Digitalisation online condition monitoring and AI analysis in a vacuum pump
Abstract: Billerudkorsnäs is a good example of an industry that associated with a 24/7 production and where faults lead to downtime in the production process which in its turn generates substantial losses. Sustainability is essential for a prosperous future for Billerudkorsnäs, and with the growth of technologies and digitalization with big data that are in line with the coming Industry 4.0 standard, the company will have the possibility to ensure a sustainable production system. However, to be able to achieve this, it is necessary to implement an intelligent maintenance system into the traditional manufacturing process. This study will, therefore focus on how online monitoring can be applied to estimate error prediction on Nash vacuum pumps by using Bluetooth low energy sensors. At Billerudkorsnäs, 6 Nash vacuum pumps have from time-to-time had a problem of bearing faults resulting in unreliable production. Even though time-based maintenance has been implemented, it has proven to be an insufficient method to uphold an efficient production and to in-time detect faults in the production process. As a response to this, first, the study has deployed a digitalized online monitoring application for fault prediction. By utilizes of Bluetooth low energy (BLE) Beacon, communication gateway (BLuFi) and a web-based platform Bluzone with cloud server services that work parallel with machine learning technology. The faults discovered by implemented application are observed via Bluzone. In addition, automatic generated e-mail sent to a vacuum pump inspector when such faults have occurred. Second, as an effect of this implementation, a classical theoretical framework based on an AutoRegressive (AR) and AR with exogenous input (ARX) for prediction modelling has been studied. The method applies historical data from the vacuum pump, and the problem with input and output data from two different applications is discussed. The results of the study gave at hand- in comparison with the technology used to-day – that the implemented new system has a more efficient in providing reliable information as to prevent unnecessary downtime in the Nash vacuum pumps. This implies a lower production cost. Although interesting results in practice it is difficult to use theoretically. In correspondence, AR prediction model results verify the model fit compared to the measured response. Also, the ARX model was tested.
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