Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A hybrid prototype selection-based deep learning approach for anomaly detection in industrial machines
Rodrigo de Paula Monteiro, Mariela Cerrada, Diego Cabrera, et al.
Expert Systems with Applications (2022) Vol. 204, pp. 117528-117528
Closed Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

Real-time AIoT anomaly detection for industrial diesel generator based an efficient deep learning CNN-LSTM in industry 4.0
Thao Nguyen-Da, Phuong Nguyen Thanh, Ming-Yuan Cho
Internet of Things (2024) Vol. 27, pp. 101280-101280
Closed Access | Times Cited: 8
Anomaly Detection Solutions: The Dynamic loss Approach in VAE for Manufacturing and IoT Environment
Praveen Vijai, P. Bagavathi Sivakumar
Results in Engineering (2025) Vol. 25, pp. 104277-104277
Open Access
A novel fault detection method for rotating machinery based on self-supervised contrastive representations
Zhe Yang, Yunwei Huang, Faisal Nazeer, et al.
Computers in Industry (2023) Vol. 147, pp. 103878-103878
Closed Access | Times Cited: 14
CE-FFGAN: A feature fusion generative adversarial network with deep embedded category information for limited sample fault diagnosis of rotating machinery under speed variation
Chen Yang, Hongkun Li, Shunxin Cao, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102605-102605
Closed Access | Times Cited: 4
Cost-effective intelligent building: Energy management system using machine learning and multi-criteria decision support
Hong Cai, Wanhao Zhang, Qiong Yuan, et al.
Energy Economics (2025), pp. 108184-108184
Closed Access
Deep Learning in Industrial Machinery: A Critical Review of Bearing Fault Classification Methods
Attiq Ur Rehman, Weidong Jiao, Yonghua Jiang, et al.
Applied Soft Computing (2025), pp. 112785-112785
Closed Access
An improved method of AUD-YOLO for surface damage detection of wind turbine blades
Li Zou, Anqi Chen, Xinhua Yang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Adversarial neighbor perception network with feature distillation for anomaly detection
Yuting Su, Enqi Su, Weiming Wang, et al.
Expert Systems with Applications (2025), pp. 126911-126911
Closed Access
Anomaly Detection in Industrial Machinery Using IoT Devices and Machine Learning: A Systematic Mapping
Sérgio F. Chevtchenko, Élisson da Silva Rocha, Monalisa Cristina Moura Dos Santos, et al.
IEEE Access (2023) Vol. 11, pp. 128288-128305
Open Access | Times Cited: 11
A two-stage adversarial Transformer based approach for multivariate industrial time series anomaly detection
Junfu Chen, Dechang Pi, Xixuan Wang
Applied Intelligence (2024) Vol. 54, Iss. 5, pp. 4210-4229
Closed Access | Times Cited: 3
A Neural-Symbolic Network for Interpretable Fault Diagnosis of Rolling Element Bearings Based on Temporal Logic
Ruoyao Tian, Meijie Cui, Gang Chen
IEEE Transactions on Instrumentation and Measurement (2024) Vol. 73, pp. 1-14
Closed Access | Times Cited: 3
TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
Ching Chang, Chiao-Tung Chan, Wei‐Yao Wang, et al.
2022 IEEE 38th International Conference on Data Engineering (ICDE) (2024), pp. 625-638
Open Access | Times Cited: 2
Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders
E.F. Souza, Cássio Bragança, Diogo Ribeiro, et al.
Railway Engineering Science (2024)
Open Access | Times Cited: 2
Teacher–student network for 3D point cloud anomaly detection with few normal samples
Jianjian Qin, Chunzhi Gu, Jun Yu, et al.
Expert Systems with Applications (2023) Vol. 228, pp. 120371-120371
Open Access | Times Cited: 6
TSI-based hierarchical clustering method and regular-hypersphere model for product quality detection
Hao Xie, Shaowu Lu, Xiaoqi Tang
Computers & Industrial Engineering (2023) Vol. 177, pp. 109094-109094
Closed Access | Times Cited: 5
Learnable product quantization for anomaly detection
Shi Zhang, Weilin Chen, Binlong Lu, et al.
Neurocomputing (2024) Vol. 582, pp. 127532-127532
Closed Access | Times Cited: 1
MCAD: Multi-classification anomaly detection with relational knowledge distillation
Zhuo Li, Yifei Ge, Xuebin Yue, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 23, pp. 14543-14557
Open Access | Times Cited: 1
The survey of industrial anomaly detection for industry 5.0
Long Wen, Yang Zhang, Wentao Hu, et al.
International Journal of Computer Integrated Manufacturing (2024), pp. 1-22
Closed Access | Times Cited: 1
A Locally Distributed Rough Set model for Feature Selection and Prototype Learning
Shuang An, Y.J. Song, Changzhong Wang, et al.
Fuzzy Sets and Systems (2024), pp. 109137-109137
Closed Access | Times Cited: 1
DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades
Li Zou, Anqi Chen, Chunzi Li, et al.
Applied Sciences (2024) Vol. 14, Iss. 19, pp. 8763-8763
Open Access | Times Cited: 1
AnoOnly: Semi-supervised anomaly detection with the only loss on anomalies
Yixuan Zhou, Peiyu Yang, Yi Qu, et al.
Expert Systems with Applications (2024), pp. 125597-125597
Closed Access | Times Cited: 1
Adversarial Fault Detector Guided by One-Class Learning for a Multistage Centrifugal Pump
Diego Cabrera, Mauricio Villacís, Mariela Cerrada, et al.
IEEE/ASME Transactions on Mechatronics (2022) Vol. 28, Iss. 3, pp. 1395-1403
Closed Access | Times Cited: 7
Clustering-Based Granular Representation of Time Series With Application to Collective Anomaly Detection
Wen Shi, Dimka Karastoyanova, Yongsheng Ma, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-12
Open Access | Times Cited: 3
Fault Classification in a Reciprocating Compressor and a Centrifugal Pump Using Non-Linear Entropy Features
Rubén Medina, Mariela Cerrada, Shuai Yang, et al.
Mathematics (2022) Vol. 10, Iss. 17, pp. 3033-3033
Open Access | Times Cited: 4
MITDCNN: A multi-modal input Transformer-based deep convolutional neural network for misfire signal detection in high-noise diesel engines
Wenjie Li, Xiangpeng Liu, D.H. Wang, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121797-121797
Closed Access | Times Cited: 2
Page 1 - Next Page

Scroll to top