With the rapid development of smart manufacturing, data-driven deep learning (DL) methods are widely used for bearing fault diagnosis. Aiming at the problem of model training crashes when data are imbalanced and the difficulty of traditional signal a...
Time-series classification remains a critical task across various domains, demanding models that effectively capture both local recurrence structures and global temporal dependencies. We introduce a novel framework that transforms time series into im...
In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, thi...
This paper presents a Load-Dependent Multimodal Vibration Signal Enhancement and Fusion Framework (LD-MVSEFF) for load-specific condition monitoring, building on the Customised Load Adaptive Framework (CLAF). The proposed approach enhances the classi...
Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to...
Bearings serve as vital components in rotating machinery. Fault diagnosis of bearings constitutes an essential area within mechanical health monitoring. However, most existing methods rely solely on single-modal data or employ a single signal-to-imag...
To address the drawbacks of traditional convolutional neural network-based rolling bearing fault diagnosis techniques, including poor feature extraction, low diagnostic accuracy, and poor generalization capability, a dual-channel rolling bearing faul...
Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short...
The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing the in...
The cutting head is the primary cutting load device of the roadheader. The harsh, complex excavation environment often leads to a scarcity of labelled fault samples, significantly hindering the development of accurate fault diagnosis models. This pap...
Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for the safe operation of electric vehicles and energy storage systems. However, most existing methods rely on complete charging curves or manual feature engineering...
With the increasing penetration of renewable energy sources and power-electronic devices, modern power systems exhibit pronounced wideband oscillation characteristics with large frequency spans, strong modal coupling, and significant time-varying beh...
Fritillaria thunbergii Miq. (ZBM) and Fritillaria hupehensis Hsiao et K.C. Hsia (HBBM) are two common medicinal and edible species of the genus Fritillaria, both possessing high economic and pharmacological value. Given their differences in pharmacol...
To comprehensively leverage the temporal, static, and dynamic information features of multi-channel surface electromyography (sEMG) signals for gesture recognition, considering the sensitive temporal characteristics of sEMG signals to action amplitud...
As a core component of mechanical transmission systems, gear damage status significantly impacts the safety and efficiency of an overall mechanical system. However, existing fault diagnosis methods often struggle to extract features effectively in co...
Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and...
Transformers are critical equipment in power transmission and distribution systems, and the condition of their windings significantly impacts their reliable operation. Therefore, the fault diagnosis of transformer windings is of great importance. Add...
The challenge of accurately diagnosing mechanical failures in high-voltage circuit breakers is exacerbated by the non-stationary characteristics of vibration signals. This study proposes a Dual-Channel Convolutional Neural Network (DC-CNN) framework...
To address class imbalance in transmission line fault traveling-wave samples, the strong non-stationarity of transient traveling-wave features, and the limited identification capability of single-representation methods, this paper proposes a fault ty...
To address the limitations of traditional fault diagnosis methods for rotating machinery, which heavily rely on single-dimensional vibration data and fail to fully exploit the deep features of time-series data, this study proposes an innovative diagn...
Vibration-based diagnosis can identify transformer core looseness non-intrusively, but subtle vibration differences and noise-disturbed Gramian angular field (GAF) textures reduce diagnostic accuracy at low signal-to-noise ratios (SNRs). This paper p...
Gramian Angular Field (GAF) encodings transform time series into two-dimensional images suitable for convolutional neural network (CNN) classification. Existing applications typically use either the Gramian Angular Summation Field (GASF) or the Grami...
This paper proposes a framework to perform the sensor classification by using multivariate time series sensors data as inputs. The framework encodes multivariate time series data into two-dimensional colored images, and concatenate the images into on...
The online rapid classification of multi-cultivar watermelon, including seedless and seeded types, has far-reaching significance for enhancing quality control in the watermelon industry. However, interference in one-dimensional spectra affects the hi...
In order to exploit the advantages of CNN models in the detection of small floating targets on the sea surface, this paper proposes a new framework for encoding radar echo Doppler spectral sequences into images and explores two different ways of enco...
This paper addresses the critical issue of fault detection and prediction in electric motor machinery, a prevalent challenge in industrial applications. Faults in these machines, stemming from mechanical or electrical issues, often lead to performanc...
