An Unsupervised Fault Warning Method Based on Hybrid Information Gain and a Convolutional Autoencoder for Steam Turbines DOI Creative Commons

Jinxing Zhai,

Jing Ye, Yue Cao

и другие.

Energies, Год журнала: 2024, Номер 17(16), С. 4098 - 4098

Опубликована: Авг. 18, 2024

Renewable energy accommodation in power grids leads to frequent load changes plants. Sensitive turbine fault monitoring technology is critical ensure the stable operation of system. Existing techniques do not use information sufficiently and are sensitive early signs. To solve this problem, an unsupervised warning method based on hybrid gain a convolutional autoencoder (CAE) for intermediate flux proposed. A high-precision intermediate-stage prediction model established using CAE. The calculation proposed filter features multi-dimensional sensors. Hampel time series outlier detection introduced deal with factors such as sensor faults noise. achieves highest diagnosis accuracy through experiments real data compared traditional methods. Real show that relatively improves diagnostic by average 2.12% gate recurrent unit networks, long short-term memory other models. Meanwhile, can effectively improve models, maximum 1.89% relative improvement. noteworthy its superiority applicability.

Язык: Английский

Interpretable degradation tensor modeling through multi-scale and multi-level time-frequency feature fusion for machine health monitoring DOI
Tongtong Yan,

Xueqi Xing,

Dong Wang

и другие.

Information Fusion, Год журнала: 2025, Номер 117, С. 102935 - 102935

Опубликована: Янв. 5, 2025

Язык: Английский

Процитировано

3

A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis DOI
You Keshun, Wang Puzhou, Peng Huang

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер unknown, С. 110556 - 110556

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

18

A Three-Channel Feature Fusion Approach Using Symmetric ResNet-BiLSTM Model for Bearing Fault Diagnosis DOI Open Access
Yingyong Zou, Tao Liu,

Xingkui Zhang

и другие.

Symmetry, Год журнала: 2025, Номер 17(3), С. 427 - 427

Опубликована: Март 12, 2025

For mechanical equipment to operate normally, rolling bearings—which are crucial parts of rotating machinery—need have their faults diagnosed. This work introduces a bearing defect diagnosis technique that incorporates three-channel feature fusion and is based on enhanced Residual Networks Bidirectional long- short-term memory networks (ResNet-BiLSTM) model. The can effectively establish spatial-temporal relationships better capture complex features in data by combining the powerful spatial extraction capability ResNet bidirectional temporal modeling BiLSTM. Specifically, one-dimensional vibration signals first transformed into two-dimensional images using Continuous Wavelet Transform (CWT) Markov Transition Field (MTF). upgraded ResNet-BiLSTM network then used extract combine original signal along with from two types images. Finally, experimental validation performed datasets. results show compared other state-of-the-art models, computing cost greatly reduced, params flops at 15.4 MB 715.24 MB, respectively, running time single batch becomes 5.19 s. fault accuracy reaches 99.53% 99.28% for datasets, successfully realizing classification task.

Язык: Английский

Процитировано

1

An interpretable integration fusion time-frequency prototype contrastive learning for machine fault diagnosis with limited labeled samples DOI
Yutong Dong, Hongkai Jiang, Xin Wang

и другие.

Information Fusion, Год журнала: 2025, Номер 124, С. 103340 - 103340

Опубликована: Май 29, 2025

Язык: Английский

Процитировано

1

A novel dynamic machine learning-based eXplainable fusion monitoring: Application to industrial and chemical processes DOI Creative Commons
Husnain Ali, Rizwan Safdar, Yuanqiang Zhou

и другие.

Machine Learning Science and Technology, Год журнала: 2024, Номер 6(1), С. 015005 - 015005

Опубликована: Дек. 17, 2024

Abstract The complexity and fusion dynamism of the modern industrial chemical sectors have been increasing with rapid progress IR 4.0–5.0. transformative characteristics Industry 4.0–5.0 not fully explored in terms fundamental importance explainability. Traditional monitoring techniques for automatic anomaly detection, identifying potential variables, root cause analysis fault information are intelligent enough to tackle intricate problems real-time practices sectors. This study presents a novel dynamic machine learning based explainable approach address issues process systems. methodology aims detect faults, identify their key causes feature analyze path propagation time magnitude one variable another impact. proposed using domain multivariate granger-entropy-aided independent component (DICA)—distributed canonical correlation approach, incorporating dynamics wrapping supported delay-signed directed graph. utilized application processes verified continuous stirred tank reactor Tennessee Eastman as practical benchmarks. framework’s validations efficiency evaluated established such classic computed ICA DICA standard model scenarios. outcomes results showed that newly developed strategy is preferable previous approaches regarding explainability robust detection identification actual high FDRs low FARs.

Язык: Английский

Процитировано

5

Physical knowledge-driven feature fusion and reconstruction network for fault diagnosis with incomplete multisource data DOI
Dingyi Sun, Yongbo Li, Sixiang Jia

и другие.

Mechanical Systems and Signal Processing, Год журнала: 2024, Номер 225, С. 112222 - 112222

Опубликована: Дек. 19, 2024

Язык: Английский

Процитировано

3

Fault diagnosis method based on multimodal-deep tensor projection network under variable working conditions DOI
Li Zhi, Chenyu Liu, Wenjing Huang

и другие.

Mechanical Systems and Signal Processing, Год журнала: 2025, Номер 225, С. 112336 - 112336

Опубликована: Янв. 13, 2025

Язык: Английский

Процитировано

0

Power Transformer Prognostics and Health Management Using Machine Learning: A Review and Future Directions DOI Creative Commons
Ryad Zemouri

Machines, Год журнала: 2025, Номер 13(2), С. 125 - 125

Опубликована: Фев. 7, 2025

Power transformers (PTs) play a vital role in the electrical power system. Assessing their health to predict remaining useful life is essential optimise maintenance. Scheduling right maintenance for equipment at time ultimate goal of any system utility. Optimal has number benefits: human and social, by limiting sudden service interruptions, economic, due direct indirect costs unscheduled downtime. PT now produces large amounts easily accessible data increasing use IoT, sensors, connectivity between physical assets. As result, transformer prognostics management (PT-PHM) methods are increasingly moving towards artificial intelligence (AI) techniques, with several hundreds scientific papers published on topic PT-PHM using AI techniques. On other hand, world undergoing new evolution third generation models: large-scale foundation models. What current state research PT-PHM? trends challenges where do we need go management? This paper provides comprehensive review art analysing more than 200 papers, mostly journals. Some elements guide given end document.

Язык: Английский

Процитировано

0

A nonlinear dynamics method using multi-sensor signal fusion for fault diagnosis of rotating machinery DOI
Fei Chen, Jie Liu, Xiaoxi Hu

и другие.

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103190 - 103190

Опубликована: Фев. 22, 2025

Язык: Английский

Процитировано

0

A novel diffusion model with Shapley value analysis for anomaly detection and identification of wind turbine DOI
Qingtao Yao,

Bohua Chen,

Aijun Hu

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер 284, С. 127925 - 127925

Опубликована: Апрель 28, 2025

Язык: Английский

Процитировано

0