Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review

Vibration, Journal Year: 2024, Volume and Issue: 7(4), P. 1013 - 1062

Published: Oct. 31, 2024

Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these often leads costly downtime and potential safety risks, further emphasizing the importance monitoring health state. Vibration signal analysis is now a common approach for this purpose, it provides useful information related dynamic behavior machines. This research aimed conduct comprehensive examination current methodologies employed stages vibration analysis, which encompass preprocessing, post-processing phases, ultimately leading application Artificial Intelligence-based diagnostics prognostics. An extensive search was conducted various databases, including ScienceDirect, IEEE, MDPI, Springer, Google Scholar, 2020 early 2024 following PRISMA guidelines. Articles that aligned with at least one targeted topics cited above provided unique methods explicit results qualified retention, while those were redundant or did not meet established inclusion criteria excluded. Subsequently, 270 articles selected an initial pool 338. The review highlighted several deficiencies preprocessing step experimental validation, implementation rates 15.41% 10.15%, respectively, prototype studies. Examination processing phase revealed time scale decomposition have become essential accurate signals, they facilitate extraction complex remains obscured original, undecomposed signals. Combining such time–frequency shown be ideal combination extraction. In context fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), random forests been identified five most frequently algorithms. Meanwhile, transformer-based models are emerging promising venue prediction RUL values, along data transformation. Given conclusions drawn, future researchers urged investigate interpretability integration diagnosis prognosis developed aim applying them real-time contexts. Furthermore, there need studies disclose details datasets operational conditions machinery, thereby improving reproducibility. Another area warrants investigation differentiation types present signals obtained bearings, defect overall system embedded within

Language: Английский

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Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search

Published: Sept. 21, 2024

Instruments such as eye-tracking devices have contributed to understanding how users interact with screen-based search engines.However, user-system interactions in audio-only channels -as is the case for Spoken Conversational Search (SCS) -are harder characterize, given lack of instruments effectively and precisely capture interactions.Furthermore, this era information overload, cognitive bias can significantly impact we seek consume -especially context controversial topics or multiple viewpoints.This paper draws upon insights from disciplines (including seeking, psychology, science, wearable sensors) provoke novel conversations community.To end, discuss future opportunities propose a framework including multimodal methods experimental designs settings.We demonstrate preliminary results an example.We also outline challenges offer suggestions adopting approach, ethical considerations, assist researchers practitioners exploring biases SCS.

Language: Английский

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Challenges and new perspectives of developmental cognitive EEG studies

NeuroImage, Journal Year: 2022, Volume and Issue: 260, P. 119508 - 119508

Published: July 23, 2022

Despite shared procedures with adults, electroencephalography (EEG) in early development presents many specificities that need to be considered for good quality data collection. In this paper, we provide an overview of the most representative cognitive developmental EEG studies focusing on neuroimaging technique young participants, such as attrition and artifacts. We also summarize results research obtained time time-frequency domains use more advanced signal processing methods. Finally, briefly introduce three recent standardized pipelines will help promote replicability comparability across experiments ages. While paper does not claim exhaustive, it aims give a sufficiently large challenges solutions available conduct robust studies.

Language: Английский

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Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

Brain Sciences, Journal Year: 2022, Volume and Issue: 12(10), P. 1275 - 1275

Published: Sept. 22, 2022

Electroencephalography (EEG) records the electrical activity of brain, which is an important tool for automatic detection epileptic seizures. It certainly a very heavy burden to only recognize EEG epilepsy manually, so method computer-assisted treatment great importance. This paper presents seizure algorithm based on variational modal decomposition (VMD) and deep forest (DF) model. Variational performed recordings, first three functions (VMFs) are selected construct time–frequency distribution signals. Then, log−Euclidean covariance matrix (LECM) computed represent properties form features. The model applied complete signal classification, non-neural network with cascade structure that performs feature learning through forest. In addition, improve classification accuracy, postprocessing techniques generate discriminant results by moving average filtering adaptive collar expansion. was evaluated Bonn dataset Freiburg long−term dataset, former achieved sensitivity specificity 99.32% 99.31%, respectively. mean this 21 patients in were 95.2% 98.56%, respectively, false rate 0.36/h. These demonstrate superior performance advantage our indicate its research potential detection.

