{"type":"ScholarlyArticle","title":"Monitoring of a Dynamic System Based on Autoencoders","authors":[{"name":"Aomar Osmani","slug":"aomar-osmani","given":"Aomar","family":"Osmani","affiliation":"Laboratoire LIPN-UMR CNRS 7030 , PRES Sorbone Paris Cité , France"},{"name":"Massinissa Hamidi","slug":"massinissa-hamidi","given":"Massinissa","family":"Hamidi","affiliation":"Laboratoire LIPN-UMR CNRS 7030 , PRES Sorbone Paris Cité , France"},{"name":"Salah Bouhouche","slug":"salah-bouhouche","given":"Salah","family":"Bouhouche","affiliation":"Industrial Technologies Research Center , CRTI-DTSI , Algiers , Algeria"}],"abstract":"Monitoring industrial infrastructures are undergoing a critical transformation with industry 4.0.  Monitoring solutions must follow the system behavior in real time and must adapt to its continuous change. We propose in this paper an autoencoder model-based approach for tracking abnormalities in industrial application. A set of sensors collects data from turbo-compressors and an original two-level machine learning LSTM autoencoder architecture defines a continuous nominal vibration model. Normalized thresholds (ISO 20816) between the model and the system generates a possible abnormal situation to diagnose. Experimental results, including hyper-parameter optimization on large real data and domain expert analysis, show that our proposed solution gives promising results.","doi":"10.24963/ijcai.2019/254","url":"https://ijcai.org/proceedings/2019/254","pdf":"https://ijcai.org/proceedings/2019/0254.pdf","markdown":"https://ijcai.org/proceedings/2019/254.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/254","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":254,"pages":{"first":1836,"last":1843},"keywords":["Multidisciplinary Topics and Applications: Real-Time Systems","Machine Learning: Time-series;Data Streams","Machine Learning Applications: Other Applications","Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning","Machine Learning Applications: Applications of Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Alain and Bengio, 2014] Guillaume Alain and Yoshua Ben- gio. What regularized auto-encoders learn from the data- generating distribution. JMLR, 15(1):3563-3593, 2014.","title":"What regularized auto-encoders learn from the datagenerating distribution","authors":"Alain, Bengio, Guillaume Alain, Yoshua Bengio","year":"2014","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ali et al., 2015] Jaouher Ben Ali, Nader Fnaiech, Lotfi Saidi, Brigitte Chebel-Morello, and Farhat Fnaiech. Ap- plication of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89:16-27, 2015.","title":"Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals","authors":"Ali","year":"2015","venue":"Applied Acoustics","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bach and Maloof, 2008] Stephen H Bach and Marcus A Maloof. Paired learners for concept drift. In Eighth IEEE International Conference on Data Mining, 2008., pages 23-32. IEEE, 2008.","title":"Paired learners for concept drift","authors":"H Stephen, Marcus A Bach, Maloof","year":"2008","venue":"Eighth IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bengio et al., 2015] Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. Scheduled sampling for se- quence prediction with recurrent neural networks. In NIPS, pages 1171-1179, 2015.","title":"Scheduled sampling for sequence prediction with recurrent neural networks","authors":"Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Borghesi et al., 2018] Andrea Borghesi, Andrea Bar- tolini, Michele Lombardi, Michela Milano, and Luca Benini. Anomaly detection using autoencoders in high performance computing systems. arXiv preprint arXiv:1811.05269, 2018.","title":"Anomaly detection using autoencoders in high performance computing systems","authors":"Andrea Borghesi, Andrea Bartolini, Michele Lombardi, Michela Milano, Luca Benini","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cooijmans et al., 2016] Tim Cooijmans, Nicolas Bal- las, César Laurent, C ¸aglar Gülc ¸ehre, and Aaron Courville. Recurrent batch normalization. arXiv preprint arXiv:1603.09025, 2016.","title":"Recurrent batch normalization","authors":"Tim Cooijmans, Nicolas Ballas, César Laurent, C ¸aglar Gülc ¸ehre, Aaron Courville","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Djaidir et al., 2017] Benrabeh Djaidir, Ahmed Hafaifa, and Abdallah Kouzou. Faults detection in gas turbine rotor us- ing vibration analysis under varying conditions. Journal of Theoretical and Applied Mechanics, 55(2):393-406, 2017.","title":"Benrabeh Djaidir, Ahmed Hafaifa, and Abdallah Kouzou. Faults detection in gas turbine rotor us-ing vibration analysis under varying