Improving predictive maintenance benefits from online monitoring of spindles: case study in woodworking machine tool

Abstract

Objective: This article aims to present a case study of the application of an online monitoring system for spindles in a furniture industry, analyzing the benefits to predictive maintenance and business. Methodology/approach: A literature review was carried out followed by a case study Originality/Relevance: Focusing the development of maintenance techniques, especially predictive maintenance and those supported by enabling technologies from Industry 4.0, such as Internet of Things (IoT), it may be possible to carry out online monitoring of spindles with a focus on reducing catastrophic or unplanned events. Main results: The main results are to know the normal behavior of the machine, the possibility of obtaining information in real time, managerial data for sight management, and the possibility of identifying spindle failure before it becomes a catastrophic failure, thus reducing the costs of maintaining the spindles. Theoretical contributions: As a contribution, we discuss the development of the system to digitize the data through the operation available in an outsourced cloud environment. This data can then be returned to the company in the form of dashboards for cash management, developing agility in decision making to facilitate the predictive maintenance in addition to validating the online monitoring system for spindle management in furniture industry processes.