that the accuracy rates of established models in the literature (AlexNet, GoogLeNet, and ResNet-50) diminished to as low as 73%, particularly in porcelain insulator data, thereby validating the developed model’s proficiency in differentiating during icing detection processes and its adaptability to varying conditions. Full article

(This article belongs to the Special Issue Intelligent Sensors for Fault Diagnosis in Power Equipment)

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19 pages, 5869 KB

Open AccessArticle

A Self-Powered Vibration Sensing System for High-Voltage Transmission Lines with Equipotential Connections

by Xueqiong Zhu, Jinggang Yang, Chengbo Hu, Zhen Wang, Ziquan Liu and Zhengyu Liu

Sensors 2026, 26(11), 3574; https://doi.org/10.3390/s26113574 - 4 Jun 2026

Viewed by 520

Abstract

In this work, a self-powered vibration sensing system is proposed, based on a spatial magnetic field energy harvester, a duty-cycled circuit module, a piezoresistive graphene-based vibration sensor, and a wireless communication unit. The energy harvester is capable of generating an output power of 729 μW under a magnetic field excitation of 0.11 mT at 50 Hz. The duty-cycled circuit module enables closed-loop self-powered operation of the sensing system by efficient power storage and periodic measurement, and LoRa wireless transmission. The graphene-based sensor exhibits stable low-frequency vibration responses and good linearity and can capture composite vibration signals containing 4 Hz and 50 Hz components. These results indicate the potential of the proposed system for future transmission-line vibration sensing applications. Full article

(This article belongs to the Special Issue Intelligent Sensors for Fault Diagnosis in Power Equipment)

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20 pages, 4237 KB

Open AccessArticle

PaEDNet: A Robust Denoising and Classification Framework for Vibration-Based Fault Diagnosis with Measurement Noise

by Xiaojing Liao, Yongwei Chi, Yu Bai, Qinya Dai, Peiyu Zhao, Na Li, Linlin Sun and Dongyang Li

Sensors 2026, 26(11), 3435; https://doi.org/10.3390/s26113435 - 29 May 2026

Cited by 1 | Viewed by 502

Abstract

To address the problem of fault-related structures and noise disturbances in rolling bearing vibration signals being highly coupled in the original one-dimensional signal domain under severe noise conditions, in this study, we propose a Phase-space adaptive Expert Denoising Network (PaEDNet), a robust fault diagnosis framework that integrates representation construction, adaptive restoration, and condition discrimination. Unlike existing methods that mainly enhance network modelling directly in the original signal domain, the proposed framework first constructs a spatially organised two-dimensional similarity representation through phase-space reconstruction, which further unfolds fault-related dynamic structures from temporal entanglement and provides a more suitable preliminary representation domain for subsequent restoration. On this basis, a CoPaMoE-augmented adaptive denoising module is introduced into the representation domain to improve structural restoration capability under heterogeneous noise and different local patterns. DenseNet is then employed for fault classification, thereby forming an integrated fault diagnosis framework combining representation reconstruction, noise restoration, and condition discrimination. The resulting pipeline performs end-to-end diagnosis from raw vibration signals to fault labels at inference, while training is conducted in a stage-wise manner. Experimental results derived using the two public datasets, CWRU and PU, show that the proposed method consistently outperforms multiple comparative models under different signal-to-noise ratio conditions and maintains stronger robustness in low-SNR scenarios. Under the −6 dB condition, PaEDNet achieves classification accuracies of 93.98% and 90.45% on the two datasets, respectively. Further ablation studies and expert-routing analysis demonstrate that the combination of structured representation construction and adaptive expert restoration jointly enables the improved performance of the model. In this study, we provide a new modelling perspective for the fault diagnosis of vibration signals in complex noisy environments. Full article

(This article belongs to the Special Issue Intelligent Sensors for Fault Diagnosis in Power Equipment)

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