{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:59:05Z","timestamp":1775915945342,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["FCT PD\/BDE\/135105\/2017"],"award-info":[{"award-number":["FCT PD\/BDE\/135105\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020)","award":["Project n 39479; Funding Reference: POCI-01-0247-FEDER-39479"],"award-info":[{"award-number":["Project n 39479; Funding Reference: POCI-01-0247-FEDER-39479"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a key industrial task. Since labeling is costly, requiring a manual effort, we focus on unsupervised detection approaches. In particular, we assume a computationally light low-dimensional problem formulation based on angle\u2013torque pairs. Our work is focused on two unsupervised machine learning (ML) algorithms: isolation forest (IForest) and a deep learning autoencoder (AE). Several computational experiments were held by assuming distinct datasets and a realistic rolling window evaluation procedure. First, we compared the two ML algorithms with two other methods, a local outlier factor method and a supervised Random Forest, on older data related with two production days collected in November 2020. Since competitive results were obtained, during a second stage, we further compared the AE and IForest methods by adopting a more recent and larger dataset (from February to March 2021, totaling 26.9 million observations and related to three distinct assembled products). Both anomaly detection methods obtained an excellent quality class discrimination (higher than 90%) under a realistic rolling window with several training and testing updates. Turning to the computational effort, the AE is much lighter than the IForest for training (around 2.7 times faster) and inference (requiring 3.0 times less computation). This AE property is valuable within this industrial domain since it tends to generate big data. Finally, using the anomaly detection estimates, we developed an interactive visualization tool that provides explainable artificial intelligence (XAI) knowledge for the human operators, helping them to better identify the angle\u2013torque regions associated with screw tightening failures.<\/jats:p>","DOI":"10.3390\/computers11040054","type":"journal-article","created":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T12:11:14Z","timestamp":1649419874000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6270-3962","authenticated-orcid":false,"given":"Diogo","family":"Ribeiro","sequence":"first","affiliation":[{"name":"ALGORITMI R&D Centre, Department of Information Systems, University of Minho, 4804-533 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5827-9129","authenticated-orcid":false,"given":"Lu\u00eds Miguel","family":"Matos","sequence":"additional","affiliation":[{"name":"ALGORITMI R&D Centre, Department of Information Systems, University of Minho, 4804-533 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6139-0071","authenticated-orcid":false,"given":"Guilherme","family":"Moreira","sequence":"additional","affiliation":[{"name":"Bosch Car Multimedia, 4705-820 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4380-3220","authenticated-orcid":false,"given":"Andr\u00e9","family":"Pilastri","sequence":"additional","affiliation":[{"name":"EPMQ-IT CCG ZGDV Institute, 4804-533 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7991-2090","authenticated-orcid":false,"given":"Paulo","family":"Cortez","sequence":"additional","affiliation":[{"name":"ALGORITMI R&D Centre, Department of Information Systems, University of Minho, 4804-533 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"ref_1","first-page":"485","article-title":"A Comparison of Anomaly Detection Methods for Industrial Screw Tightening","volume":"Volume 12950","author":"Gervasi","year":"2021","journal-title":"Proceedings of the Computational Science and Its Applications-ICCSA 2021-21st International Conference"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15:1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bickford, J. (1998). Handbook of Bolts and Bolted Joints, Taylor & Francis.","DOI":"10.1201\/9781482273786"},{"key":"ref_4","unstructured":"(2017). Rotary Tools for Threaded Fasteners\u2014Performance Test Method. Standard No. ISO 5393:2017."},{"key":"ref_5","unstructured":"Chen, W., Naughton, J.F., and Bernstein, P.A. (2000, January 16\u201318). LOF: Identifying Density-Based Local Outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alla, S., and Adari, S.K. (2019). Beginning Anomaly Detection Using Python-Based Deep Learning, Apress.","DOI":"10.1007\/978-1-4842-5177-5"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhou, C., and Paffenroth, R.C. (2017, January 13\u201317). Anomaly detection with robust deep autoencoders. