{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:01:31Z","timestamp":1783508491537,"version":"3.55.0"},"reference-count":59,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lilly Endowment","award":["Wabash Heartland Innovation Network (WHIN)"],"award-info":[{"award-number":["Wabash Heartland Innovation Network (WHIN)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.<\/jats:p>","DOI":"10.3390\/s23010486","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T04:17:38Z","timestamp":1672633058000},"page":"486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9554-9260","authenticated-orcid":false,"given":"Mustafa","family":"Abdallah","sequence":"first","affiliation":[{"name":"Computer and Information Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byung-Gun","family":"Joung","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2465-6296","authenticated-orcid":false,"given":"Wo Jae","family":"Lee","sequence":"additional","affiliation":[{"name":"Environmental and Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charilaos","family":"Mousoulis","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nithin","family":"Raghunathan","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Shakouri","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2118-0907","authenticated-orcid":false,"given":"John W.","family":"Sutherland","sequence":"additional","affiliation":[{"name":"Environmental and Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saurabh","family":"Bagchi","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Thomas, T.E., Koo, J., Chaterji, S., and Bagchi, S. (2018, January 3\u20137). Minerva: A reinforcement learning-based technique for optimal scheduling and bottleneck detection in distributed factory operations. Proceedings of the 2018 10th International Conference on Communication Systems & Networks (COMSNETS), Bengaluru, India.","DOI":"10.1109\/COMSNETS.2018.8328189"},{"key":"ref_2","first-page":"114","article-title":"Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm","volume":"19","author":"Scime","year":"2018","journal-title":"Addit. Manuf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jmsy.2018.01.003","article-title":"Deep learning for smart manufacturing: Methods and applications","volume":"48","author":"Wang","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ukil, A., Bandyoapdhyay, S., Puri, C., and Pal, A. (2016, January 23\u201325). IoT Healthcare Analytics: The Importance of Anomaly Detection. Proceedings of the 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Crans-Montana, Switzerland.","DOI":"10.1109\/AINA.2016.158"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5397","DOI":"10.1109\/JSEN.2016.2557345","article-title":"Energy-efficient intelligent street lighting system using traffic-adaptive control","volume":"16","author":"Shahzad","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1145\/2542049","article-title":"A survey of intrusion detection techniques for cyber-physical systems","volume":"46","author":"Mitchell","year":"2014","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chatterjee, B., Seo, D.H., Chakraborty, S., Avlani, S., Jiang, X., Zhang, H., Abdallah, M., Raghunathan, N., Mousoulis, C., and Shakouri, A. (2020). Context-Aware Collaborative Intelligence with Spatio-Temporal In-Sensor-Analytics for Efficient Communication in a Large-Area IoT Testbed. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2020.3036087"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sabahi, F., and Movaghar, A. (2008, January 26\u201331). Intrusion detection: A survey. Proceedings of the 2008 Third International Conference on Systems and Networks Communications, Sliema, Malta.","DOI":"10.1109\/ICSNC.2008.44"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bowler, A.L., Bakalis, S., and Watson, N.J. (2020). Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning. Sensors, 20.","DOI":"10.3390\/s20071813"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1109\/LRA.2017.2714135","article-title":"Categorization of anomalies in smart manufacturing systems to support the selection of detection mechanisms","volume":"2","author":"Lopez","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2018","DOI":"10.1016\/j.promfg.2017.07.353","article-title":"Anomaly detection approaches for semiconductor manufacturing","volume":"11","author":"Susto","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Leahy, K., Hu, R.L., Konstantakopoulos, I.C., Spanos, C.J., and Agogino, A.M. (2016, January 20\u201322). Diagnosing wind turbine faults using machine learning techniques applied to operational data. Proceedings of the 2016 Ieee International Conference On Prognostics Furthermore, Health Management (ICPHM), Ottawa, ON, Canada.","DOI":"10.1109\/ICPHM.2016.7542860"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.mfglet.2019.02.001","article-title":"Deep Learning for Distortion Prediction in Laser-Based Additive Manufacturing using Big Data","volume":"20","author":"Francis","year":"2019","journal-title":"Manuf. