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OPTIMAL LINEAR CONTROL APPLIED TO A NON-IDEAL CAPSULE SYSTEM WITH UNCERTAIN PARAMETERS
ROEFERO, LUIZ GUSTAVO PEREIRA,CHAVARETTE, FABIO ROBERTO,OUTA, ROBERTO,MERIZIO, IGOR FELICIANI,MORO, THIAGO CARRETA,MISHRA, VISHNU NARAYAN The Korean Society for Computational and Applied M 2022 Journal of applied mathematics & informatics Vol.40 No.1-2
The design of mechanical structures aims to meet criteria, together with the safety of operators and lives in the vicinity of the equipment. Thus, there are several cases that meeting the desired specification causes the mechanical device to perform unstable and, sometimes, chaotic behavior. In these cases, control methods are applied in order to stabilize the device when in operation, aiming at the physical integrity of the component and the device operators. In this work, we will develop a study about the influence of a controller applied in a non-ideal capsule system operating with uncertain parameters, being non-existent in the literature. For this, two initial conditions were used: one that the capsule starts from rest and another that it is already in motion. Thus, the effectiveness of the controller can be assessed in both initial conditions, restricting the movement of the internal vibration-impact system to the capsule.
Lucas Veronez Goulart Ferreira,Laxmi Rathour,Devika Dabke,Fabio Roberto Chavarette,Vishnu Narayan Mishra 한국전산응용수학회 2023 Journal of applied mathematics & informatics Vol.41 No.6
Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40$\%$ to 90$\%$) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83$\%$) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.