As a core component of the gearbox, bearings are crucial to the stability and reliability of the transmission system. However, dynamic variations in operating conditions and complex noise interference present limitations for existing fault diagnosis...
To address the challenges of extracting mechanical fault features in Gas Insulated Switchgear (GIS) under complex operating conditions and the insufficient diagnostic accuracy associated with traditional one-dimensional time-series signals, this pape...
Bearings are crucial components in motor bearing transmission systems because they reduce friction and support loads. Therefore, bearing fault diagnosis is particularly important. This paper proposes a fault diagnosis method for motor bearing transmi...
In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed...
Rapid AI adoption has intensified public and media attention toward AI-related developments, making news sentiment an increasingly important indicator of expectations and perceptions. This study constructs a global daily AI News Sentiment Index using...
Quality deterioration of chilled lamb during storage poses a challenge to meat quality and safety control, making rapid and accurate freshness-grade classification essential. Existing methods based on either spectral information or RGB image informat...
Plunger lift systems are vital for optimizing production in gas wells, but their performance can be compromised by various operational anomalies. Traditional diagnostic methods and conventional convolutional neural network (CNN) approaches often stru...
Recursive neural networks and transformers have recently become dominant in hyperspectral (HS) image classification due to their ability to capture long-range dependencies in spectral sequences. Despite the success of these sequential architectures,...
Focusing on the low accuracy and timeliness of traditional fault diagnosis methods for rolling bearings which combine massive amounts of data, a fault diagnosis method for rolling bearings based on Gramian angular field (GAF) coding technology and an...
To address the issues of insufficient rigor in existing methods for quantifying apple watercore severity and the complexity and low accuracy of traditional classification models, this study proposes a method for watercore quantification and a classif...
High-impedance fault detection poses significant challenges for distribution network maintenance and operation. We propose a dual-path neural network for high-impedance fault detection. To enhance feature extraction, we use a Gramian Angular Field al...
The data processing of airborne full-waveform light detection and ranging (LiDAR) systems has become a research hotspot in the LiDAR field in recent years. However, the accuracy and reliability of full-waveform classification remain a challenge. The...
Recursive Plots (RPs) can fully utilize the information of signals on a time scale, but their application involves the issue of manual threshold selection, and different thresholds have a significant impact on the analysis results of recursive plots,...
Time-series data generally exists in many application fields, and the classification of time-series data is one of the important research directions in time-series data mining. In this paper, univariate time-series data are taken as the research obje...
Data imbalances present a serious problem for intelligent fault diagnosis. They can lead to reduced diagnostic precision, which can jeopardize equipment reliability and safety. Based on that, this paper proposes a novel fault diagnosis method combini...
Forecasting energy demand is critical to ensure the steady operation of the power system. However, present approaches to estimating power load are still unsatisfactory in terms of accuracy, precision, and efficiency. In this paper, we propose a novel...
For most rotating mechanical transmission systems, condition monitoring and fault diagnosis of the gearbox are of great significance to avoid accidents and maintain stability in operation. To strengthen the comprehensiveness of feature extraction and...
A fractional-order image enhancement and fractal complexity-guided fusion method was developed to improve weak fault representation and introduce complexity-aware priors into dissolved gas analysis (DGA)-based diagnosis of oil-immersed power transfor...
Tool wear is a major challenge in sheet-metal forming, as it directly affects product quality and process stability. Reliable monitoring of tool-wear conditions is therefore essential, yet it remains challenging due to limited data availability and u...
Congenital heart disease (CHD) is a major cause of infant morbidity and mortality, and timely diagnosis remains difficult where advanced imaging is not consistently available. Automated phonocardiogram (PCG) screening can support early triage, but pe...
Low surface quality, undesired geometrical and dimensional tolerances, and product damage due to tool wear and tool breakage lead to a dramatic increase in production cost. In this regard, monitoring tool conditions and the machining process are cruc...
To address the challenge of detecting weak targets with small radar cross-sections (RCS), this work explores an integrated framework that leverages cross-level multimodal fusion of radar echoes. This method considers the target’s motion propert...
Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in ti...
Due to the broad frequency domain and nonlinear characteristics of ferroresonance signals, traditional time–frequency analysis methods often face challenges such as misjudgment, difficulty in threshold setting, and noise interference when extra...
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