Maintenance of complex machinery is time and resource intensive. Therefore, decreasing maintenance cycles by employing Predictive Maintenance (PdM) is sought after by many manufacturers of machines and can be a valuable selling point. However, currently PdM is a hard to solve problem getting increasingly harder with the complexity of the maintained system. One challenge is to adequately prepare data for model training and analysis. In this paper, we propose the use of expert knowledge–based preprocessing techniques to extend the standard data science–workflow. We define complex multi-purpose machinery as an application domain and test our proposed techniques on real-world data generated by numerous machines deployed in the wild. We find that our techniques enable and enhance model training.
Modern embedded systems comprise more and more software. This yields novel challenges in development and quality assurance. Complex software interactions may lead to serious performance issues that can have a crucial economic impact if they are not resolved during development. Henceforth, we decided to develop and evaluate a machine learning-based approach to identify performance issues. Our experiments using real-world data show the applicability of our methodology and outline the value of an integration into modern software processes such as continuous integration.
The Machine Health (MH) sector—which includes, for example, Predictive Maintenance, Prognostics and Health Management, and Condition Monitoring—has the potential to improve efficiency and reduce costs for maintenance and machine operation. This is achieved by data-driven analytics applications, utilising the vast amount of data collected by sensors during machine runtime. While there are numerous possible fields of application, the overall complexity of machines and applications in scientific publications is still low, preventing MH technologies from being implemented in many real-world scenarios. This may be the result of a diffuse understanding of the term complexity in the publications of this field, which results in a lack of focus towards the core problems of real-world MH applications. This article introduces a new way of discerning complexity in data-driven MH applications, enabling an effective discussion and analysis of present and future MH applications. This is achieved by creating a new taxonomy based on observations from relevant literature and substantial domain knowledge. Using this newly introduced taxonomy, we categorise recent applications of MH to demonstrate the usefulness of our approach and illustrate a still-prevalent research gap based on our findings.
