Analysis and Optimization of a New Differentially Driven Cable Parallel Robot

In this paper, a new three degrees of freedom (DOF) differentially actuated cable parallel robot is proposed. This mechanism is driven by a prismatic actuator and three cable differentials. Through this design, the idea of using differentials in the structure of a spatial cable robot is investigated. Considering their particular properties, the kinematic analysis of the robot is presented. Then, two indices are defined to evaluate the workspaces of the robot. Using these indices, the robot is subsequently optimized. Finally, the performance of the optimized differentially driven robot is compared with fully actuated mechanisms. The results show that through a proper design methodology, the robot can have a larger workspace and better performance using differentials than the fully driven cable robots using the same number of actuators

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

International audienceHydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Discharge (PD) measurements, because the main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. A study of all stator failure mechanisms reveals that more than 85 % of them involve the presence of PD activity. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Québec has been collecting more than 33 000 unlabeled PD measurement files over the last decades. Up to now, this diagnostic technique has been quantified based on global PD amplitudes and integrated PD energy irrespective of the source of the PD signal. Several PD sources exist and they all have different relative risk, but in order to recognize the nature of the PD, or its source, the judgement of experts is required. In this paper, we propose a new method based on visual data analysis to build a PD source classifier with a minimum of labeled data. A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier

Automatic fixtureless inspection of non-rigid parts based on filtering registration points

Computer-aided inspection (CAI) of non-rigid parts significantly contributes to improving performance of products, reducing assembly time and decreasing production costs. CAI methods use scanners to measure point clouds on parts and compare them with the nominal computer-aided design (CAD) model. Due to the compliance of non-rigid parts and for inspection in supplier and client facilities, two sets of sophisticated and expensive dedicated fixtures are usually required to compensate for the deformation of these parts during inspection. CAI methods for fixtureless inspection of non-rigid parts aim at scanning these parts in a free-state for which one of the main challenges is to distinguish between possible geometric deviation (defects) and flexible deformation associated with free-state. In this work, the generalized inspection fixture ( GNIF) method is applied to generate a prior set of corresponding sample points between CAD and scanned models. These points are used to deform the CAD model to the scanned model via finite element non-rigid registration. Then, defects are identified by comparing the deformed CAD model with the scanned model. The fact that some sample points can be located close to defects results in an inaccurate estimation of these defects. In this paper, a method is introduced to automatically filter out sample points that are close to defects. This method is based on curvature and von Mises stress. Once filtered, the remaining sample points are used in a new registration, which allows identifying and quantifying defects more accurately. The proposed method is validated on aerospace parts

“What-if” scenarios towards virtual assembly-state mounting for non-rigid parts inspection using permissible loads

Recent developments in the fixtureless inspection of non-rigid parts based on computer-aided inspection (CAI) methods significantly contribute to diminishing the time and cost of geometrical dimensioning and inspection. Generally, CAI methods aim to compare scan meshes, which are acquired using scanners as point clouds from non-rigid manufactured parts in a free-state, with associated nominal computer-aided design (CAD) models. However, non-rigid parts are deformed in a free-state due to their compliance behavior. Industrial inspection approaches apply costly and complex physical inspection fixtures to retrieve the functional shape of non-rigid parts in assembly-state. Therefore, fixtureless inspection methods are developed to eliminate the need for these complex fixtures and to replace them with simple inspection supports. Fixtureless inspection methods intend to virtually (numerically) compensate for flexible deformation of non-rigid parts in a free-state. Inspired by industrial inspection techniques wherein weights (e.g., sandbags) are applied as restraining loads on non-rigid parts, we present a new fixtureless inspection method in this article. Our proposed virtual mounting assembly-state inspection (VMASI) method aims at predicting the functional shape (in assembly-state) of a deviated non-rigid part (including defects such as plastic deformation). This method is capable of virtually mounting the scan mesh of a deviated non-rigid part (acquired in a free-state) into the designed assembly-state. This is fulfilled by applying permissible restraining forces (loads) that are introduced as pressures on surfaces of a deviated part. The functional shape is then predicted via a linear FE-based transformation where the value and position of required restraining pressures are assessed by our developed restraining pressures optimization (RPO) approach. In fact, RPO minimizes the orientation difference and distance between assembly mounting holes on the predicted shape of a non-rigid part with respect to nominal ones on the CAD model. Eventually, the inspection is accomplished by examining the mounting holes offset on the predicted shape of the scan model concerning the nominal CAD model. This ensures that the mounting holes on the predicted shape of a scan model in assembly-state remain in the dedicated tolerance range. This method is evaluated on two non-rigid parts to predict the required restraining pressures limited to the permissible forces during the inspection process and to predict the eventual functional shape of the scan model. We applied numerical validations for each part, for which different types of synthetic (numerically simulated) defects are included into scan meshes, to determine whether the functional shape of a geometrically deviated part can be virtually retrieved under assembly constrains

A Step Toward Accurate Automated Dimensional inspection of Non Rigid Mechanical Parts

International audienc

A Step Toward Accurate Automated Dimensional inspection of Non Rigid Mechanical Parts

International audienc

Wind Turbine Blade Damage Evaluation under Multiple Operating Conditions and Based on 10-Min SCADA Data

The present paper aims to enable the assessment of the fatigue damage of wind turbine blades over a long duration (e.g., several months/years) in conjunction with different operating regimes and based on two information sources: the 10-min SCADA data and an interpolation using response surfaces identified using the FAST aeroelastic numerical tool. To assess blade damage, prior studies highlighted the need for a high-frequency (>1 Hz) sampling rate. Because of data availability and computation resource limitations, such methods limit the duration of the analysis period, making the direct use of such an approach based on a 1 Hz wind speed signal in current wind farms impractical. The present work investigates the possibility of overcoming these issues by estimating the equivalent damage using a 1 Hz wind speed for each 10-min sample stored in the SCADA data. In the literature, the influence of operating regimes is not considered in fatigue damage estimation, and for the first time, the present project takes a pioneering approach by considering these operating regimes

Imprecise Probabilities in Fatigue Reliability Assessment of Hydraulic Turbines

Risk analyses are often performed for economic reasons and safety purposes. In some cases, these studies are biased by epistemic uncertainties due to the lack of information and knowledge, which justifies the need for expert opinion. In such cases, experts can follow different approaches for the elicitation of epistemic data, using probabilistic or imprecise theories. But how do these theories affect the reliability calculation? What are the influences of using a mixture of theories in a multivariable system with a nonexplicit limit model? To answer these questions, we propose an approach for the comparison of these theories, which was performed based on a reliability model using the first-order reliability method (FORM) approach and having the Kitagawa-Takahashi diagram as limit state. We also propose an approach, appropriate to this model, to extend the reliability calculation to variables derived from imprecise probabilities. For the chosen reliability model, obtained results show that there is a certain homogeneity among the considered theories. The study also concludes that priority should be given to expert opinions formulated according to unbounded distributions, in order to achieve better reliability calculation accuracy