  Rolling bearings are one of the most widely used bearings in industrial
machines. Deterioration in the condition of rolling bearings can result in the
total failure of rotating machinery. AI-based methods are widely applied in the
diagnosis of rolling bearings. Hybrid NN-based methods have been shown to
achieve the best diagnosis results. Typically, raw data is generated from
accelerometers mounted on the machine housing. However, the diagnostic utility
of each signal is highly dependent on the location of the corresponding
accelerometer. This paper proposes a novel hybrid CNN-MLP model-based
diagnostic method which combines mixed input to perform rolling bearing
diagnostics. The method successfully detects and localizes bearing defects
using acceleration data from a shaft-mounted wireless acceleration sensor. The
experimental results show that the hybrid model is superior to the CNN and MLP
models operating separately, and can deliver a high detection accuracy of 99,6%
for the bearing faults compared to 98% for CNN and 81% for MLP models.
