Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

  • Authors

    • Manu Kohli
    2018-05-16
    https://doi.org/10.14419/ijet.v7i2.28.12952
  • CBM, Clustering, Condition based monitoring, Corrective Maintenance, Enterprise Resource Planning, Equipment failure, ERP, HANA, Machine Learning, Plant Maintenance, Predictive Maintenance, Reliability Maintenance, SAP

  • Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations.

     

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    Kohli, M. (2018). Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures. International Journal of Engineering & Technology, 7(2.28), 312-319. https://doi.org/10.14419/ijet.v7i2.28.12952