Biologically inspired compound defect detection using a spiking neural network with continuous time–frequency gradients

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103132 - 103132

Опубликована: Янв. 24, 2025

Язык: Английский

---

Mechanical fault diagnosis based on deep transfer learning: a review

Measurement Science and Technology, Год журнала: 2023, Номер 34(11), С. 112001 - 112001

Опубликована: Июль 17, 2023

Abstract Mechanical fault diagnosis is an important method to accurately identify the health condition of mechanical equipment and ensure its safe operation. With advent era ‘big data’, it inevitable trend choose deep learning for diagnosis. At same time, improve generalization ability applications in different scenarios diagnosis, based on transfer has also been proposed become branch field This paper introduces principle learning, summarizes research application discusses shortcomings future direction

Язык: Английский

---

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Processes, Год журнала: 2024, Номер 12(2), С. 251 - 251

Опубликована: Янв. 24, 2024

This paper presents a comprehensive review of the historical development, current state art, and prospects data-driven approaches for industrial process monitoring. The subject covers vast diverse range works, which are compiled critically evaluated based on different perspectives they provide. Data-driven modeling techniques surveyed categorized into two main groups: multivariate statistics machine learning. Representative models, namely principal component analysis, partial least squares artificial neural networks, detailed in didactic manner. Topics not typically covered by other reviews, such as data exploration treatment, software benchmarks availability, real-world implementations, thoroughly analyzed. Finally, future research discussed, covering aspects related to system performance, significance usefulness approaches, development environment. work aims be reference practitioners researchers navigating extensive literature

Язык: Английский

---

Improved Binary Meerkat Optimization Algorithm for efficient feature selection of supervised learning classification

Knowledge-Based Systems, Год журнала: 2024, Номер 292, С. 111616 - 111616

Опубликована: Март 7, 2024

Язык: Английский

---

Reinforcement Learning in Process Industries: Review and Perspective

IEEE/CAA Journal of Automatica Sinica, Год журнала: 2024, Номер 11(2), С. 283 - 300

Опубликована: Янв. 29, 2024

This survey paper provides a review and perspective on intermediate advanced reinforcement learning (RL) techniques in process industries. It offers holistic approach by covering all levels of the control hierarchy. The presents comprehensive overview RL algorithms, including fundamental concepts like Markov decision processes different approaches to RL, such as value-based, policy-based, actor-critic methods, while also discussing relationship between classical RL. further reviews wide-ranging applications industries, soft sensors, low-level control, high-level distributed fault detection tolerant optimization, planning, scheduling, supply chain. discusses limitations advantages, trends new applications, opportunities future prospects for Moreover, it highlights need complex systems due growing importance digitalization

Язык: Английский

---

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system

PLoS ONE, Год журнала: 2024, Номер 19(2), С. e0296471 - e0296471

Опубликована: Фев. 21, 2024

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents novel methodology that employs Adaptive Crow Search Algorithm (ACSA) to improve identification capabilities mitigate occurrence alarms TEP. ACSA an optimization approach draws inspiration from observed behavior crows their natural environment. algorithm possesses capability adapt its search response changing dynamics process. primary objective our research devise monitoring strategy adaptable nature, with aim efficiently identifying faults within TEP while simultaneously minimizing alarms. applied order enhance variables, thresholds, decision criteria selection configuration. When compared traditional static approaches, ACSA-based better at finding reducing because it adapts well changes process disturbances. In assess efficacy suggested methodology, we have conducted comprehensive simulations on dataset. findings suggest based demonstrates superior rates concurrently mitigating frequency addition, flexibility allows manage variations, disturbances, uncertainties, thereby enhancing robustness reliability practical scenarios. To validate proposed approach, extensive were results indicate achieves higher Moreover, adaptability enables effectively handle making robust reliable real-world applications. contributions this extend beyond TEP, adaptive utilizing can be other complex processes. study provide valuable insights into development advanced techniques, offering significant benefits terms safety, reliability, operational efficiency.

Язык: Английский

---

A Review of Statistical-Based Fault Detection and Diagnosis with Probabilistic Models

Symmetry, Год журнала: 2024, Номер 16(4), С. 455 - 455

Опубликована: Апрель 8, 2024

As industrial processes grow increasingly complex, fault identification becomes challenging, and even minor errors can significantly impact both productivity system safety. Fault detection diagnosis (FDD) has emerged as a crucial strategy for maintaining reliability safety through condition monitoring abnormality recovery to manage this challenge. Statistical-based FDD methods that rely on large-scale process data their features have been developed detecting faults. This paper overviews recent investigations developments in statistical-based methods, focusing probabilistic models. The theoretical background of these models is presented, including Bayesian learning maximum likelihood. We then discuss various techniques methodologies, e.g., principal component analysis (PPCA), partial least squares (PPLS), independent (PICA), canonical correlation (PCCA), Fisher discriminant (PFDA). Several test statistics are analyzed evaluate the discussed methods. In processes, require complex matrix operation cost computational load. Finally, we current challenges future trends FDD.

Язык: Английский

---

A critical review on system architecture, techniques, trends and challenges in intelligent predictive maintenance

Safety Science, Год журнала: 2024, Номер 177, С. 106590 - 106590

Опубликована: Июнь 17, 2024

Язык: Английский

---

Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring

Smart Cities, Год журнала: 2024, Номер 7(3), С. 1261 - 1288

Опубликована: Май 28, 2024

Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely accurate fault detection maintain optimal generation. The considered case study focuses on an intelligent diagnosis (IFDD) system for the analysis radiometric infrared thermography (IRT) in a predictive maintenance setting, enabling remote inspection diagnostic monitoring power plant sites. proposed IFDD employs custom-developed deep learning approach which relies convolutional neural networks effective multiclass classification defect types. is challenging task issues such as IRT data scarcity, defect-patterns’ complexity, low image acquisition quality due noise calibration issues. Hence, this research carefully prepares customized high-quality but severely imbalanced six-class thermographic dataset panels. With respect previous approaches, numerical temperature values floating-point used train validate models. trained models display high accuracy efficient anomaly diagnosis. Finally, create trust system, process underlying model investigated with perceptive explainability, portraying most discriminant features, mathematical-structure-based interpretability, achieve feature clustering.

Язык: Английский

---

A relationship-aware calibrated prototypical network for fault incremental diagnosis of electric motors without reserved samples

Reliability Engineering & System Safety, Год журнала: 2024, Номер 252, С. 110429 - 110429

Опубликована: Авг. 12, 2024

Язык: Английский

---