Data analytics using statistical methods and machine learning: a case study of power transfer units

• Published in: International journal of advanced manufacturing technology Vol. 114; no. 5-6; pp. 1859 - 1870
• Main Authors: Sheuly, Sharmin Sultana, Barua, Shaibal, Begum, Shahina, Ahmed, Mobyen Uddin, Güclü, Ekrem, Osbakk, Michael
• Format: Journal Article
• Language: English
• Published: London Springer London 01.05.2021; Springer Nature B.V
• Subjects: Advanced Manufacturing; Application; Assembly lines; CAE) and Design; Computer-Aided Engineering (CAD; Data analysis; Data analytics; Engineering; Error analysis; Fault detection; Industrial and Production Engineering; Machine Learning; Manufacturing; Mathematical analysis; Mechanical Engineering; Media Management; Power transfer; Power Transfer Unit; Predictive maintenance; Sensors; Statistical analysis; Statistical methods; Power transfer unit; Statistical analysis; Fault detection; Data analytics; Machine Learning; Advanced manufacturing; Predictive maintenance
• ISSN: 0268-3768; 1433-3015; 1433-3015
• DOI: 10.1007/s00170-021-06979-7

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.