Abstract
In this study, we introduced an expert system (ESvbrPAL2v), responsible for monitoring assets based on vibration signature analysis through a set of algorithms based on the Paraconsistent Annotated Logic – PAL. Being a non-classical logic, the main feature of the PAL is to support contradictory inputs in its foundation. It is therefore suitable for building algorithmic models capable of performing out appropriate treatment for complex signals, such as those coming from vibration. The ESvbrPAL2v was built on an ATMega2560 microcontroller, where vibration signals were captured from the mechanical structures of the machines by sensors and, after receiving special treatment through the Discrete Fourier Transform (DFT), then properly modeled to paraconsistent logic signals and vibration patterns. Using the PAL fundamentals, vibration signature patterns were built for possible and known vibration issues stored in ESvbrPAL2v and continuously compared through configurations composed by a network of paraconsistent algorithms that detects anomalies and generate signals that will report on the current risk status of the machine in real time. The tests to confirm the efficiency of ESvbrPAL2v were performed in analyses initially carried out on small prototypes and, after the initial adjustments, tests were carried out on bearings of a group of medium-power motor generators built specifically for this study. The results are shown at the end of this study and have a high index of signature identification and risk of failure detection. These results justifies the method used and future applications considering that ESvbrPAL2v is still in its first version.
Highlights
In any industry, asset condition monitoring is vital and has been enhanced with new technologies and methodologies aiming failure predictions and optimization of time and costs related to corrective and preventive maintenance
The objective of this work is to show an algorithmic structure based on Paraconsistent Logic (PL) working as an expert system (ESvbrPAL2v) capable of continuously monitoring the vibration of bearings to warn about risks of breaking an industrial machine (Da Costa & Abe, 2000) (Côrtes, et al.,2022) (Da Silva Filho et al, 2021)
As expected ESvbrPAL2v was able to properly identify when the equipment was operating on normal conditions, reporting a higher coincidence index of 97.2%
Summary
Asset condition monitoring is vital and has been enhanced with new technologies and methodologies aiming failure predictions and optimization of time and costs related to corrective and preventive maintenance. Several factors can be taken into consideration when determining the condition of an asset, from electrical parameters such as power and current consumption to mechanical parameters such as vibration and thermals such as ambient and asset temperature. In modern high speed bearing failure diagnosis, methods based on vibration signals are widely used and continuous online monitoring of rotating machines is necessary to assess real-time health conditions reducing the possibility of downtime (Kwon et al, 2016) (Chen et al, 2016) (Ince et al, 2016) (Lei & Wu, 2020) (Janssens et al, 2016)
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