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In order to increase machinery resource, energy and time efficiency, Condition Monitoring (CM) offers a wide set of beneficial tools. Those tools can basically be segmented in maintenance improvements or the optimization of process parameters. CM requires data input from a component, which is then analyzed using data based or physical models, which...
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Digital transformation can provide a competitive edge for many manufacturers, however many smaller companies may not have the capabilities needed to embrace this opportunity and may be left behind. This paper reports on an approach which is attempting to alleviate this by creating a low-cost pathway to help manufacturing small and medium sized enterprises (SMEs) engage with digitalisation. This paper focuses on industrial monitoring and explores the potential for developing simple monitoring systems that solve real operation challenges in SMEs using low-cost, off-the-shelf technologies. A blueprint for developing such systems is presented and then exemplified through a case study system. The paper concludes that low-cost monitoring can be feasible given the right application and operating environment.
Digital manufacturing is focussed on leveraging the availability of digital information to improve the effectiveness of manufacturing activities. One of the digitalisation pathways for manufacturing is monitoring, which can be challenging due to the high costs of industrial monitoring solutions and the difficulty in justifying their return on investment. This study examines whether the introduction of low cost technologies can address the monitoring needs of digital manufacturing. In particular, we consider the role non-industrial, “off-the-shelf” technologies can play. The main aim of this paper is to present blueprints for low cost monitoring of industrial operations and identify candidate low cost technologies which can contribute effectively to the implementation of these systems. Related work on low cost monitoring and commercially available technologies are analysed and evaluated. Low-cost monitoring blueprints and candidate technologies are proposed based on the results of the analysis. An example implementation of a presented blueprint indicates the potential of integrating non-industrial, off-the-shelf technologies into low cost monitoring solutions.
It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.