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lee, W.J., Mendis, G.P., and Sutherland, J.W. (2019, January 4\u20136). Development of an Intelligent Tool Condition Monitoring System to Identify Manufacturing Tradeoffs and Optimal Machining Conditions. Proceedings of the 16th Global Conference on Sustainable Manufacturing. Procedia Manufacturing, Buenos Aires, Argentina.","DOI":"10.1016\/j.promfg.2019.04.031"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.compind.2006.02.011","article-title":"SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox","volume":"57","author":"Garcia","year":"2006","journal-title":"Comput. Ind."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kroll, B., Schaffranek, D., Schriegel, S., and Niggemann, O. (2014, January 16\u201319). System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants. Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), Barcelona, Spain.","DOI":"10.1109\/ETFA.2014.7005202"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neucom.2018.05.017","article-title":"Anomaly detection and predictive maintenance for photovoltaic systems","volume":"310","author":"Leonardi","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1080\/00207543.2017.1351644","article-title":"Smart manufacturing","volume":"56","author":"Kusiak","year":"2018","journal-title":"Int. J. Prod. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lee, W.J., Mendis, G.P., Triebe, M.J., and Sutherland, J.W. (2019). Monitoring of a machining process using kernel principal component analysis and kernel density estimation. J. Intell. Manuf.","DOI":"10.1007\/s10845-019-01504-w"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.patrec.2020.06.008","article-title":"Using an autoencoder in the design of an anomaly detector for smart manufacturing","volume":"136","author":"Alfeo","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5247","DOI":"10.1109\/ACCESS.2017.2689040","article-title":"Big IoT data analytics: Architecture, opportunities, and open research challenges","volume":"5","author":"Marjani","year":"2017","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/JIOT.2017.2724845","article-title":"Multitier fog computing with large-scale iot data analytics for smart cities","volume":"5","author":"He","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1109\/TPWRS.2002.804943","article-title":"ARIMA models to predict next-day electricity prices","volume":"18","author":"Contreras","year":"2003","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.ijforecast.2019.02.011","article-title":"FFORMA: Feature-based forecast model averaging","volume":"36","author":"Athanasopoulos","year":"2020","journal-title":"Int. J. Forecast."},{"key":"ref_26","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_28","first-page":"279","article-title":"Modular learning in neural networks","volume":"647","author":"Ballard","year":"1987","journal-title":"Aaai"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","article-title":"DeepAR: Probabilistic forecasting with autoregressive recurrent networks","volume":"36","author":"Salinas","year":"2020","journal-title":"Int. J. Forecast."},{"key":"ref_30","unstructured":"Jeff, R. (2019, September 30). Considerations For Accelerometer Selection When Monitoring Complex Machinery Vibration. Available online: http:\/\/www.vibration.org\/Presentation\/IMI%20Sensors%20Accel%20Presentation%200116.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"784","DOI":"10.3390\/s8020784","article-title":"Suitability of MEMS Accelerometers for Condition Monitoring: An experimental study","volume":"8","author":"Albarbar","year":"2008","journal-title":"Sensors"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1108\/02656719610118151","article-title":"Failure mode and effects analysis","volume":"13","author":"Teng","year":"1996","journal-title":"Int. J. Qual. Reliab. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lee, W.J., Wu, H., Huang, A., and Sutherland, J.W. (2019). Learning via acceleration spectrograms of a DC motor system with application to condition monitoring. Int. J. Adv. Manuf. Technol.","DOI":"10.1007\/s00170-019-04563-8"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/TKDE.2010.31","article-title":"Bridging domains using world wide knowledge for transfer learning","volume":"22","author":"Xiang","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s13634-016-0355-x","article-title":"A survey of machine learning for big data processing","volume":"2016","author":"Qiu","year":"2016","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Torrey, L., and Shavlik, J. (2010). Transfer learning. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global.","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Abdallah, M., Rossi, R., Mahadik, K., Kim, S., Zhao, H., and Bagchi, S. (2022, January 17\u201321). AutoForecast: Automatic Time-Series Forecasting Model Selection. Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM \u201922), Atlanta, GA, USA.","DOI":"10.1145\/3511808.3557241"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ling, X., Dai, W., Xue, G.R., Yang, Q., and Yu, Y. (2008, January 24\u201327). Spectral domain-transfer learning. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA.","DOI":"10.1145\/1401890.1401951"},{"key":"ref_39","unstructured":"Chen, T., Goodfellow, I., and Shlens, J. (2015). Net2net: Accelerating learning via knowledge transfer. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/JSEN.2020.3020573","article-title":"Calibration of Haptic Sensors Using Transfer Learning","volume":"21","author":"Wang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1109\/JSEN.2019.2947026","article-title":"Multi-Fault Bearing Classification Using Sensors and ConvNet-Based Transfer Learning Approach","volume":"20","author":"Udmale","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_42","unstructured":"Koizumi, Y., Kawaguchi, Y., Imoto, K., Nakamura, T., Nikaido, Y., Tanabe, R., Purohit, H., Suefusa, K., Endo, T., and Yasuda, M. (2020). Description and discussion on DCASE2020 challenge task2: Unsupervised anomalous sound detection for machine condition monitoring. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hsieh, R.J., Chou, J., and Ho, C.H. (2019, January 18\u201321). Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing. Proceedings of the 2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA), Kaohsiung, Taiwan.","DOI":"10.1109\/SOCA.2019.00021"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2734","DOI":"10.1109\/ACCESS.2020.3047838","article-title":"Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis","volume":"9","author":"Fathy","year":"2021","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/0047-259X(85)90027-2","article-title":"Prediction of multivariate time series by autoregressive model fitting","volume":"16","author":"Lewis","year":"1985","journal-title":"J. Multivar. Anal."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tokg\u00f6z, A., and \u00dcnal, G. (2018, January 2\u20135). A RNN based time series approach for forecasting turkish electricity load. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404313"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gers, F., Schmidhuber, J., and Cummins, F. (1999, January 7\u201310). Learning to forget: Continual prediction with LSTM. Proceedings of the 1999 Ninth International Conference on Artificial Neural Networks ICANN 99, (Conf. Publ. No. 470), Edinburgh, UK.","DOI":"10.1049\/cp:19991218"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_49","first-page":"1","article-title":"Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting","volume":"32","author":"Sen","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_50","unstructured":"Chollet, F. (2016). Building autoencoders in keras. Keras Blog, 14, Available online: https:\/\/blog.keras.io\/building-autoencoders-in-keras.html."},{"key":"ref_51","unstructured":"Wang, Y., Smola, A., Maddix, D., Gasthaus, J., Foster, D., and Januschowski, T. (2019, January 10\u201315). Deep factors for forecasting. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Shi, Y., Li, C., Kim, J., Cai, W., Han, Z., and Feng, D.D. (2016). DeepGene: An advanced cancer type classifier based on deep learning and somatic point mutations. BMC Bioinform., 17.","DOI":"10.1186\/s12859-016-1334-9"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3257","DOI":"10.3233\/JIFS-169508","article-title":"Computer Aided Qur\u2019an Pronunciation using DNN","volume":"34","author":"Raafat","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Elaraby, M.S., Abdallah, M., Abdou, S., and Rashwan, M. (2016, January 23\u201327). A Deep Neural Networks (DNN) Based Models for a Computer Aided Pronunciation Learning System. Proceedings of the International Conference on Speech and Computer, Budapest, Hungary.","DOI":"10.1007\/978-3-319-43958-7_5"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1007\/s10618-015-0444-8","article-title":"On the evaluation of unsupervised outlier detection: Measures, datasets, and an empirical study","volume":"30","author":"Campos","year":"2016","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_57","unstructured":"Gulli, A., and Pal, S. (2017). Deep Learning with Keras, Packt Publishing."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"4088","DOI":"10.1109\/TII.2021.3088057","article-title":"Federated Transfer Learning Based Cross-Domain Prediction for Smart Manufacturing","volume":"18","author":"Wang","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_59","unstructured":"Abdallah, M., Lee, W.J., Raghunathan, N., Mousoulis, C., Sutherland, J.W., and Bagchi, S. (2021). Anomaly detection through transfer learning in agriculture and manufacturing IoT systems. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/486\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:56:13Z","timestamp":1760118973000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/486"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,2]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010486"],"URL":"https:\/\/doi.org\/10.3390\/s23010486","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,2]]}}}