This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space.

This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system.

When a robot is teleoperated, its operator control is based on transmitted images. Network limitations and/or a remote distance usually cause delays or interruptions of the image transmission, which is one of the reasons for the instability of teleoperation systems. In this work, we propose a high-update-rate image generation method using past low update image and current grip position and electrical motor current of gripper received by sensors during teleoperation via a conditional generative adversarial network. The main challenge is that such a network can generate current high-update-rate images from past low-update-rate one, the current high-update-rate grip force, and the grip angle. We equipped a robot gripper with a camera and a grip force sensor and collected a large dataset of robot vision, grip force, and grip angle sequences; objects with deformation, including irregular deformation, and rigid objects were tested in the experiment to verify the possibility of high-update-rate image generation under various grip conditions. We found that the proposed network allows the generation of current images with high update rate. Here, we present the results of real-time image generation from delayed images and sensor data along with the root-mean-square value, the peak signal-to-noise ratio, and the structural similarity index measure and compare the images generated by the proposed network with those from other networks.