]. T. Stauffer, N. Sands, and D. Dunn, Alarm management and isa-18 a journey, not a destination, Texas A & M Instrumentation Symposium, p.660, 2000.

D. Beebe, S. Ferrer, and D. Logerot, The Connection of peak alarm rates to plant incidents and what you can do to minimize, Process Safety Progress, vol.32, issue.1, pp.72-77, 2013.
DOI : 10.1002/prs.11539

M. Cordier and C. Dousson, Alarm Driven Monitoring Based on Chronicles, 4th Sumposium on Fault Detection Supervision and Safety for Technical Processes (SafeProcess), pp.286-291, 2000.
DOI : 10.1016/S1474-6670(17)37375-5

G. Carrault, M. Cordier, R. Quiniou, M. Garreau, J. Bellanger et al., A Model-Based Approach for Learning to Identify Cardiac Arrhythmias, Lecture Note on Artificial Intelligence, vol.1620, pp.165-174, 1999.
DOI : 10.1007/3-540-48720-4_18

B. Morin and H. Debar, Correlation on intrusion: an application of chronicles, 6th International Conference on recent Advances in Intrusion Detection RAID, 2003.

F. Fessant, F. Clérot, and C. Dousson, Mining of an Alarm Log to Improve the Discovery of Frequent Patterns, Industrial Conference on Data Mining, pp.144-152, 2004.
DOI : 10.1109/72.846731

A. Subias, L. Travé-massuyès, and E. Lecorronc, Learning chronicles signing multiple scenario instances, IFAC World Congress, pp.26-29
DOI : 10.3182/20140824-6-ZA-1003.02579
URL : https://hal.archives-ouvertes.fr/hal-01162866

M. Schleburg, L. Christiansen, N. F. Thornhill, A. Fay, S. R. Kondaveeti et al., A combined 680 analysis of plant connectivity and alarm logs to reduce the number of alerts in an automation system Graphical tools for routine assessment of industrial alarm systems, Journal of Process Control Computers and Chemical Engineering, vol.23, issue.46, pp.839-851, 2012.

F. Higuchi, I. Yamamoto, T. Takai, M. Noda, and H. Nishitani, Use of Event Correlation Analysis to Reduce Number of Alarms, 10th International Symposium on Process Systems Engineering: Part A, pp.1521-1526, 2009.
DOI : 10.1016/S1570-7946(09)70644-3

Z. Ge and Z. Song, Multimode process monitoring based on Bayesian method, Journal of Chemometrics, vol.17, p.700
DOI : 10.1007/978-1-4471-0347-9

X. Liu, M. Noda, and H. Nishitani, Evaluation of plant alarm systems by behavior simulation using a virtual subject, Computers & Chemical Engineering, vol.34, issue.3, 2010.
DOI : 10.1016/j.compchemeng.2009.11.017

F. Yang, S. Shah, D. Xiao, and T. Chen, Improved correlation analysis and visualization of industrial alarm data, {ISA} Transactions, vol.51, issue.4, p.710, 2012.

I. Izadi, S. L. Shah, D. S. Shook, S. R. Kondaveeti, and T. Chen, A Framework for Optimal Design of Alarm Systems, IFAC Proceedings Volumes, vol.42, issue.8, pp.651-656, 2003.
DOI : 10.3182/20090630-4-ES-2003.00108

A. Pariyani, W. D. Seider, U. G. Oktem, and M. Soroush, Dynamic risk analysis using alarm databases to improve process safety and product quality: Part II-Bayesian analysis, AIChE Journal, vol.107, issue.3, pp.826-841, 2012.
DOI : 10.1016/S0304-4076(01)00123-3

J. Liu and D. Chen, Nonstationary fault detection and diagnosis for mul- 725 timode processes, AIChE Journal, vol.56, issue.1, pp.207-219, 2010.

