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Traditionally, ball bearing condition monitoring is done by a human expert whose judgement is based on bearing vibration and temperature. In this paper, a method is described for classifying normal ball bearings and damaged ball bearings using scalar features, derived from their vibration signals, and a feedforward multi-layer neural network, trained using the back propagation algorithm. Two experimental test rigs, used for acquiring the vibration signals for the two types of ball bearings studied here, are described. Several scalar features, derived from the raw vibration signals, are discussed. Next, training of a feedforward multi-layer neural network with these scalar features, using back propagation algorithm, is presented. It is shown that with these scalar features, the neural network is successful in classifying normal and damaged ball bearings.

During the last two decades there has been much interest in the effects of infrasound on people. This is partly due to assessments made by some scientists that infrasound could cause side-effects such as intestinal irritations,throbbing in the head,nervous breakdowns,certain allergies and other unpleasant phenomena of modern life found in industrial cities. With the possible effects on road safety this paper describes measurements of infrasound intensity in European passenger cars. It was found that infrasonic sound pressure levels (ISPL) were proportional to vehicle speed, window opening and vehicle size. Also driving with a fitted roof rack tended to increase ISPL by as much as 20 dB. It was also found that under most adverse driving conditions ISPL never exceeded 110 dB which is below the threshold to degrade human performance. It seems therefore that the original significance placed on infrasound in cars was overestimated.