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098052"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.cirp.2021.04.026","article-title":"Incremental discovery of new defects: Application to screwing process monitoring","volume":"70","author":"Ferhat","year":"2021","journal-title":"CIRP Ann."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez de Pis\u00f3n, F.J., Urraca, R., Quinti\u00e1n, H., and Corchado, E. (2017). Kernel Density-Based Pattern Classification in Blind Fasteners Installation. Hybrid Artificial Intelligent Systems, Springer International Publishing.","DOI":"10.1007\/978-3-319-59650-1"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Matsuno, T., Huang, J., and Fukuda, T. (2013, January 6\u201310). Fault detection algorithm for external thread fastening by robotic manipulator using linear support vector machine classifier. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631058"},{"key":"ref_11","first-page":"1","article-title":"Monitoring Screw Fastening Process: An Application of SVM Classification","volume":"11","author":"Ponpitakchai","year":"2016","journal-title":"Naresuan Univ. Eng. J. NUEJ"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cao, X., Liu, J., Meng, F., Yan, B., Zheng, H., and Su, H. (2019, January 11\u201313). Anomaly Detection for Screw Tightening Timing Data with LSTM Recurrent Neural Network. Proceedings of the 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), Shenzhen, China.","DOI":"10.1109\/MSN48538.2019.00072"},{"key":"ref_13","unstructured":"Shimbun, N.K. (1989). Poka-Yoke: Improving Product Quality by Preventing Defects, CRC Press."},{"key":"ref_14","unstructured":"Solace (2022, February 23). Advanced Event Broker. An Event Mesh for Connected Enterprises. Available online: https:\/\/solace.com\/."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"MacGregor, J.F., and Nomikos, P. (1996). Monitoring batch processes. Batch Processing Systems Engineering, Springer.","DOI":"10.1007\/978-3-642-60972-5_11"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"238","DOI":"10.2307\/1403797","article-title":"Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties","volume":"57","author":"Fix","year":"1989","journal-title":"INternational Stat. Rev. Rev. Int. Stat."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_19","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_20","unstructured":"Gulli, A., and Pal, S. (2017). Deep Learning with Keras, Packt Publishing Ltd."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., and Zhou, Z. (2008, January 15\u201319). Isolation Forest. Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy.","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"28161","DOI":"10.1007\/s11042-021-10924-x","article-title":"Point-Denoise: Unsupervised outlier detection for 3D point clouds enhancement","volume":"80","author":"Regaya","year":"2021","journal-title":"Multim. Tools Appl."},{"key":"ref_23","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), Lille, France."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Matos, L.M., Cortez, P., Mendes, R., and Moreau, A. (2019, January 14\u201319). Using Deep Learning for Mobile Marketing User Conversion Prediction. Proceedings of the International Joint Conference on Neural Networks, IJCNN 2019, Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851888"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/S0169-2070(00)00065-0","article-title":"Out-of-sample tests of forecasting accuracy: An analysis and review","volume":"16","author":"Tashman","year":"2000","journal-title":"Int. J. Forecast."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"114287","DOI":"10.1016\/j.eswa.2020.114287","article-title":"Multi-objective Grammatical Evolution of Decision Trees for Mobile Marketing user conversion prediction","volume":"168","author":"Pereira","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_27","first-page":"4:1","article-title":"A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates Be Reconciled?","volume":"Volume 192","author":"Ligett","year":"2021","journal-title":"Proceedings of the 2nd Symposium on Foundations of Responsible Computing, FORC 2021"},{"key":"ref_28","unstructured":"Hollander, M., Wolfe, D.A., and Chicken, E. (2013). Nonparametric Statistical Methods, John Wiley & Sons."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/4\/54\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:50:26Z","timestamp":1760136626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/4\/54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,8]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["computers11040054"],"URL":"https:\/\/doi.org\/10.3390\/computers11040054","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,8]]}}}