Skip to main content

Addressing concept drift to improve system availability by updating one-class data-driven models


Data-driven models have been used to detect system faults, thereby increasing industrial system availability. The ability to search data streams while dealing with concept drift are challenges for data-driven models. The objective of this work is to demonstrate a general method to manage concept drift when using one-class data-driven models. The method has been used to develop an automatically retrained and updated polygon-based model. In this paper, the available industrial data allowed for use of one-class data-driven models, and the polygon-based model was selected because it has previously been successful. Possible scenarios that allow one-class data-driven models to be retrained or updated were identified. Based on the identified scenarios, a method to automatically update a polygon-based model online is proposed. The method has been tested and verified using data collected from a Bosch Rexroth Mellansel AB hydraulic drive system. Data representing relevant faults was inserted into the data set in close collaboration with engineers from the company. The results show that the developed polygon-based model method was able to address the concept drift issue and was able to significantly improve the classification accuracy compared to the static polygon-based model. Thereby, the model could significantly improve industrial system availability when applied in the relevant production process. This paper shows that the developed polygon-based model requires small memory space while its updating procedure is simple and fast. Finally, the identified scenarios may be helpful as input for supporting other one-class data-driven models to cope with concept drift, thus increasing the generalizability of the results.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13


  • Alsyouf I, Alzghoul A (2009) Soft computing applications in wind power systems: a review and analysis. Paper presented at the European offshore wind conference and exhibition, Stockholm

  • Alzghoul A, Löfstrand M (2011) Increasing availability of industrial systems through data stream mining. Comput Ind Eng 60(2):195–205. doi:10.1016/j.cie.2010.10.008

    Article  Google Scholar 

  • Alzghoul A, Löfstrand M, Backe B (2012) Data stream forecasting for system fault prediction. Comput Ind Eng 62(4):972–978. doi:10.1016/j.cie.2011.12.023

    Article  Google Scholar 

  • Andrew AM (1979) Another efficient algorithm for convex hulls in two dimensions. Inf Process Lett 9(5):216–219

    Article  MATH  Google Scholar 

  • Andrews JD, Moss TR (2002) Reliability and risk assessment. Professional Engineering Publishing Limited, Bury St Edmunds

    Google Scholar 

  • Bifet A, Gama J, Pechenizkiy M, Zliobaite I (2011) Handling concept drift: importance, challenges and solutions. PAKDD-2011 Tutorial, Shenzhen

  • Daszykowski M, Walczak B, Massart DL (2002) Looking for natural patterns in analytical data. 2. Tracing local density with OPTICS. J Chem Inf Comput Sci 42(3):500–507

    Article  Google Scholar 

  • Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd international conference on KDD, pp 226–231

  • Hu J, Si J, Olson BP, He J (2005) Feature detection in motor cortical spikes by principal component analysis. IEEE Trans Neural Syst Rehabil Eng 13(3):256–262

    Article  Google Scholar 

  • Isermann R (2011) Fault-diagnosis applications model-based condition monitoring: actuators, drives, machinery, plants, sensors, and fault-tolerant systems. Springer, Berlin, Heidelberg

    Book  Google Scholar 

  • Karacal SC (2007) Mining machine data streams using statistical process monitoring techniques. Paper presented at the IIE annual conference and expo

  • Kargupta H (2004) VEDAS: a mobile and distributed data stream mining system for real-time vehicle monitoring. In: SIAM proceedings series, pp 300–311

  • Khan L, Han J, Thuraisingham B (2009) Integrating novel class detection with classification for concept-drifting data streams. In: Lecture notes in computer science, vol 5782, LNAI (Part 2)

  • Lee J, Magoulès F (2012) Detection of concept drift for learning from stream data. In: Proceeding of the 14th IEEE international conference on high performance computing and communications, pp 241–245

  • MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967, California, p 14

  • Markou M, Singh S (2003a) Novelty detection: a review––Part 1: statistical approaches. Sig Process 83(12):2481–2497

    Article  MATH  Google Scholar 

  • Markou M, Singh S (2003b) Novelty detection: a review––Part 2: neural network based approaches. Sig Process 83(12):2499–2521

    Article  MATH  Google Scholar 

  • Matthews B, Srivastava AN (2008) Comparative analysis of data-driven anomaly detection methods. In: Paper presented at the JANNAF conference on propulsion systems, Proceedings of the joint army navy NASA air force conference on propulsion, Orlando

  • Spinosa EJ, De Carvalho APDLF, Gama J (2007) OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams. In: Proceedings of the ACM symposium on applied computing, pp 448–452

  • Xu C, Wedlund D, Helgoson M, Risch T (2013) Model-based validation of streaming data:(industry article). In: Proceedings of the 7th ACM international conference on distributed event-based systems. ACM, pp 107–114

  • Yang ZM, Djurdjanovic D, Ni J (2008) Maintenance scheduling in manufacturing systems based on predicted machine degradation. J Intell Manuf 19(1):87–98

    Article  Google Scholar 

Download references


This work was funded by The EU FP7 Project SmartVortex and the SSPI project (Scalable search of product lifecycle information) funded by the Swedish Foundation for Strategic Research (SSF). The authors wish to acknowledge the collaborating industrial partners at Bosch Rexroth Mellansel AB for their help and support, especially Mr. Arne Byström and Dr. Hc. Bengt Liljedahl.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ahmad Alzghoul.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alzghoul, A., Löfstrand, M. Addressing concept drift to improve system availability by updating one-class data-driven models. Evolving Systems 6, 187–198 (2015).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Concept drift
  • Data stream mining
  • Availability
  • Industry
  • Polygon-based method