A Novel Vibration Centroid-Based Approach for Fault Diagnosis of Transformer Winding

Fault Diagnosis of On-Load Power Transformers via Weak Sideband Vibration Identification

The winding structure is a critical component of a power transformer. Assessing its structural condition during operation poses significant challenges for grid maintenance. Unlike static windings, the on-load windings undergo periodic stiffness changes under electromagnetic forces (EMFs). In this study, a novel parametric vibration model based on the time-varying stiffness is investigated. Next, the Mathieu equation is employed to analyze the sideband vibration response, and the spectral correlation (SC) method is applied to detect weak signals. This study presents a transfer learning framework employing a knowledge separator and a domain-adversarial network to address the challenge of limited fault samples. In laboratory experiments, the structural degradation of windings was simulated by adjusting the clamping force. Both numerical simulations and experimental results show that reducing the clamping pressure from 4.0 to 2.0 MPa causes the characteristic frequency to shift from approximately 46–20 Hz. The performance of the transfer learning is assessed based on the prediction accuracy of the unlabeled samples in the target domain following training in the source domain. Finally, vibration monitoring was performed on a field transformer, which served to verify the correlation between the observed 16.1 Hz low-frequency sideband vibration and the defects in the winding structure.

A Data-Driven Vibration Analysis Framework for Micro-Motor Fault Diagnosis and Quality Control

The reliability of the internal micro-motors is crucial for the performance and lifespan of electric toothbrushes. In this paper, a vibration-based fault detection method is proposed to identify micro-motor defects in electric toothbrushes. A dedicated signal acquisition device was designed and developed to capture the vibration signals of micro-motors using a high-precision accelerometer. To effectively characterize the micro-motor conditions, comprehensive features were extracted from the raw vibration data in both the time and frequency domains. A random forest (RF) algorithm was then employed to evaluate the importance of all extracted features. To better interpret the extracted features based on fault mechanisms, and to reduce dimensionality and computational overhead while avoiding overfitting, the top three features with the highest importance scores were selected to form the optimal feature subset. Finally, a support vector machine (SVM) model was utilized to classify the motor states based on the selected features. Experimental results demonstrate that the proposed method, combining RF-based feature selection and SVM classification, achieves outstanding diagnostic performance. Specifically, the model yields a balanced accuracy of 94.44%, a defect recall of 88.89%, a defect F1-score of 94.12%, a Matthews correlation coefficient of 93.74%, a geometric mean of 94.28%, and an area under the receiver operating characteristic curve of 100.00%. These robust metrics confirm that the proposed approach can accurately and efficiently detect micro-motor faults in electric toothbrushes, providing a practical and reliable solution for quality control and condition monitoring in manufacturing.

Fault Diagnosis of Automotive Gasoline Engines Using Time–Frequency Vibration Image Analysis

CWT-PSDT-Based Identification of Electromagnetic-Related Stator Vibration Frequency Components in a Hydro-Generator

Accurate identification of electromagnetically induced stator vibration frequency components is essential for the online condition monitoring of hydro-generators, particularly for assessing the dynamic state of the stator core under normal operating conditions. In engineering practice, the fast Fourier transform (FFT) is widely used for vibration spectrum analysis; however, because the measured vibration response is simultaneously affected by electromagnetic excitation, mechanical rotation, hydraulic disturbance, and external harmonic interference, FFT-based spectra often contain multiple frequency components whose structural relevance is difficult to determine directly. To address this issue, this paper proposes a coupled continuous wavelet transform and power spectral density transmissibility (CWT-PSDT) method for identifying key vibration frequency components with stable time-frequency energy and inter-sensor transmissibility in hydro-generator stator vibration signals. In the proposed framework, the analytic Morlet wavelet is first employed to localize dominant energy bands in the time-frequency domain, and PSDT is then used to screen frequency components with relatively stable inter-sensor transmissibility characteristics, thereby reducing the ambiguity caused by excitation-dominated spectral components. A clamped-clamped beam model is first used for numerical validation, and the maximum identification error of the first five natural frequencies is 4.22%. Experiments on a Francis turbine-generator test rig under five operating conditions further show that the proposed method can distinguish the mechanical rotational component near 10.3 Hz from the electromagnetic-related component near 50.8 Hz, while retaining higher-order electromagnetic-related components around 150 Hz and 250 Hz. The results demonstrate that the proposed CWT-PSDT method provides a physically interpretable and data-efficient approach for extracting stator-core-related spectral features, and offers a theoretical basis for spectrum-based online monitoring and future abnormal-condition comparison of hydro-generator stator responses.

Real-Time Vibration Detection and Signal Analysis of Machines

This work is a condition-based maintenance low-cost real-time vibration monitoring system of industrial machines based on the ESP32 microcontroller and MPU6050 triaxial accelerometer. The axis-wise and overall RMS vibration amplitude is calculated in the system by time domain processing, which offsets them based on a specified threshold that is installed in MATLAB and which is also embedded in the profile parameters per the requirements of vibration severity. The FFT-based real-time spectral analysis is used to detect the vibration frequency components that are predominant in mechanical faults. The machine condition is automatically determined to be safe, warning or dangerous based on quantitative factors such as mean RMS vibration and danger ratio based on time. Multimachine experimental results record values of RMS vibration between 0.02 g and 0.78 g and a predominant low-frequency vibration signal between 0.3 and 3 Hz. The proposed system was implemented with classification accuracy of 91.67%, analysis latency of less than 100ms. The entire self-sacrificing system has a cost of less than 15 USD, making it fit the limited resource-based industrial settings.

Three-Dimensional Approach to the Transfer Function in Transformer Windings and Its Application to the Interpretation of SFRA Results

Power transformer integrity is critical for system reliability as mechanical winding deformations can alter electrical behavior. Sweep Frequency Response Analysis (SFRA) is widely used to detect such alterations; however, fault localization remains challenging due to the terminal nature of conventional measurements. This paper characterizes the distributed electromagnetic behavior of a transformer winding using spatially resolved transfer functions along its axial position. Controlled faults are introduced by modifying inter-disc capacitance, and magnitude and phase data are used to construct three-dimensional representations as a function of frequency and winding position. The proposed framework visualizes how localized perturbations redistribute modal energy within the winding. Selected diagnostic frequencies correspond to spatial electromagnetic modes, providing meaningful features for the classification model. A two-stage Support Vector Machine (SVM) is implemented to localize faults into predefined winding regions using only terminal measurements. The method achieves 94.4% overall classification accuracy. Results demonstrate that conventional SFRA data contain sufficient information for faulty localization when interpreted within a distributed-modal framework.