Frequent Chronicle Mining: Application on Predictive Maintenance

Chronicles are a kind of sequential patterns that consider the time dimension to produce relevant knowledge for decision makers. Mined from pairs of event-time, chronicles are represented in graphs for which vertices are events and edges are labeled with intervals representing the time between the two linked events. Chronicle mining is interesting in several domains where predicting the time interval of an event is important, such as network failure analysis, pharmaco-epidemiology and human activities analysis. In this work, we are interested in predicting the failure time of monitored industrial machines. We introduce a new approach to mine the most relevant chronicles in an industrial data set. The extracted chronicles are then used to predict the failure time of a given machine. Our approach is validated through several experiments led on a benchmark data set.

[1]  Dmitriy Fradkin,et al.  Under Consideration for Publication in Knowledge and Information Systems Mining Sequential Patterns for Classification , 2022 .

[2]  Alain Mille,et al.  A complete chronicle discovery approach: application to activity analysis , 2012, Expert Syst. J. Knowl. Eng..

[3]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[4]  Yuhua Li,et al.  Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control , 2008, NIPS Causality: Objectives and Assessment.

[5]  Christian Borgelt,et al.  Frequent item set mining , 2012, WIREs Data Mining Knowl. Discov..

[6]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[7]  David Gross-Amblard,et al.  Discriminant chronicles mining: Application to care pathways analytics , 2017 .

[8]  Jasmina Novakovic,et al.  Using Information Gain Attribute Evaluation to Classify Sonar Targets , 2009 .

[9]  Marie-Odile Cordier,et al.  Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms , 2003, Artif. Intell. Medicine.

[10]  Xifeng Yan,et al.  CloSpan: Mining Closed Sequential Patterns in Large Datasets , 2003, SDM.

[11]  Huilong Duan,et al.  On mining clinical pathway patterns from medical behaviors , 2012, Artif. Intell. Medicine.

[12]  R. Keith Mobley,et al.  An introduction to predictive maintenance , 1989 .

[13]  Christophe Dousson,et al.  Discovering Chronicles with Numerical Time Constraints from Alarm Logs for Monitoring Dynamic Systems , 1999, IJCAI.