In the majority of machines, bearings are among the most crucial components. Bearings are so important that they have been the subject of intensive research and ongoing development throughout the years. Often, bearing fails to reach its expected service life, resulting in failures that cause economic losses. Therefore, there has been a growing interest in research on bearing failure diagnosis systems due to the availability of condition monitoring techniques. Fault feature extraction techniques with the application of signal processing methods and machine learning techniques introduce an Intelligent Fault Diagnosis system that can identify and diagnose the bearing faults. Many researchers have been interested in such techniques in recent decades, which use artificial intelligence to diagnose machine health conditions. In this article, the authors have explored certain fault diagnosis methodologies based on signal processing and machine learning. From the discussed literature review, a research gap for future work has been defined.
Team barriers breakers Moto is to design and fabricate the sophisticated and simple kart design with factor of high fuel economy as well as with more suitable driver comfort without reconciliation the kart performance.This paper aims to increase the factor of safety of go kart chassis which is designed keeping in mind the rules imposed by INDKC 2019. This paper tends to design all the convenient features established in the go kart vehicle. There is involvement of many systems in manufacturing of go kart such as steering, braking, transmission, chassis etc. We have extensively designed and carried out the design analysis regarding separate all the systems involved in the kart’s specifications The design has been modeled in Catia V5 and Solidworks while the analysis was done in Ansys R1 and same rendering was done using Solidworks
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