A Data Scientific Approach Towards Predictive Maintenance Application in Manufacturing Industry

Most industries have recently started to harness the power of data to assess their performance and improve their production systems for future competitiveness and sustainability. Therefore, utilization of data for obtaining insights through data-driven approaches is invading every domain of industrial applications. Predictive maintenance (PdM) is one of the highest impacted industrial use cases in data-driven applications due to its ability to predict machine failures by implementing machine learning algorithms. This study aims to propose a systematic data scientific approach to provide valuable insights by analysing industrial alarm and event log data, which might further be used for investigation in root cause understanding and planning of necessary maintenance activities. To do that, a Cross-Industry Standard Process for Data Mining (CRISP-DM) is followed as a reference model in this study. The results are presented by first understanding the relationship between alarms and product types being processed in the selected machines by using exploratory data analysis (EDA). Along with this, the behavior of problematic alarms is identified. Afterward, a predictive analysis formulated as a multi-class classification problem is performed using various Machine Learning (ML) models to predict the category of alarm and generate rules to be used for further investigation in maintenance planning. The performance of the developed models is evaluated based on the different metrics and the decision tree model is selected with the higher accuracy score among them. As a theoretical contribution, this study presents an implementation of predictive modeling in a structured way, which uses a systematic data scientific approach based on industrial alarm and event log data. On the other hand, as a practical contribution, this study provides a set of decision rules that can act as decision support for further exploration of possible in-depth root causes through the other contextual data, and hence it gives an initial foundation towards PdM application in the case company

Usability and Usefulness of Circularity Indicators for Manufacturing Performance Management

Advances in industrial digitalization present many opportunities for process and product data exploitation in manufacturing, unlocking new systemic measures of performance beyond a single machine, process, facility area and even beyond the factory gates. However, existing data models and manufacturing systems\u27 performance measures are still focused on productivity, quality and delivery time, which could potentially lead to an accelerated linear economy. To shift to more circular industrial systems, we need to identify and assess circularity opportunities in ways that align the goals of sustainable and industrial development. In this study, micro-level circular indicators were reviewed, selected, analysed and tested in a manufacturing company to evaluate their usability and usefulness to guide process improvements. The aim is to enable circular and eco-efficient solutions towards sustainable production systems. Usability and usefulness of the indicators are essential to their integration into established environmental and operations management systems. The main contribution of this study is in the identification of key features making circularity indicators usable and useful from a manufacturer\u27s perspective. The conclusion also suggests directions for further research on tools and methods to support circular manufacturing

Modified high cervical approach for C3-4 anterior pathology in difficult neck patients

Introduction: The anterior approach to cervical pathologies is a time-tested versatile approach. It is, however, associated with a number of pharyngo-tracheo-laryngeal complications (PTL complications) such as dysphonia, dysphagia, and aspiration, more commonly in high cervical C3-4 inclusive pathologies and even more so in patients with “difficult neck.” The modified high cervical approach was devised and employed to address these issues at our institution. Materials and Methods: Patients who underwent surgery for anterior cervical C3-4 inclusive pathologies between January 2015 and April 2018 were included in the study. Parameters for considering difficult neck were defined. Patient subgroup with difficult neck underwent surgery through a modified high cervical approach, whereas others underwent surgery through a standard approach. The incidence of pharyngo-tracheo-laryngeal complications in both subgroups of this patient set was compared among itself as well with a similar patient set with the same two subgroups, both of which underwent surgery through standard approach alone from May 2010 to December 2014 – before the introduction of modified high cervical approach. Results: A total of 280 patients underwent surgery for C3-4 level pathology between May 2010 and April 2018. There were 197 males and 93 females in this population. Mean age was 45.8 ± 6.3 years. Incidence of pharyngo-tracheo-laryngeal complications was 20.3% in patients who underwent surgery before the employment of modified high cervical approach – 32.4% of difficult neck and 16.6% of others developed features of pharyngo-tracheo-laryngeal complications. After employment of modified high cervical approach, 16.67% of difficult neck and 16.2% of other patients developed features of pharyngo-tracheo-laryngeal complications. Conclusion: The modified high cervical technique is a good surgical option to prevent pharyngo-tracheo-laryngeal complications in cases of anterior C3-4 pathology when operating of patients with difficult neck