A conceptual framework for machine learning algorithm selection for predictive maintenance

The Industry 4.0 paradigm enables advanced data-driven decision-making processes leading many manufacturers to a digital transformation. Within this context, Predictive Maintenance (PdM) - i.e. a maintenance strategy that predicts failures in advance - based on Machine Learning (ML) - i.e. a set of algorithms to analyze data for pattern recognition - emerged as one of the most prominent data-driven analytical approaches for maximizing availability and efficiency of industrial systems. Indeed, there exists a considerable body of literature dealing with ML-based PdM where a wide set of ML algorithms has been applied to a broad range of industrial settings. Whilst this results in extensive knowledge on the topic, the need to choose the right algorithm for a specific task arises as a challenging issue since it is considered an essential stage in the development and implementation of an ML-oriented approach. To respond to such a necessity, this work proposes a conceptual framework to guide practitioners as well as non-expert users in ML algorithm selection for PdM issues. The aim is to provide a set of guidelines and recommendations for the identification of which ML techniques are likely to achieve valuable performance for specific tasks or datasets. First, the most commonly applied ML algorithms in PdM are analyzed together with their core characteristics, advantages, and disadvantages. Then, several decision variables depending on dataset and ML characteristics, learning objectives, accuracy and interpretability are considered. Finally, illustrative case studies are presented to demonstrate how the proposed framework can be adopted in real industrial applications

Identifying Customer Returns in a Printed Circuit Board Production Line Using the Mahalanobis Distance

This paper discusses as its primary research question the viability of using the Mahalanobis Distance as a multivariate method for detecting outliers in an industrial setting. An algorithm is used to detect future customer returns in a printed circuit board production line situated in Sibiu, Romania. From the literature, there is a lack of methods, tools and guidelines concerning the paradigm of Zero-Defect Manufacturing. The novelty of the method presented includes separation of highly specialized, future outliers from other outliers, and further automation using Python, a Docker container, a graphical user interface, a search-engine and a reporting tool. This allows the method to be used without external assistance. The data used is extracted industrial datasets from Continentals datalake. The algorithm detects 20% of future outliers and has been implemented by Continental. This can possibly be improved by increasing domain knowledge. The generality of the algorithm in principle allows for use at any of Continental’s production lines. There are strong assumptions regarding the requirements for the method, including benefits of employing domain knowledge critical variable identification and detection rate improvements. Further improvements of detection rate are also discussed. The paper concludes that the algorithm can detect a percentage of highly specialized outliers with simple automation in Python, but also acknowledges limitations in terms of increased demands from data quality and domain knowledge