The most crucial transmission components utilized in rotating machinery are gears and bearings. In a gearbox, the bearings support the force acting on the gears. Compound Faults in both the gears and bearings may cause heavy vibration and lead to early failure of components. Despite their importance, these compound faults are rarely studied since the vibration signals of the compound fault system are strongly dominated by noise. This work proposes an intelligent approach to fault identification of a compound gear-bearing system using a novel Bessel kernel-based Time-Frequency Distribution (TFD) called the Bessel transform. The Time-frequency images extracted using the Bessel transform are used as an input to the Convolutional Neural Network (CNN), which classifies the faults. The effectiveness of the proposed approach is validated with a case study, and a testing efficiency of 94% is achieved. Further, the proposed method is compared with the other TFDs and found to be effective.

In these remarks I do undertake one more time the attempt to answer the following question: what do agnostics really want? This issue is so complicated that even the agnostics themselves had great trouble in delivering the answer. This is also related to these agnostics, such as the recalled here Charles Darwin, Albert Einstein, and Stephen W. Hawking, who belong to the greatest format of scholars. The agnostics are being distanced from, both the atheists and theists. However they do judge differently their views it is important that as well the first as the latter ones may appreciate what stands behind agnosticism and this might be very variable.