J. Zhu, Y. Shu, J. Zhao, and F. Yang, A dynamic alarm management strategy for chemical process transitions, Journal of 730 Loss Prevention in the Process Industries, pp.207-218, 2014.
DOI : 10.1016/j.jlp.2013.07.008

Z. Jing, L. Boang, and Y. Hao, Fault diagnosis strategy for startup process 735 based on standard operating procedures, 25th Chinese Control and Decision Conference (CCDC), 2013, pp.2013-4221

P. Duan, F. Yang, T. Chen, and S. L. Shah, Direct Causality Detection via the Transfer Entropy Approach, IEEE Transactions on Control Systems Tech- 740 nology, pp.2052-2066, 2013.
DOI : 10.1109/TCST.2012.2233476

F. Yang and D. Xiao, Progress in Root Cause and Fault Propagation Analysis of Large-Scale Industrial Processes, Journal of Control Science and Engineering, vol.31, issue.1, pp.20128-20136, 2012.
DOI : 10.1016/S0967-0661(02)00050-3

A. Bhagwat, R. Srinivasan, and P. Krishnaswamy, Fault detection during process transitions: a model-based approach, Chemical Engineering Science, vol.58, issue.2, pp.309-325, 2003.
DOI : 10.1016/S0009-2509(02)00520-1

]. R. Srinivasan, P. Viswanathan, H. Vedam, and A. Nochur, A framework for managing transitions in chemical plants, Computers & Chemical Engineering, vol.29, issue.2, pp.750-305, 2005.
DOI : 10.1016/j.compchemeng.2004.09.024

S. Xu, A. Adhitya, and R. Srinivasan, Hybrid Model-Based Framework for Alarm Anticipation, Industrial & Engineering Chemistry Research, vol.53, issue.13, pp.5182-5193, 2014.
DOI : 10.1021/ie4014953

C. Dousson, P. Gaborit, and M. Ghallab, Situation recognition: representation and algorithms, IJCAI: International Joint Conference on Artificial Intelligence, pp.166-172, 1993.

J. Vsquez, L. Trav-massuys, A. Subias, F. Jimenez, and C. Agudelo, Alarm management based on diagnosis, 4th {IFAC} Conference on Intelligent Control and Automation Sci- 770 encesICONS 2016Reims, France, pp.126-131, 2016.
DOI : 10.1016/j.ifacol.2016.07.101

R. Pons, A. Subias, and L. Travé-massuyès, Iterative hybrid causal model based diagnosis: Application to automotive embedded functions, Chronicles construction starting from the fault model of the system to diagnose International Workshop on Principles of Diagnosis (DX04), pp.51-56, 2004.
DOI : 10.1016/j.engappai.2014.09.016

E. Mayer, Inductive learning of chronicles, European Conference on Artificial Intelligence, pp.471-472, 1998.

M. W. Floyd and B. Esfandiari, A Case-Based Reasoning Framework for Developing Agents Using Learning by Observation, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, pp.531-538, 2011.
DOI : 10.1109/ICTAI.2011.86

M. Floyd, M. Bicakci, and B. Esfandiari, Case-based learning by observation in robotics using a dynamic case representation, FLAIRS Conference Quiniou, Mining temporal patterns with quantitative intervals ICDM Workshops, pp.785-218, 2008.

W. Wang, T. Guyet, R. Quiniou, M. Cordier, and F. Masseglia, Online and adaptive anomaly detection: detecting intrusions in unlabelled audit data streams, pp.457-458, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00460723

]. T. Guyet and R. Quiniou, Extracting temporal patterns from interval-based sequences, IJCAI, pp.790-1306, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00618444

T. Mitsa, Temporal data mining, 2010.
DOI : 10.1201/9781420089776

C. Dousson and T. V. Duong, Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems, IJCAI 99: 795 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp.620-626, 1999.

D. Cram, B. Mathern, and A. Mille, A complete chronicle discovery approach: application to activity analysis, Expert Systems, vol.42, issue.4, pp.321-346, 2012.
DOI : 10.1111/j.1468-0394.2011.00591.x
URL : https://hal.archives-ouvertes.fr/hal-01354577

H. Mannila, H. Toivonen, and A. I. Verkamo, Discovery of frequent episodes in 800 event sequences, Data Mining and Knowledge Discovery, vol.1, issue.3, pp.259-289, 1997.
DOI : 10.1023/A:1009748302351

F. Fessant and F. Clérot, An efficient som-based pre-processing to improve the discovery of frequent patterns in alarm logs, pp.276-282, 2006.