Model of vibrodiagnostic procedure for predictive maintenance of rotary machines

Mihajlov, Darko; Praščević, Momir; Ličanin, Marko

doi:10.5937/se2001039m

Cited by 1 publication

(1 citation statement)

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“…Based on the recognition of an anomaly in the operating behaviour of a machine tool, not only limited to vision-based approaches, the literature shows some works which try to forecast the detected signal to implement some sort of predictive maintenance. [23] present an approach based on a vibro-diagnostic model for predictive maintenance of rotary machines. [6] present a machine learning based approach to classify the vibration signal of a machine into normal or anomalous.…”

Section: Related Workmentioning

confidence: 99%

Intelligent vision based wear forecasting on surfaces of machine tool elements

Schlagenhauf

Burghardt

2021

SN Appl. Sci.

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To realize autonomous production machines it is necessary that machines are able to automatically and autonomously predict their condition. Although many classical as well as Deep Learning based approaches have shown the ability to classify faults, so far there are no approaches that go beyond the basic detection of faults. A complete, image based predictive maintenance approach for machine tool components has to the best of our knowledge not been investigated so far. In this paper it is shown how defects on a Ball Screw Drive (BSD) can be automatically detected by using a machine learning based detection module, quantified by using an intelligent defect quantification module and finally forecasted by a prognosis module in a combined approach. To optimize the presented method, it is shown how existing domain knowledge can be formalized in an expert system and combined with the predictions of the machine learning model to aid quality of the prediction system. This enables the practitioner to forecast the severity of failures on BSD and prevent machine breakdowns. The work is intended to set new accents for the development of practical predictive maintenance systems and bridging the fields of machine learning and production engineering. The code is available under: https://github.com/2Obe/Pitting_Pred_Maintenance.

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Section: Related Workmentioning

confidence: 99%