Piezoelectric-array-based MISO diagnostic system for CNN-condition monitoring of bearing/gearbox instruments

Abstract

The article presents a novel MISO (multi-input-single-output) diagnostic system suitable for spatial condition monitoring of bearing/gearbox instruments with multi-location defects. The sensor array consists of three piezoelectric patches: one is attached to the surface of the bearing house and the other two connected in parallel are mounted on the wall of the planetary gear. These two sets of patches are electrically connected in series for sensing the fault signals whose sources of anomalies come from either the bearing or the gear. They offer an advantage of allowing a single voltage output from multiple inputs. In addition, two inductances are connected to the sensor array to form LC resonant circuits for filtering the irrelevant noise at high frequency. A convolutional neural network (CNN) classifier is trained by 12x150 FFT spectrums. The result from the testing data with 12x10 FFT spectrums shows that the average accuracy is achieved to be as high as 92:5%, confirming the soundness of the proposed model.