{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T23:19:23Z","timestamp":1773703163380,"version":"3.50.1"},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"DOI":"10.1109\/iccad64771.2025.11099203","type":"proceedings-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T17:54:52Z","timestamp":1754502892000},"page":"1-7","source":"Crossref","is-referenced-by-count":2,"title":["Novelty Detection in Rotating Machinery: Assessment of Unsupervised Machine Learning Models for Medium-Sized Industrial Bearings"],"prefix":"10.1109","author":[{"given":"Luigi Gianpio Di","family":"Maggio","sequence":"first","affiliation":[{"name":"Politecnico di Torino,Dept. of Mechanical and Aerospace Engineering,Torino,Italy"}]},{"given":"Eugenio","family":"Brusa","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,Dept. of Mechanical and Aerospace Engineering,Torino,Italy"}]},{"given":"Cristiana","family":"Delprete","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,Dept. of Mechanical and Aerospace Engineering,Torino,Italy"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1201\/9781351228626"},{"key":"ref2","article-title":"Vibration-based condition monitoring: industrial, aerospace and automotive applications","author":"Randall","year":"2011","journal-title":"Mechanisms and Machine Science"},{"key":"ref3","volume-title":"Readiness for predictive maintenance at scale report 2023","author":"AG","year":"2023"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106587"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"119738","DOI":"10.1016\/j.eswa.2023.119738","article-title":"Condition Monitoring using Machine Learning: A Review of Theory, Applications, and Recent Advances","volume":"221","author":"Surucu","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1080\/15376494.2022.2102274"},{"issue":"22","key":"ref7","doi-asserted-by":"crossref","first-page":"10404","DOI":"10.3390\/app142210404","article-title":"Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization","volume":"14","author":"Umar","year":"2024","journal-title":"Applied Sciences"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1177\/16878132241298371"},{"issue":"1","key":"ref9","article-title":"Self-supervised Learning Approach for Anomaly Detection in Rotating Machinery","volume-title":"Annual Conference of the PHM Society","volume":"16","author":"De Fabritiis"},{"key":"ref10","first-page":"1","article-title":"Anomaly Detection in Rotary Equipment using hybrid model with HGSO Optimization","volume-title":"2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","author":"V"},{"key":"ref11","first-page":"1","article-title":"Realistic Condition-Based Anomaly Detection of Multi-Faults in Rotating Machines","volume-title":"2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET)","author":"Mishra"},{"key":"ref12","first-page":"1","article-title":"Novelty Detection of a Rolling Bearing using Long Short-Term Memory Autoencoder","volume-title":"2022 37th International Technical Conference on Circuits\/Systems, Computers and Communications (ITC-CSCC)","author":"Asavalertpalakorn"},{"key":"ref13","volume-title":"CWRU Bearing Data Center"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"108732","DOI":"10.1016\/j.ymssp.2021.108732","article-title":"Towards better benchmarking using the CWRU bearing fault dataset","volume":"169","author":"Hendriks","year":"2022","journal-title":"Mechanical Systems and Signal Processing"},{"key":"ref15","year":"2016","journal-title":"Mechanical vibration\u2014 Measurement and evaluation of machine vibration \u2014 Part 1: General guidelines"},{"key":"ref16","year":"2022","journal-title":"Mechanical vibration \u2014 Measurement and evaluation of machine vibration \u2014 Part 3: Industrial machinery with a power rating above 15 kW and operating speeds between 120 r\/min and 30 000 r\/min"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1177\/1748006x231218363"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/machines10010054"},{"key":"ref19","volume-title":"Dataset of Vibration, Temperature and Speed Measurements for Multiple Types of Localized Defects on Spherical Roller Bearings across Multiple Operating Conditions","author":"Di Maggio","year":"2024"},{"issue":"22","key":"ref20","doi-asserted-by":"crossref","first-page":"12458","DOI":"10.3390\/app132212458","article-title":"Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks","volume":"13","author":"Di Maggio","year":"2023","journal-title":"Applied Sciences"},{"issue":"1","key":"ref21","doi-asserted-by":"crossref","first-page":"211","DOI":"10.3390\/s23010211","article-title":"Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification","volume":"23","author":"Di Maggio","year":"2022","journal-title":"Sensors"},{"key":"ref22","first-page":"1","article-title":"Integrated Autoencoder-Level Set Method Outperforms Autoencoder for Novelty Detection","volume-title":"2022 International Joint Conference on Neural Networks (IJCNN)","author":"Shuo Liu"},{"issue":"3","key":"ref23","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/TLA.2024.10431423","article-title":"Novelty detection algorithms to help identify abnormal activities in the daily lives of elderly people","volume":"22","author":"Fernandes","year":"2024","journal-title":"IEEE Latin America Transactions"},{"key":"ref24","first-page":"413","article-title":"Isolation Forest","volume-title":"2008 Eighth IEEE International Conference on Data Mining","author":"Liu"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"ref27","first-page":"1131","article-title":"Elliptic Envelope Based Detection of Stealthy False Data Injection Attacks in Smart Grid Control Systems","volume-title":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","author":"Ashrafuzzaman"}],"event":{"name":"2025 International Conference on Control, Automation and Diagnosis (ICCAD)","location":"Barcelona, Spain","start":{"date-parts":[[2025,7,1]]},"end":{"date-parts":[[2025,7,3]]}},"container-title":["2025 International Conference on Control, Automation and Diagnosis (ICCAD)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11099041\/11099073\/11099203.pdf?arnumber=11099203","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T05:00:29Z","timestamp":1754542829000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11099203\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/iccad64771.2025.11099203","relation":{},"subject":[],"published":{"date-parts":[[2025,7,1]]}}}