Log-based predictive maintenance

Success of manufacturing companies largely depends on reliability of their products. Scheduled maintenance is widely used to ensure that equipment is operating correctly so as to avoid unexpected breakdowns. Such maintenance is often carried out separately for every component, based on its usage or simply on some fixed schedule. However, scheduled maintenance is labor-intensive and ineffective in identifying problems that develop between technician's visits. Unforeseen failures still frequently occur. In contrast, predictive maintenance techniques help determine the condition of in-service equipment in order to predict when and what repairs should be performed. The main goal of predictive maintenance is to enable pro-active scheduling of corrective work, and thus prevent unexpected equipment failures.

[1]  Marko Grobelnik,et al.  Training text classifiers with SVM on very few positive examples , 2003 .

[2]  James R. Foulds,et al.  A review of multi-instance learning assumptions , 2010, The Knowledge Engineering Review.

[3]  Boris Babenko.  Multiple Instance Learning: Algorithms and Applications , 2008 .

[4]  Michael I. Jordan,et al.  Detecting large-scale system problems by mining console logs , 2009, SOSP '09.

[5]  Jay Lee,et al.  PREVENTING FAILURES BY MINING MAINTENANCE LOGS WITH CASE-BASED REASONING , 2005 .

[6]  Michal Aharon,et al.  One Graph Is Worth a Thousand Logs: Uncovering Hidden Structures in Massive System Event Logs , 2009, ECML/PKDD.

[7]  Mohammad H. Poursaeidi,et al.  Robust support vector machines for multiple instance learning , 2014, Ann. Oper. Res..

[8]  Qiang Fu,et al.  Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[9]  Jun Zhou,et al.  MILIS: Multiple Instance Learning with Instance Selection , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Ling Huang,et al.  Online System Problem Detection by Mining Patterns of Console Logs , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[11]  James R. Foulds,et al.  Revisiting Multiple-Instance Learning Via Embedded Instance Selection , 2008, Australasian Conference on Artificial Intelligence.

[12]  Bibudh Lahiri,et al.  Finding Critical Thresholds for Defining Bursts , 2011, DaWaK.

[13]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[14]  Wei Peng,et al.  An integrated framework on mining logs files for computing system management , 2005, KDD '05.

[15]  Yixin Chen,et al.  MILES: Multiple-Instance Learning via Embedded Instance Selection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Hendrik Blockeel,et al.  Multi-Instance Learning , 2010, Encyclopedia of Machine Learning.

[17]  Evangelos E. Milios,et al.  Clustering event logs using iterative partitioning , 2009, KDD.

[18]  Zhi-Hua Zhou,et al.  Ensembles of Multi-instance Learners , 2003, ECML.

[19]  Francis R. Bach,et al.  Bolasso: model consistent Lasso estimation through the bootstrap , 2008, ICML '08.

[20]  Slobodan Vucetic,et al.  Mixture Model for Multiple Instance Regression and Applications in Remote Sensing , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Joseph F. Murray,et al.  Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application , 2005, J. Mach. Learn. Res..

[22]  Haimonti Dutta,et al.  A process for predicting manhole events in Manhattan , 2009, Machine Learning.

[23]  Risto Vaarandi,et al.  A data clustering algorithm for mining patterns from event logs , 2003, Proceedings of the 3rd IEEE Workshop on IP Operations & Management (IPOM 2003) (IEEE Cat. No.03EX764).

[24]  Zhi-Hua Zhou.  Multi-Instance Learning : A Survey , 2004 .

[25]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[26]  Mark Craven,et al.  Supervised versus multiple instance learning: an empirical comparison , 2005, ICML.

[27]  Ling Huang,et al.  Mining Console Logs for Large-Scale System Problem Detection , 2008, SysML.

[28]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[29]  Philip S. Yu,et al.  Online Failure Forecast for Fault-Tolerant Data Stream Processing , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[30]  Chokchai Leangsuksun,et al.  Baler: deterministic, lossless log message clustering tool , 2011, Computer Science - Research and Development.