conditions","authors":"Djaidir","year":"2017","venue":"Journal of Theoretical and Applied Mechanics","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Elsken et al., 2018] Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. Neural architecture search: A survey. arXiv preprint arXiv:1808.05377, 2018.","title":"Neural architecture search: A survey","authors":"Thomas Elsken, Jan Hendrik Metzen, Frank Hutter","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gal and Ghahramani, 2016] Yarin Gal and Zoubin Ghahra- mani. A theoretically grounded application of dropout in recurrent neural networks. In NIPS, pages 1019-1027, 2016.","title":"Yarin Gal and Zoubin Ghahramani. A theoretically grounded application of dropout in recurrent neural networks","authors":"Ghahramani Gal","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Greff et al., 2017] Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10):2222-2232, 2017.","title":"Lstm: A search space odyssey","authors":"Klaus Greff, K Rupesh, Jan Srivastava, Koutník, Jürgen Bas R Steunebrink, Schmidhuber","year":"2017","venue":"IEEE transactions on neural networks and learning systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Head et al., 2018] Tim Head, MechCoder, Gilles Louppe, Iaroslav Shcherbatyi, et al. scikit-optimize/scikit- optimize: v0.5.2, March 2018.","title":"scikit-optimize/scikitoptimize","authors":"Tim Head, Gilles Mechcoder, Iaroslav Louppe, Shcherbatyi","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hinton and Salakhutdinov, 2006] Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks. science, 313(5786):504-507, 2006.","title":"Reducing the dimensionality of data with neural networks","authors":"Geoffrey E Hinton, Ruslan R Salakhutdinov","year":"2006","venue":"science","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.","title":"Long short-term memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Hoos and Leyton-Brown, 2014] Holger Hoos and Kevin Leyton-Brown. An efficient approach for assessing hy- perparameter importance. In International Conference on Machine Learning, pages 754-762, 2014.","title":"An efficient approach for assessing hyperparameter importance","authors":"Holger Hoos, Kevin Leyton-Brown","year":"2014","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ioffe and Szegedy, 2015] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, volume 37, pages 448-456.","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","authors":"Szegedy Ioffe, Sergey Ioffe, Christian Szegedy","year":"2015","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"PMLR, 2015.","title":null,"authors":"Pmlr","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Isermann, 2005] Rolf Isermann. Model-based fault- detection and diagnosis-status and applications. Annual Reviews in control, 29(1):71-85, 2005.","title":"Model-based faultdetection and diagnosis-status and applications","authors":"Rolf Isermann","year":"2005","venue":"Annual Reviews in control","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lee et al., 2015] Jay Lee, Behrad Bagheri, and Hung-An Kao. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3:18-23, 2015.","title":"A cyber-physical systems architecture for industry 4.0-based manufacturing systems","authors":"Jay Lee, Behrad Bagheri, Hung-An Kao","year":"2015","venue":"Manufacturing Letters","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lees et al., 2009] AW Lees, JK Sinha, and MI Friswell. Model-based identification of rotating machines. Mechan- ical Systems and Signal Processing, 23(6):1884-1893, 2009.","title":"Model-based identification of rotating machines","authors":"Lees","year":"2009","venue":"Mechanical Systems and Signal Processing","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2000] Bo Li, M-Y Chow, Yodyium Tipsuwan, and James C Hung. Neural-network-based motor rolling bear- ing fault diagnosis. IEEE transactions on industrial elec- tronics, 47(5):1060-1069, 2000.","title":"Neural-network-based motor rolling bearing fault diagnosis","authors":"Bo Li, M-Y Chow, Yodyium Tipsuwan, James C Hung","year":"2000","venue":"IEEE transactions on industrial electronics","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Lin et al., 2017] Tao Lin, Tian Guo, and Karl Aberer. Hy- brid neural networks for learning the trend in time series. