Resilient Semi-Supervised Meta-Learning Network based on wavelet transform and K-means optimization for fluid classification DOI
Hengxiao Li, Shanchen Pang, Youzhuang Sun

et al.

Physics of Fluids, Journal Year: 2024, Volume and Issue: 36(12)

Published: Dec. 1, 2024

In the field of geological exploration, accurately distinguishing between different types fluids is crucial for development oil, gas, and mineral resources. Due to scarcity labeled samples, traditional supervised learning methods face significant limitations when processing well log data. To address this issue, paper presents a novel fluid classification method known as Resilient Semi-Supervised Meta-Learning Network (RSSMLN) based on wavelet transform K-means optimization, which combines advantages few-shot semi-supervised learning, aiming optimize recognition in Initially, study employs small set samples train initial model utilizes pseudo-label generation clustering prototypes, thereby enhancing model's accuracy generalization ability. Subsequently, during feature extraction process, preprocessing techniques are introduced enhance time-frequency representation data through multi-scale decomposition. This process effectively captures high-frequency low-frequency features, providing structured information subsequent convolution operations. By employing dual-channel heterogeneous convolutional kernel extractor, RSSMLN can capture subtle features significantly improve accuracy. Experimental results indicate that compared various standard deep models, achieves superior performance identification tasks. research provides reliable solution oilfield applications offers scientific support resource exploration evaluation.

Language: Английский

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework DOI Creative Commons
Muhammad Siddique, Zahoor Ahmad,

N. Ullah

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(12), P. 4009 - 4009

Published: June 20, 2024

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing non-local means and adaptive histogram equalization, results new enhanced leak-induced scalograms (ELIS) capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage a belief network (DBN) fine-tuned with genetic algorithm (GA) unified least squares support vector machine (LSSVM) to improve feature extraction classification accuracy. DBN-GA precisely extracts informative features, while LSSVM classifier distinguishes between leaky non-leak conditions. By concentrating solely on capabilities ELIS processed through optimized DBN-GA-LSSVM model, this research achieves high accuracy reliability, making significant contribution monitoring maintenance. innovative capturing complex patterns can be applied real-time leak critical infrastructure safety several industrial applications.

Language: Английский

Citations

17
Acoustic Emission-Based Pipeline Leak Detection and Size Identification Using a Customized One-Dimensional DenseNet DOI Creative Commons

Faisal Saleem,

Zahoor Ahmad, Muhammad Siddique

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1112 - 1112

Published: Feb. 12, 2025

Effective leak detection and size identification are essential for maintaining the operational safety, integrity, longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, excessive computational costs, which limits their feasibility real-time monitoring applications. This study presents a novel acoustic emission (AE)-based pipeline approach, integrating Empirical Wavelet Transform (EWT) adaptive frequency decomposition with customized one-dimensional DenseNet architecture achieve precise classification. The methodology begins EWT-based signal segmentation, isolates meaningful bands enhance leak-related feature extraction. To further improve quality, thresholding denoising techniques applied, filtering out low-amplitude while preserving critical diagnostic information. denoised signals processed using DenseNet-based deep learning model, combines convolutional layers densely connected propagation extract fine-grained temporal dependencies, ensuring accurate classification presence severity. Experimental validation was conducted on real-world AE data collected under controlled non-leak conditions at varying pressure levels. proposed model achieved an exceptional accuracy 99.76%, demonstrating its ability reliably differentiate between normal operation multiple severities. method effectively reduces costs robust performance across diverse operating environments.

Language: Английский

Citations

3
Research on non‐contact infrared imaging technique for multilayer storage identification of oil tanks based on an improved edge‐detection algorithm DOI Open Access
Y. Wei, Hongwei Chen, Yang Li

et al.

The Canadian Journal of Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 2, 2025

Abstract Detection of internal storage objects in tanks is crucial for production the petrochemical industry and chemical raw material storage. Compared to traditional methods, infrared detection provides benefits like non‐contact operation, safety, efficiency. In image processing, utilizing edge obtain information an advanced approach. By analyzing thermal texture tank images extracting boundaries between different regions, it possible predict volume To address issues noise, lack clarity, discontinuity existing a novel algorithm called wavelet transform mathematical morphological fusion improve (WMF‐IED) proposed. Roberts, Prewitt, Sobel, Laplacian Gaussian (LOG) WMF‐IED offers several advantages. It not only clear continuous edges but also exhibits minimal mean squared error (MSE). Additionally, achieves maximum signal‐to‐noise ratio (SNR) peak (PSNR). These factors show proposed algorithm's superior performance. Moreover, experimental platform was designed constructed analyze contents using algorithm. The results demonstrate that has strong universality can detect various prediction errors are less than 4% 6% liquid level sludge detection, respectively. Based on analysis results, recommended sampling value proposed, which be selected minimum error.

Language: Английский

Citations

1
P-DETR: A Transformer-Based Algorithm for Pipeline Structure Detection DOI Creative Commons

Ibrahim Akinjobi Aromoye,

Lo Hai Hiung,

Patrick Sébastian

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104652 - 104652

Published: March 1, 2025

Language: Английский

Citations

1
Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution Autoencoder and Artificial Neural Network DOI Creative Commons
Wasim Zaman, Zahoor Ahmad, Jong-Myon Kim

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(3), P. 851 - 851

Published: Jan. 28, 2024

This paper proposes a new fault diagnosis method for centrifugal pumps by combining signal processing with deep learning techniques. Centrifugal facilitate fluid transport through the energy generated impeller. Throughout operation, variations in pressure at pump’s inlet may impact generalization of traditional machine models trained on raw statistical features. To address this concern, first, vibration signals are collected from pumps, followed application lowpass filter to isolate frequencies indicative faults. These then subjected continuous wavelet transform and Stockwell transform, generating two distinct time–frequency scalograms. The Sobel is employed further highlight essential features within these For feature extraction, approach employs parallel convolutional autoencoders, each tailored specific scalogram type. Subsequently, extracted merged into unified pool, which forms basis training two-layer artificial neural network, aim achieving accurate classification. proposed validated using three datasets obtained pump under varying pressures. results demonstrate classification accuracies 100%, 99.2%, 98.8% dataset, surpassing achieved reference comparison methods.