Language: Английский

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Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(3), P. 1512 - 1512

Published: Jan. 23, 2023

The detection of mental fatigue is an important issue in the nascent field neuroergonomics. Although machine learning approaches and especially deep designs have constantly demonstrated their efficiency to automatically detect critical features from raw data, computational resources for training predictions are usually very demanding. In this work, we propose a shallow convolutional neural network, with three layers, using electroencephalogram (EEG) data that can alleviate burden provide fast detection. As such, model was created utilizing time-frequency domain features, extracted Morlet wavelet analysis. These combined higher-level characteristics learnt by model, resulted resilient solution, able attain high prediction accuracy (97%), while reducing time computing costs. Moreover, incorporating subsequent SHAP values analysis on contributed creation, indications low frequency (theta alpha band) brain wave were indicated as prominent detectors.

Language: Английский

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A comprehensive survey of complex brain network representation

Meta-Radiology, Journal Year: 2023, Volume and Issue: 1(3), P. 100046 - 100046

Published: Nov. 1, 2023

Recent years have shown great merits in utilizing neuroimaging data to understand brain structural and functional changes, as well its relationship different neurodegenerative diseases other clinical phenotypes. Brain networks, derived from modalities, attracted increasing attention due their potential gain system-level insights characterize dynamics abnormalities neurological conditions. Traditional methods aim pre-define multiple topological features of networks relate these measures or demographical variables. With the enormous successes deep learning techniques, graph played significant roles network analysis. In this survey, we first provide a brief overview neuroimaging-derived networks. Then, focus on presenting comprehensive both traditional state-of-the-art deep-learning for mining. Major models, objectives are reviewed within paper. Finally, discuss several promising research directions field.

Language: Английский

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Electrophysiological correlates of inhibitory control in children: Relations with prenatal maternal risk factors and child psychopathology

Development and Psychopathology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 14

Published: April 24, 2024

Abstract Inhibitory control plays an important role in children’s cognitive and socioemotional development, including their psychopathology. It has been established that contextual factors such as socioeconomic status (SES) parents’ psychopathology are associated with inhibitory control. However, the relations between neural correlates of have rarely examined longitudinal studies. In present study, we used both event-related potential (ERP) components time-frequency measures to evaluate pathways factors, prenatal SES maternal psychopathology, behavioral emotional problems a large sample children ( N = 560; 51.75% females; M age 7.13 years; Range 4–11 years). Results showed theta power, which was positively predicted by negatively related externalizing problems, mediated negative relation them. ERP amplitudes latencies did not mediate association risk (i.e., psychopathology) internalizing problems. Our findings increase our understanding linking early

Language: Английский

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Time–frequency dynamics of error monitoring in childhood: An EEG study

Developmental Psychobiology, Journal Year: 2022, Volume and Issue: 64(3)

Published: Feb. 28, 2022

Abstract Error monitoring allows individuals to monitor and adapt their behavior by detecting errors. is thought develop throughout childhood adolescence. However, most of this evidence comes from studies in late adolescence utilizing event‐related potentials (ERPs). The current study utilizes time–frequency (TF) connectivity analyses provide a comprehensive examination age‐related changes error‐monitoring processes across early ( N = 326; 50.9% females; 4–9 years). ERP indicated the presence error‐related negativity (ERN) error positivity (Pe) all ages. Results showed no error‐specific ERN Pe. TF suggested frontocentral responses delta theta signal strength (power), consistency (intertrial phase synchrony), synchrony (interchannel synchrony) between frontrocentral frontolateral clusters—all which increased with age. Additionally, examines reliability effect size estimates measures. For measures, more trials were needed achieve acceptable than what commonly used psychophysiological literature. Resources facilitate measurement reporting are provided. Overall, findings highlight utility useful information for future examining development monitoring.

Language: Английский

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After-Effects of Parieto-Occipital Gamma Transcranial Alternating Current Stimulation on Behavioral Performance and Neural Activity in Visuo-Spatial Attention Task

Published: Jan. 1, 2025

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

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AI-Enhanced Neurophysiological Assessment

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Journal Year: 2025, Volume and Issue: unknown, P. 33 - 64

Published: Jan. 3, 2025

Advancements in artificial intelligence (AI) are revolutionizing neurophysiology, enhancing precision and efficiency assessing brain nervous system function. AI-driven neurophysiological assessment integrates machine learning, deep neural networks, advanced data analytics to process complex from electroencephalography, electromyography techniques. This technology enables earlier diagnosis of neurological disorders like epilepsy Alzheimer's by detecting subtle patterns that may be missed human analysis. AI also facilitates real-time monitoring predictive analytics, improving outcomes critical care neurorehabilitation. Challenges include ensuring quality, addressing ethical concerns, overcoming computational limits. The integration into neurophysiology offers a precise, scalable, accessible approach treating disorders. chapter discusses the methodologies, applications, future directions assessment, emphasizing its transformative impact clinical research fields.

Language: Английский

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