Paper Title:
Predicting Equipment Failure On SAP ERP Application Using Machine Learning Algorithms
Authors:
Manu Kohli
Summary:
A framework model to predict equipment failure has been keenly sought by asset intensive organisations. Timely prediction of equipment failure reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk.
In this paper, the author has proposed an equipment reliability model, for equipment type pumps, designed by applying data extraction algorithm on equipment maintenance records residing in SAP application. Author has initially applied unsupervised learning technique of clustering and performed classes to cluster evaluation to ensure generalisation of the model. Thereafter as part of supervised learning, data from the finalised data model was fed into various Machine Learning (ML) algorithms where the classifier was trained, with an objective to predict equipment breakdown.
The classifier was tested on test data sets where it was observed that support vector machine (SVM) and Decision Tree (DT) algorithms were able to classify and predict equipment breakdown with high accuracy and true positive rate (TPR) of more than 95 percent.
