You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

Data Analytics using Statistical Methods and Machine Learning: A Case Study of Power Transfer Units


Publication Type:

Journal article


International Journal of Advanced Manufacturing Technology



Sensors can produce large amounts of data related to products, design and materials; however, it is important to use the right data for the right purposes. Therefore, detailed analysis of data accumulated from different sensors in production and assembly manufacturing lines is necessary to minimize faulty products and understand the production process. Additionally, when selecting analytical methods, manufacturing companies must select the most suitable techniques. This paper presents a data analytics approach to extract useful information, such as important measurements for the dimensions of a shim, a small part for aligning shafts, from the manufacturing data of a Power Transfer Unit (PTU). This paper also identifies the best techniques and analytical approaches within the following six individual areas: 1) identifying measurements associated with faults; 2) identifying measurements associated with shim dimensions; 3) identifying associations between station codes; 4) predicting shim dimensions; 5) identifying duplicate samples in faulty data; and 6) identifying error distributions associated with measurement. These areas are analysed in accordance with two analytical approaches: a) statistical analysis and b) machine learning (ML)-based analysis. The results show a) the relative importance of measurements with regard to the faulty unit and shim dimensions, b) the error distribution of measurements, and c) the reproduction rate of faulty units. Additionally, both statistical analysis and ML-based analysis have shown that the measurement ‘PTU housing measurement’ is the most important measurement among available shim dimensions. Additionally, certain faulty stations correlated with one another. ML is shown to be the most suitable technique in three areas (e.g., identifying measurements associated with faults), while statistical analysis is sufficient for the other three areas (e.g., identifying measurements associated with shim dimensions) because they do not require a complex analytical model. This study provides a clearer understanding of assembly line production and identifies highly correlated and significant measurements of a faulty unit.


author = {Sharmin Sultana Sheuly and Shaibal Barua and Shahina Begum and Mobyen Uddin Ahmed and Ekrem G{\"u}cl{\"u} and Michael Osbakk},
title = {Data Analytics using Statistical Methods and Machine Learning: A Case Study of Power Transfer Units},
volume = {69},
number = {9-12},
pages = {1--10},
month = {March},
year = {2021},
journal = {International Journal of Advanced Manufacturing Technology},
url = {}