This paper investigates the possibilities of applying the random forests algorithm (RF) in machine fault diagnosis, and proposes a hybrid method combined with genetic algorithm to improve the classification accuracy. The proposed method is based on RF, a novel ensemble classifier which builds a number of decision trees to improve the single tree classifier. Although there are several existing techniques for faults diagnosis, the application research on RF is meaningful and necessary because of its fast execution speed, the characteristics of tree classifier, and high performance in machine faults diagnosis. The proposed method is demonstrated by a case study on induction motor fault diagnosis. Experimental results indicate the validity and reliability of RF-based diagnosis method.

Inductive sensor-based measurement techniques are useful for a wide range of biomedical applications. However, optimizing the noise performance of these sensors is challenging at broadband frequencies, owing to the frequency-dependent reactance of the sensor. In this work, we describe the fundamental limits of noise performance and bandwidth for these sensors in combination with a low-noise amplifier. We also present three equivalent methods of noise matching to inductive sensors using transformer-like network topologies. Finally, we apply these techniques to improve the noise performance in magnetic particle imaging, a new molecular imaging modality with excellent detection sensitivity. Using a custom noise-matched amplifier, we experimentally demonstrate an 11-fold improvement in noise performance in a small animal magnetic particle imaging scanner.

High-frame-rate ultrasound imaging uses unfocused transmissions to insonify an entire imaging view for each transmit event, thereby enabling frame rates over 1,000 fps. At these high frame rates, it is naturally challenging to realize real-time transfer of channel-domain raw data from the transducer to the system back-end. Our work seeks to halve the total data transfer rate by uniformly decimating the receive channel count by 50% and, in turn, doubling the array pitch. We show that, despite the reduced channel count and the inevitable use of a sparse array aperture, the resulting beamformed image quality can be maintained by designing a custom convolutional encoder-decoder neural network to infer the radiofrequency (RF) data of the nullified channels. This deep learning framework was trained with in vivo human carotid data (5 MHz plane wave imaging; 128 channels; 31 steering angles over a 30° span; 62,799 frames in total). After training, the network was tested on an in vitro point target scenario that was dissimilar to the training data, in addition to in vivo carotid validation datasets. In the point target phantom image beamformed from inferred channel data, spatial aliasing artifacts attributed to array pitch doubling were found to be reduced by up to 10 dB. For carotid imaging, our proposed approach yielded a lumen-to-tissue contrast that was on average within 3 dB compared to the full-aperture image, whereas without channel data inferencing, the carotid lumen was obscured. When implemented on an RTX-2080 GPU, the inference time to apply the trained network was 4 ms which favors real-time imaging. Overall, our technique shows that, with the help of deep learning, channel data transfer rates can be effectively halved with limited impact on the resulting image quality.1