Time–Frequency Analysis of Acoustic Partial Discharge Signals in a 20 kV Switchgear: A Case Study Using an Acoustic Camera

Cross-Attention Fusion of Time-Frequency Acoustic Features for Insulation Fault Recognition in Power Equipment Using a Microphone Array System.

This protocol provides a robust non-contact strategy for insulation fault diagnosis and condition monitoring of electrical power equipment by extracting and fusing time- and frequency-domain acoustic features for automated fault classification.

Partial discharge in power transformers: A critical review of acoustic emission and UHF-based detection and localization methods

Partial discharge (PD) is a principal precursor of insulation deterioration in power transformers, and its early detection and accurate localization are essential to prevent progressive degradation and catastrophic breakdown. This review compares acoustic emission (AE) sensing and ultra-high-frequency (UHF) electromagnetic sensing and establishes the conditions under which each technique, or their combination, is to be preferred. The paper is presented as a structured critical review conducted under a transparent, pre-defined search protocol. Six bibliographic databases were searched using documented Boolean strings for English-language records published between 1990 and 2025; records were screened against explicit inclusion and exclusion criteria, appraised for methodological quality, and reduced to a final evidence base whose selection is documented in a study-selection flow diagram (Figure 1). To remove the ambiguity of unqualified descriptors, every comparative judgement in this review is made against an explicit rating rubric (Table 3) in which detection sensitivity, localization accuracy, noise immunity, detection range and cost are given operational definitions with quantitative or procedural thresholds. Assessed against these definitions, AE and UHF do not resolve into a simple ordering; their relative performance is conditional on sensor placement and on system architecture. UHF sensing attains the lower detection threshold, and internally mounted UHF sensors benefit from the electromagnetic shielding afforded by the grounded tank, whereas externally mounted UHF sensors are exposed both to substation and broadcast interference and to aperture attenuation — which reconciles the apparently contradictory claims regarding UHF noise immunity. The corresponding contradiction concerning AE localization is likewise resolved by architecture rather than by physics: all-acoustic time-difference-of-arrival (TDOA) localization must solve for four unknowns and is vulnerable to structure-borne arrivals preceding the direct oil-borne path, whereas AE triggered by a simultaneous electrical or UHF reference reduces the problem to three unknowns and achieves reported errors of the order of 0.1 m. Localization uncertainty is shown to be dominated not by the choice of estimator but by acoustic-velocity uncertainty, sensor-position error and timing synchronization. The evidence supports a hybrid architecture — UHF for detection and time reference, AE for spatial localization — rather than a choice between the two. The review contributes an operationally defined comparison rubric, a reconciliation of contradictory performance claims in the existing literature, a rigorous statement of the second-order cone programming (SOCP) localization formulation together with its assumptions and sensor-count requirements, and an evidence-based comparison of offline, periodic-online and continuous monitoring architectures.

Analyzing seasonally and wind-dependent performance of rain detection from underwater acoustic power spectral density.

Rainfall can be empirically monitored by analyzing characteristic spectral features in the ocean's ambient sound. Previous work to detect and estimate rainfall from passive underwater acoustics used linear transformations of these features; Ma and Nystuen [J. Atmos. Oceanic Technol. 22, 1225-1248 (2005)] measured acoustic power at three narrowband frequencies, extended by Mallary, Berg, Buck, and Tandon [J. Acoust. Soc. Am. 154(1), 556-570 (2023)] and Berg [Master's thesis (2023)] to principal component analysis (PCA). PCA represents broadband spectra with small numbers of linear coefficients [Hotelling, J. Educ. Psychol. 24(6), 417-441 (1933)]. This research extends the PCA broadband approach to design detectors by rain, wind, and season to finely tune subspaces for each class. Five-minute power spectral density (PSDs) are computed using Welch's method and are separated into dry (<1 mm/hr) and rainy (≥1 mm/hr) recordings for each season and wind category. A linear dimension reduction matrix is defined for the dry PSDs of each season and wind category while preserving >99% of variance. Rainfall can then be detected using a likelihood ratio test of PSDs for each class. At a shallow-water site, separation of detectors by season and wind improves detection performance from 44.7% of rainfall volume to 55.1% (64.7% with false alarm filtering) with 1% false alarms. Accounting for wind and season may improve monitoring of natural processes such as rainfall from underwater acoustic recordings.

Designing of Low-cost Acoustic Analyzer Device with Arduino for Monitoring Hydropower Generator

Low-cost acoustic monitoring can support condition-related data collection for hydropower generators where commercial instruments may be costly or difficult to deploy. This study developed an acoustic acquisition and monitoring arrangement using a KY-038 sound sensor, a parabolic reflector, and an Arduino Mega 2560 architecture for a 15 MW Kaplan hydroturbine alternator. Approximately 100 measurements were collected in each of three defined alternator sectors during normal operation. The sensor output was converted from analogue voltage to pressure and sound level for recording and interpretation. The measurements showed a spatial increase in sound level with proximity to the rotor. Reported average levels for the non-pressed configuration were approximately 73.05 dB in the cooling region, 75.83 dB in the air-gap region, and 79.05 dB near the rotor. With the reflector pressed against the measurement location, the reported sector averages were 74.24, 76.27, and 80.69 dB, respectively. The two acquisition configurations therefore showed a similar spatial pattern, while the pressed configuration produced slightly higher measured levels. These findings indicate that consistent sensor position and contact are important for repeatable baseline acoustic mapping. The proposed low-cost arrangement provides a practical basis for repeated monitoring under comparable operating conditions. However, measurements were not obtained under confirmed fault conditions, and the parabolic-reflector mounting technique requires further improvement and validation.

Acoustic signal - based misfire detection in internal combustion engines using machine learning techniques

Use of Principal Component Analysis and Autoencoder Neural Networks in Characterization of Acoustic Emission Waveforms from Composite Coupon Specimens

Acoustic emission (AE) monitoring is a method of structural health monitoring that relies on the detection of elastic waves generated by the release of concentrated strain energy when damage is created in a structural material. One of its strengths is that registered waveforms can, in theory, be used to draw conclusions about the origin of the signals, allowing to estimate damage type, location, and severity. One of the problems, however, is how to automate the interpretation and reliably retain useful information from AE waveforms, which are often thousands of samples long. In this work, a method of waveform analysis in the frequency domain is presented that combines principal compo- nent analysis and an autoencoder to reduce the dimensionality of the problem to a pair of parameters, capturing the spectrum shape and the spectrum en- ergy content. Tensile tests were carried out on composite coupon specimens while monitoring AEs, and the progression of damage was tracked by X-ray scanning them in two locations before and after tensile testing. Locations and types of damage in the scans are in good agreement with the results of the AE monitoring and analysis framework. The method is proposed as a tool for automated interpretation of AE signals with the potential to be generalized to other material, layup, and sensor setups.