Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure Though widely investigated past couple decades, continued advancement still desirable improve upon existing techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior variable working conditions multiple severities. In current work, a two-layered...

To prevent potential instability the early detection of cracks is imperative due to prevalent use concrete in critical infrastructure. Automated techniques leveraging artificial intelligence, machine learning, and deep learning as traditional manual inspection methods are time-consuming. The existing automated crack algorithms, despite recent advancements, face challenges robustness, particularly precise amidst complex backgrounds visual distractions, while also maintaining low inference...

This article proposes a fault diagnosis (FD) method that is based on bispectrum analysis and convolutional neural network (CNN) to identify bearing faults under inconsistent working conditions, such as high shaft speed variations with cracks of multiple scales compound faults. First, the bispectra vibration signals are extracted, which exhibit consistent patterns conditions. Second, CNN stochastic optimization function (AdaMax) proposed extract interclass representations from bispectra,...

In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) to analyze vibration signals variable speed and load conditions, (2) statistical feature extraction introduced capture significance from invariant pattern of analyzed by STC, (3) selection process introducing wrapper-based selector—Boruta, (4) filtration considered top selector avoid...

Due to enhanced safety, cost‐effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area research over the past several decades. Many algorithms have developed that can efficiently classify faults under constant speed conditions. However, performances these traditional deteriorate with fluctuations shaft speed. In couple years, deep learning not only improved classification performance in various disciplines (e.g., image...

This paper proposes a reliable leak detection method for water pipelines under different operating conditions. approach segments acoustic emission (AE) signals into short frames based on the Hanning window, with an overlap of 50%. After segmentation from each frame, intermediate quantity, which contains symptoms and keeps its characteristic adequately stable even when environmental conditions change, is calculated. Finally, k-nearest neighbor (KNN) classifier trained using features extracted...

Spherical storage tanks are used in various industries to store substances like gasoline, oxygen, waste water, and liquefied petroleum gas (LPG). Cracks the unaccepted defects, as can leak or spill contained substance through these cracks. Leakage from hazardous contaminate environment may lead fatal accidents. Therefore, ability detect cracks spherical is necessary avoid damage ensure public safety. In this paper, we present a crack detection case study of tank. The was performed using...

Keloid treatment remains a challenging task due to the high recurrence rates and limited effectiveness of monotherapies. Objective: To determine efficacy combining intralesional Triamcinolone Acetonide (TA) with cryotherapy. Methods: A quasi-experimental study was conducted at Dermatology Department, Nishtar Hospital, Multan, over 6 months from 30th June 2024, December 31, 2024. Eighty patients were enrolled using non-probability consecutive sampling. Baseline characteristics, including...

Pressure vessels (PV) are designed to hold liquids, gases, or vapors at high pressures in various industries, but a ruptured pressure vessel can be incredibly dangerous if cracks not detected the early stage. This paper proposes robust crack identification technique for using genetic algorithm (GA)-based feature selection and deep neural network (DNN) an acoustic emission (AE) examination. First, hybrid features extracted from multiple AE sensors that represent diverse symptoms of faults....

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis time, frequency, or time-frequency domain data. These engineered features susceptible to variations under inconsistent machine operation due the non-stationary, non-linear, complex nature recorded vibration signals. To address these issues, numerous deep...

Abstract Identification of damage and selection a restoration strategy in concrete structures is contingent upon automatic inspection for crack detection assessment. Most research on deep learning models autonomous has focused solely measuring dimensions, omitting the generalization power model. This utilizes novel step transfer (STL) added extreme machine (ELM) approach to develop an assessment surface cracks structures. STL helpful mining generalized abstract features from different sets...

Abstract This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned computer vision (CV) based automated strategies, incorporating object detection image segmentation techniques. Recent efforts integrated complex techniques such as deep convolutional neural networks (DCNNs) transformers for task. However, these encounter challenges in localizing cracks....

The You Only Look Once (YOLO) network is considered highly suitable for real-time object detection tasks due to its characteristics, such as high speed, single-shot detection, global context awareness, scalability, and adaptability real-world conditions. This work introduces a comprehensive analysis of various YOLO models detecting cracks in concrete structures, aiming assist the selection an optimal model future segmentation tasks. are initially trained on dataset containing both images...

Boiler heat exchange in thermal power plants involves tubes to transfer from the fuel water. tube leakage can cause outages and huge generation loss. Therefore, early detection of leaks boiler is necessary avoid such accidents. In this study, a leak classification mechanism was designed using wavelet packet transform (WPT) analysis acoustic emission (AE) signals acquired fully connected deep neural network (FC-DNN). WPT AE enabled extraction features associated with different conditions...

A robot manipulator is a multi-degree-of-freedom and nonlinear system that used in various applications, including the medical area automotive industries. Uncertain conditions which operates, as well its nonlinearities, represent challenges for fault diagnosis fault-tolerant control (FDC) are addressed through proposed FDC technique. machine-learning-based neural adaptive, high-order, variable structure observer (FD) modern, fuzzy, backstepping, use (FC) algorithm, this paper. In first...

This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB/Simulink model is developed based on electrical parameters to simulate the short circuit by adding resistor parallel phase “a” of rotor. The resulting high current new indicates presence circuit. By measuring stator three-phase currents, fault can be detected as currents exhibit asymmetric behavior. Fluctuations...

The performance of wireless networks is related to the optimized structure antenna. Therefore, in this paper, a Machine Learning (ML)-assisted new methodology named Self-Adaptive Bayesian Neural Network (SABNN) proposed, aiming optimize antenna pattern for next-generation networks. In addition, statistical analysis presented SABNN evaluated paper and compared with current Gaussian Process (GP). training cost convergence speed are also discussed paper. final stage, proposed model's measured...

Abstract Lung cancer holds the highest fatality rate among cancers, emphasizing importance of early detection. Computer algorithms have gained prominence across various domains, including lung diagnosis. These assist specialists, especially in medical imaging, yet current efforts lack comprehensive CT data analysis; handling imbalanced datasets and fully exploiting spatial information. The analysis hinders ability to identify subtle variations texture structure that are crucial for detecting...