AI Detect: A Machine Learning Based Approach for Fault Identification in Gear Bearing System using Low-Frequency Data

Acoustic Resonance Testing of Small Data on Sintered Cogwheels

“…The field involved with cogwheels is vast, and yet most work in the literature has been performed in the context of gearboxes or bearing systems [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], i.e., many gears are inside in a system and attached to each other. For such systems, the main focus of fault detection lies on a system failure dealing with conditions monitoring in lifespan analysis, which occurs mainly due to malfunctioning components suffering from wear, abrasion and pollution, such as sand or lubrication.…”

“…Although gearboxes or bearing systems related work have made progress [ 5 , 6 , 7 ], usually by means of modern deep learning (DL) [ 9 ], the employed methods are not always applicable to small data problems as discussed in [ 10 , 11 ], e.g., 22 layers of GoogLeNet [ 12 ] used in [ 6 ].…”

“…Moreover, not all studies reflect real-world scenarios, e.g., the tooth of a gear is intentionally cut off [ 5 ]. In addition, many studies do not mention the sample size of a dataset nor countermeasures against overfitting whether the proposed work is suitable for small data problems, e.g., [ 7 ].…”