Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

NASA Space Engineering Research Center Symposium on VLSI Design

The NASA Space Engineering Research Center (SERC) is proud to offer, at its second symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories and the electronics industry. These featured speakers share insights into next generation advances that will serve as a basis for future VLSI design. Questions of reliability in the space environment along with new directions in CAD and design are addressed by the featured speakers

Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction

Proximal soil sensors and geostatistical tools in precision agriculture applications

Recognition of spatial variability is very important in precision agriculture applications. The use of proximal soil sensors and geostatistical techniques is highly recommended worldwide to detect spatial variation not only in fields but also within-field (micro-scale). This study involves, as a first step, the use of visible and near infrared (vis-NIR) spectroscopy to estimate soil key properties (6) and obtain high resolution maps that allow us to model the spatial variability in the soil. Different calibration models were developed using partial least square regression (PLSR) for different soil properties. These calibration models were evaluated by both cross-validation and independent validation. Results show good to excellent calibration models for most of soil properties under study in both cross-validation and independent validation. The on-line maps created using the collected on-line spectra and the calibration models previously estimated for each soil property were compared with three different maps (measured, predicted, error). The second step uses multivariate geostatistical analysis to develop three different geostatistical models (soil, spectral, fusion). The soil model includes 8 soil properties, spectral model includes 4 soil properties and the fusion model includes 12 soil properties. The three models were evaluated by cross-validation and the results show that the goodness of fitting can be considered as satisfactory for the soil model, whereas the performance of the spectral model was quite poor. Regarding the fusion model, it performed quite well, though the model generally underestimated the high values and overestimated the low values. An independent validation data set was used to evaluate the performance of the three models calculating three statistics: mean error (ME), as an indicator of bias; mean standardized squared error (MSSE), as an indicator of accuracy, and root mean squared error (RMSE), as an indicator of precision of estimation. Synthetically, the two, soil and fusion, models performed quite similarly, whereas the performance of the spectral model was much poorer. With regard to delineation of management zones (MZs), the factor cokriging analysis was applied using the three different models. The first factor (F1) for the soil and fusion models was related to soil properties that affect soil fertility, whereas for the spectral model was related to P (-0.88) and pH (-0.42). Based on the first factor of the soil and fusion models, three management zones were delineated and classified as low, medium and high fertility zones using isofrequency classes. Spatial similarity between the yield map and delineated MZs maps based on F1 for the soil and fusion models was calculated. The overall accordance between the two maps was 40.0 % for the soil model and 38.6 % for the fusion model. The two models performed quite similarly. These results can be interpreted as more than 50% of the yield variation was ascribable to more dynamic factors than soil parameters not included in this study, such as agro-meteorological conditions, plant diseases, nutrition stresses, etc. However, the results are quite promising for the application of the proposed approach in site-specific management.</br

Optimization of Safety Control System for Civil Infrastructure Construction Projects

Labor-intensive repetitive activities are common in civil construction projects. Construction workers are prone to developing musculoskeletal disorders-related injuries while performing such tasks. The government regulatory agency provides minimum safety requirement guidelines to the construction industry that might not be sufficient to prevent accidents and injuries in a construction site. Also, the regulations do not provide insight into what can be done beyond the mandatory requirements to maximize safety and underscore the level of safety that can be attained and sustained on a site. The research addresses the aforestated problem in three stages: (i) identification of theoretical maximum attainable level of safety, safety frontier, (ii) identification of underlying system inefficiencies and operational inefficiencies, and (iii) identification of achievable level of safety, sustainable safety. The research proposes a novel approach to identify the safety frontier by kinetic analysis of the human body while performing labor-intensive repetitive tasks. The task is a combination of different unique actions, which further involve several movements. For identifying a safe working procedure, each movement frame needs to be analyzed to compute the joint stress. Multiple instances of repetitive tasks can then be analyzed to identify unique actions exerting minimum stress on joints. The safety frontier is a combination of such unique actions. For this, the research proposes to track the skeletal positional data of workers performing different repetitive tasks. Unique actions involved in all tasks were identified for each movement frame. For this, several machine learning techniques were implemented. Moreover, the inverse dynamics principle was used to compute the stress induced by essential joints. In addition to the inverse dynamics principle, several machine learning algorithms were implemented to predict lower back moments. Then, the safety frontier was computed, combining the unique actions exerting minimum stress to the joints. Furthermore, the research conducted a questionnaire survey with construction experts to identify the factors affecting system inefficiencies that are not under the control of the project management team and operational inefficiencies that are under control. Then, the sustainable safety was computed by adding system inefficiencies to the safety frontier and removing operational inefficiencies from observed safety. The research validated the applicability of the proposed methodology in a real construction site. The application of random forest classifier, one-vs-rest classifier, and support vector machine approach were validated with high accuracy (\u3e95%). Similarly, random forest regressor, lasso regression, gradient boosting evaluation, stacking regression, and deep neural network were explored to predict the lower back moment. Random forest regressor and deep neural network predicted the lower back moment with an explained variance of 0.582 and 0.700, respectively. The computed safety frontier and sustainable safety can potentially facilitate the construction sector to improve safety strategies by providing a higher safety benchmark for monitoring, including the ability to monitor postural safety in real-time. Moreover, different industrial sectors such as manufacturing and agriculture can implement the similar approach to identify safe working postures for any labor-intensive repetitive task

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Twentieth Annual Conference on Manual Control, Volume 1

The 48 papers presented were devoted to humanopeator modeling, application of models to simulation and operational environments, aircraft handling qualities, teleopertors, fault diagnosis, and biodynamics