Language: Английский

Citations

8
Fault Diagnosis of a Multistage Centrifugal Pump Using Explanatory Ratio Linear Discriminant Analysis DOI Creative Commons
Saif Ullah, Zahoor Ahmad, Jong-Myon Kim

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(6), P. 1830 - 1830

Published: March 13, 2024

This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses challenge background noise and interference in vibration signals by identifying fault-sensitive frequency band (FSFB). From FSFB, raw hybrid statistical features are extracted time, frequency, time–frequency domains, forming comprehensive feature pool. Recognizing that not all adequately represent MCP conditions can reduce classification accuracy, we propose novel ER-LDA method. evaluates importance calculating between interclass distance intraclass scatteredness, facilitating selection discriminative through LDA. fusion ER-based assessment LDA yields technique. The resulting selective set is then passed into k-nearest neighbor (K-NN) algorithm condition classification, distinguishing normal, mechanical seal hole, scratch, impeller defect states MCP. proposed technique surpasses current cutting-edge techniques classification.

Language: Английский

Citations

7
Soundscape Characterization Using Autoencoders and Unsupervised Learning DOI Creative Commons
Daniel Alexis Nieto-Mora, Maria Cristina Ferreira de Oliveira, Camilo Sánchez‐Giraldo

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(8), P. 2597 - 2597

Published: April 18, 2024

Passive acoustic monitoring (PAM) through recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, diversity, community interactions, human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due the large volumes ARU data. In this work, we propose a non-supervised framework using autoencoders extract soundscape features. We applied dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based autoencoder features represents cluster information with prototype spectrograms centroid decoder part neural network. analysis provides valuable insights into distribution temporal patterns various sound compositions within study area. By utilizing autoencoders, identify significant characterized recurring intense types across multiple frequency ranges. comprehensive understanding area's allows us pinpoint crucial sources gain deeper its environment. results encourage further exploration unsupervised algorithms as promising alternative path for environmental changes.

Language: Английский

Citations

7
An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning DOI Creative Commons
Niamat Ullah, Zahoor Ahmad, Muhammad Siddique

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(21), P. 8850 - 8850

Published: Oct. 31, 2023

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in CP vibration signal are often attenuated due to background interference noises, thus affecting sensitivity traditional statistical features towards faults. Furthermore, extracting health-sensitive information from needs human expertise knowledge. To extract autonomously signals, proposed approach initially selects a healthy baseline signal. is then computed between obtained under different operating conditions, yielding coherograms. WCA processing technique that used measure degree linear correlation two signals as function frequency. coherograms carry about vulnerability faults color intensity changes according change health conditions. utilize conditions CP, they provided Convolution Neural Network (CNN) Autoencoder (CAE) extraction discriminant autonomously. CAE extracts global variations coherograms, CNN local related health. combined into single latent space vector. identify vector classified using Artificial (ANN). method identifies with higher accuracy compared already existing methods when it tested acquired real-world industrial CPs.

Language: Английский

Citations

13
TFC-TFECR feature extraction and state recognition of acoustic emission signal of cylindrical roller bearing DOI
Yang Yu, Yun Li, Ping Yang

et al.

Nondestructive Testing And Evaluation, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 19

Published: April 4, 2024

Rolling bearings are widely used in rotating machinery, such as aero-engine spindles, flying machines, wind turbines, etc. Bearing condition monitoring is of practical importance. The acoustic emission (AE) signal has impact and rapid attenuation characteristics. Most existing research on fault diagnosis not focused According to this characteristic, a time-frequency coherent energy change rate (TFC-TFECR) method proposed identify the AE signals bearing faults. This paper investigates effect (TFC) coefficient. It also focuses deviation TFC-TFECR method, which superior energy. Feature extraction from cylindrical roller carried out through three typical states bearings. feature values input into SVM model, sparrow search algorithm optimises model. experimental results show that can effectively realise state recognition bearings, accuracy reaches 99.3827% at 600 r/min 98.7654% 1200 r/min. provides new for non-destructive testing machinery

Language: Английский

Citations

4
Non-contact detection of leak-derived energy loss for an in-service pipeline system using digital image correlation DOI

Taiki Hagiwara,

Yohei Asada,

T. Tsubota

et al.

Nondestructive Testing And Evaluation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: Jan. 2, 2025

This study presents a novel method for no-contact detection of water leakage in pressurized pipelines using digital image correlation (DIC). focuses on pipe deformation caused by hammers including information, which combines the idea transient test-based techniques (TTBTs) and non-destructive testing (NDT). Water induces hydraulic energy loss damping both strain stored pipe. We focus detecting leak-derived this DIC. In experiment, we measured due to with DIC an in-service agricultural pipeline system. Experimental cases included conditions no leak, one leak at downstream upstream. As result, can detect hoop axial changes pressure pipeline. Based these changes, determine The further location is, greater is. provides non-contact system, enables us remotely safely inspect that are difficult access.

Language: Английский

Citations

0