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pages 2273-2279, 2017.","title":"Hybrid neural networks for learning the trend in time series","authors":"Tao Lin, Tian Guo, Karl Aberer","year":"2017","venue":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17","doi":null,"url":null,"internal_url":"/proceedings/2017/316"},{"position":23,"raw":"[Maaten and Hinton, 2008] Laurens van der Maaten and Ge- offrey Hinton. Visualizing data using t-sne. JMLR, 9(Nov):2579-2605, 2008.","title":"Laurens van der Maaten and Geoffrey Hinton","authors":"Hinton Maaten","year":"2008","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Ng, 2011] Andrew Ng. Sparse autoencoder. CS294A Lec- ture notes, 72(2011):1-19, 2011.","title":"Sparse autoencoder","authors":"Andrew Ng","year":"2011","venue":"CS294A Lecture notes","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Osmani and Hamidi, 2018] Aomar Osmani and Massinissa Hamidi. Hybrid and convolutional neural networks for lo- comotion recognition. In Proceedings of the 2018 ACM UbiComp/ISWC 2018 Adjunct, pages 1531-1540. ACM, 2018.","title":"Aomar Osmani and Massinissa Hamidi. Hybrid and convolutional neural networks for locomotion recognition","authors":"Hamidi Osmani","year":"2018","venue":"Proceedings of the 2018 ACM UbiComp/ISWC 2018 Adjunct","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Osmani et al., 2017] Aomar Osmani, Massinissa Hamidi, and Abdelghani Chibani. Machine learning approach for infant cry interpretation. In (ICTAI), 2017 IEEE 29th In- ternational Conference on, pages 182-186. IEEE, 2017.","title":"Machine learning approach for infant cry interpretation","authors":"Osmani","year":"2017","venue":"IEEE 29th International Conference on","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Paszke et al., 2017] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary De- Vito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017.","title":"Automatic differentiation in pytorch","authors":"Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary De-Vito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Qi et al., 2017] Yumei Qi, Changqing Shen, Dong Wang, Juanjuan Shi, Xingxing Jiang, and Zhongkui Zhu. Stacked sparse autoencoder-based deep network for fault diagno- sis of rotating machinery. IEEE Access, 5:15066-15079, 2017.","title":"Stacked sparse autoencoder-based deep network for fault diagnosis of rotating machinery","authors":"Yumei Qi, Changqing Shen, Dong Wang, Juanjuan Shi, Xingxing Jiang, Zhongkui Zhu","year":"2017","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Rahmoune et al., 2017] Mohamed Ben Rahmoune, Ahmed Hafaifa, and Mouloud Guemana. Fault diagnosis in gas turbine based on neural networks: Vibrations speed ap- plication. In Advances in Acoustics and Vibration, pages 1-11. Springer, 2017.","title":"Fault diagnosis in gas turbine based on neural networks: Vibrations speed application","authors":"Mohamed Ben Rahmoune, Ahmed Hafaifa, Mouloud Guemana","year":"2017","venue":"Advances in Acoustics and Vibration","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Rifai et al., 2011] Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio. Contractive auto-encoders: Explicit invariance during feature extrac- tion. In Proceedings of the 28th International Conference on Machine Learning, pages 833-840. Omnipress, 2011.","title":"Contractive auto-encoders: Explicit invariance during feature extraction","authors":"Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, Yoshua Bengio","year":"2011","venue":"Proceedings of the 28th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Santurkar et al., 2018] Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry. How does batch normalization help optimization?(no, it is not about in- ternal covariate shift). arXiv preprint arXiv:1805.11604, 2018.","title":"How does batch normalization help optimization?(no, it is not about internal covariate shift)","authors":"Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Snoek et al., 2012] Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of ma- chine learning algorithms. In NIPS, pages 2951-2959, 2012.","title":"Practical bayesian optimization of machine learning algorithms","authors":"Snoek","year":"2012","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Tidriri et al., 2016] Khaoula Tidriri, Nizar Chatti, Sylvain Verron, and Téodor Tiplica. Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future chal- lenges. Annual Reviews in Control, 42:63-81, 2016.","title":"Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges","authors":"Tidriri","year":"2016","venue":"Annual Reviews in Control","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Tobon-Mejia et al., 2012] Diego Alejandro Tobon-Mejia, Kamal Medjaher, Noureddine Zerhouni, and Gerard Tripot. A data-driven failure prognostics method based on mixture of gaussians hidden markov models. IEEE Trans- actions on reliability, 61(2):491-503, 2012.","title":"A data-driven failure prognostics method based on mixture of gaussians hidden markov models","authors":"Tobon-Mejia","year":"2012","venue":"IEEE Transactions on reliability","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}