Constructing a reliable health indicator for bearings using convolutional autoencoder and continuous wavelet transform

Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator ( ) to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data‐driven methods have been proposed to construct the , most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data‐driven method based on the convolutional autoencoder (CAE) is presented to construct the . For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two‐dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (MD) between the healthy and failure stages was measured as the . The proposed method was tested on a benchmark bearing dataset and compared with several other traditional construction models. Experimental results indicate that the constructed exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults

Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments

Loop closure is an inseparable part of any accurate and reliable visual simultaneous localization and mapping (SLAM) algorithm for autonomous vehicles and mobile robots. Loop closure potentially decreases the impact of the cumulative drift while generating the map of the traversed environment. In this paper, a heuristic similarity and location-based approach for loop closure in unexplored environments is introduced. The current SLAM implementation on average requires 0.295 seconds per frame from which only 0.0270 seconds are the runtime latencies of the similarity and location-based real-time loop closure (SNAPS), which includes trajectory correction. The proposed approach results in a 65% decrease in the mean deviation from the ground truth. In the conducted study, neither conventional bag-of-words models, nor computationally expensive deep neural networks have been used to detect and perform loop closure, which makes the proposed approach both interpretable and efficient. In fact, we propose a method which tries to find loop closure candidates based on the location and also an interpretable similarity score attained from the generated thumbnails of the read frames instead of the local descriptors. Additionally, the employed discount factor applied on the pose trajectory update rule guarantees a consistent and accurate map. Lastly, the KITTI dataset is used to demonstrate the efficiency and accuracy of SNAPS for SLAM

Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 DoF delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data are available. Due to the sequential nature of the data, nonlinearity of the system, and correlations between parameter time-series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method can calculate RUL using Gaussian process (GP), as a degradation model, given HI values as its input

RASAECO : Requirements Analysis of Software for the AECO industry

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Digitalization is forging its path in the architecture, construction, engineering, operation (AECO) industry. This trend demands not only solutions for data governance but also sophisticated cyber-physical systems with a high variety of stakeholder background and very complex requirements. Existing approaches to general requirements engineering ignore the context of the AECO industry. This makes it harder for the software engineers usually lacking the knowledge of the industry context to elicit, analyze and structure the requirements and to effectively communicate with AECO professionals. To live up to that task, we present an approach and a tool for collecting AECOspecific software requirements with the aim to foster reuse and leverage domain knowledge. We introduce a common scenario space, propose a novel choice of an ubiquitous language wellsuited for this particular industry and develop a systematic way to refine the scenario ontologies based on the exploration of the scenario space. The viability of our approach is demonstrated on an ontology of 20 practical scenarios from a large project aiming to develop a digital twin of a construction site

A mixed-perception approach for safe human–robot collaboration in industrial automation

Digital-enabled manufacturing systems require a high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; however, this presence of workers in the shared workspace with robots decreases the productivity, as the robot is not aware about the human position and intention, which leads to concerns about human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (cobots). The main idea here is to significantly enhance safety using a combination of recognition of human actions using visual perception and at the same time interpreting physical human–robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection, which in combination, are expected to lead to the enhancement of human safety and an increase in the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation

Wearable robotic protection : symbiosis between man and machine

Reducing workloads with technological advancements is now integral to many areas of industrial production. The article describes the development of an exoskeleton suit to reduce workload and enhance worker safety