Vibration Analysis in Bearings for Failure Prevention using CNN

Timely failure detection for bearings is of great importance to prevent economic loses in the industry. In this article we propose a method based on Convolutional Neural Networks (CNN) to estimate the level of wear in bearings. First of all, an automatic labeling of the raw vibration data is performed to obtain different levels of bearing wear, by means of the Root Mean Square features along with the Shannon's entropy to extract features from the raw data, which is then grouped in seven different classes using the K-means algorithm to obtain the labels. Then, the raw vibration data is converted into small square images, each sample of the data representing one pixel of the image. Following this, we propose a CNN model based on the AlexNet architecture to classify the wear level and diagnose the rotatory system. To train the network and validate our proposal, we use a dataset from the center of Intelligent Maintenance Systems (IMS), and extensively compare it with other methods reported in the literature. The effectiveness of the proposed strategy proved to be excellent, outperforming other approaches in the state-of-the-art.

On the safety of vulnerable road users by cyclist orientation detection using Deep Learning

In this work, orientation detection using Deep Learning is acknowledged for a particularly vulnerable class of road users,the cyclists. Knowing the cyclists' orientation is of great relevance since it provides a good notion about their future trajectory, which is crucial to avoid accidents in the context of intelligent transportation systems. Using Transfer Learning with pre-trained models and TensorFlow, we present a performance comparison between the main algorithms reported in the literature for object detection,such as SSD, Faster R-CNN and R-FCN along with MobilenetV2, InceptionV2, ResNet50, ResNet101 feature extractors. Moreover, we propose multi-class detection with eight different classes according to orientations. To do so, we introduce a new dataset called "Detect-Bike", containing 20,229 cyclist instances over 11,103 images, which has been labeled based on cyclist's orientation. Then, the same Deep Learning methods used for detection are trained to determine the target's heading. Our experimental results and vast evaluation showed satisfactory performance of all of the studied methods for the cyclists and their orientation detection, especially using Faster R-CNN with ResNet50 proved to be precise but significantly slower. Meanwhile, SSD using InceptionV2 provided good trade-off between precision and execution time, and is to be preferred for real-time embedded applications.