Search results for "rain"
showing 10 items of 10658 documents
Analysis of hybrid vehicle transmissions with any number of modes and planetary gearing: kinematics, power flows, mechanical power losses
2021
Abstract This paper is focused on upgrading a unified parametric model, available in the literature, that can perform both the analysis and the design of Power-Split Continuously Variable Transmissions (PS-CVTs), which are particularly promising to deploy in the hybrid electric powertrain. In particular, this work is focused on the analysis of PS-CVTs and proposes a new matrix approach for identifying the basic functional parameters underlying the model from the constructive layout of the transmission. This new method does not rely on a case-specific formulation, thus befits any power-split transmission, regardless of the number of planetary and ordinary gear sets and their constructive arr…
Re-forming end-of-life components through single point incremental forming
2020
Abstract Applying Circular Economy strategies is mandatory to face material demand while minimizing the environmental impact. Manufacturing processes are to be thought as means to enable material/component reuse strategies. This paper presents the suitability of Single Point Incremental Forming (SPIF) to re-form End-of-life sheet metal components. Deep drawing followed by SPIF process on aluminium alloys were carried out to simulate reforming processes chain. The resulting thinning and strain distributions were experimentally analysed for different configurations. The research proves that the local action and enhanced formability nature of SPIF allow non-homogeneously thinned and reduced fo…
Extreme minimal learning machine: Ridge regression with distance-based basis
2019
The extreme learning machine (ELM) and the minimal learning machine (MLM) are nonlinear and scalable machine learning techniques with a randomly generated basis. Both techniques start with a step in which a matrix of weights for the linear combination of the basis is recovered. In the MLM, the feature mapping in this step corresponds to distance calculations between the training data and a set of reference points, whereas in the ELM, a transformation using a radial or sigmoidal activation function is commonly used. Computation of the model output, for prediction or classification purposes, is straightforward with the ELM after the first step. In the original MLM, one needs to solve an addit…
Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks
2018
Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is propos…
Dynamic Modeling, Energy Analysis, and Path Planning of Spherical Robots on Uneven Terrains
2020
Spherical robots are generally comprised of a spherical shell and an internal actuation unit. These robots have a variety of applications ranging from search and rescue to agriculture. Although one of the main advantages of spherical robots is their capability to operate on uneven surfaces, energy analysis and path planning of such systems have been studied only for flat terrains. This work introduces a novel approach to evaluate the dynamic equations, energy consumption, and separation analysis of these robots rolling on uneven terrains. The presented dynamics modeling, separation analysis, and energy analysis allow us to implement path planning algorithms to find an optimal path. One of t…
Assembly Assistance System with Decision Trees and Ensemble Learning
2021
This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared …
Enhancing Disaster Response for Hazardous Materials Using Emerging Technologies: The Role of AI and a Research Agenda
2019
Despite all efforts like the introduction of new training methods and personal protective equipment, the need to reduce the number of First Responders (FRs) fatalities and injuries remains. Reports show that advances in technology have not yet resulted in protecting FRs from injuries, health impacts, and odorless toxic gases effectively. Currently, there are emerging technologies that can be exploited and applied in emergency management settings to improve FRs protection. The aim of this paper is threefold: First, to conduct scenario analysis and situations that currently threat the first responders. Second, to conduct gap analysis concerning the new technology needs in relations to the pro…
Vibration reduction on city buses: Determination of optimal position of engine mounts
2010
International audience; This study is composed of three essential parts. The first part describes an indirect semi-experimental method which is used to reconstruct the excitation force of an operating diesel engine from the acceleration data measured at the mounting points. These internal forces can not be directly measured with force sensors; they have to be derived from the dynamic deformation of the engine support, so a theoretical analysis is carried out to derive the equations for the force re-construction.The second part deals with prevention of low frequency vibration of the powertrain from spreading to the rest of the vehicle. Three uncoupling techniques are used to minimize these v…
Mitigation of Fatigue Damage and Vibration Severity of Electric Drivetrains by Systematic Selection of Motion Profiles
2016
The offshore drilling industry is among the most demanding markets for electrical equipment. Heave motion, irregular cyclic loads, harsh weather conditions, and vibrations are causing accelerated deterioration of drilling equipment. One of the most common solutions to these problems is to design actuation systems of such machinery overly conservative to gain additional safety, which results in too high initial investment and maintenance costs. To mitigate the fatigue damage and vibration severity of rotating elements of electric drivetrains operating offshore, this paper presents a comparative analysis of four popular input functions used in motion control of industrial systems. We evaluate…
Load Torque Estimation Method to Design Electric Drivetrains for Offshore Pipe Handling Equipment
2016
One of the main design objectives for electric drivetrains operating in offshore drilling equipment is to keep them as small, yet as effective, as possible, to minimize space they occupy on drill floor and maximize their performance. However, practical experience shows that typically choices made by design engineers are too conservative due to the lack of enough data characterizing load conditions. This results in too costly and too heavy selected components. Therefore, in the current paper we present a method to estimate required full-scale motor torque using a scaled down experimental setup and its computational model. A gripper arm of an offshore vertical pipe handling machine is selecte…