Universite de technologie de Belfort-Montbeliard

About: Universite de technologie de Belfort-Montbeliard is a education organization based out in Belfort, France. It is known for research contribution in the topics: Coating & Microstructure. The organization has 1087 authors who have published 2593 publications receiving 47819 citations.

The study of the laser parameters and environment variables effect on mechanical properties of high compact parts elaborated by selective laser melting 316L powder

Abstract: In this work, a systematic analysis of the main parameters for the selective laser melting (SLM) of a commercial stainless steel 316L powder was conducted to improve the mechanical properties and dimensional accuracy of the fabricated parts. First, the effects of the processing parameters, such as the laser beam scanning velocity, laser power, substrate condition and thickness of the powder layer, on the formation of single tracks for achieving a continuous melting and densification of the material were analysed. Then, the influence of the environmental conditions (gas nature) and of the preheating temperature on the density and dimensional accuracy of the parts was considered. The microstructural features of the SLM SS 316L parts were carefully observed to elucidate the melting-solidification mechanism and the thermal history, which are the basis of the manufacturing process. Finally, the mechanical properties of the corresponding material were also determined.

Wireless communication technologies for ITS applications [Topics in Automotive Networking]

Abstract: Intelligent transport systems are the rising technology in the near future to build cooperative vehicular networks in which a variety of different ITS applications are expected to communicate with a variety of different units. Therefore, the demand for highly customized communication channel for each or sets of similar ITS applications is increased. This article explores the capabilities of available wireless communication technologies in order to produce a win-win situation while selecting suitable carrier( s) for a single application or a profile of similar applications. Communication requirements for future ITS applications are described to select the best available communication interface for the target application(s).

Remaining Useful Life Estimation of Critical Components With Application to Bearings

Abstract: Prognostics activity deals with the estimation of the Remaining Useful Life (RUL) of physical systems based on their current health state and their future operating conditions. RUL estimation can be done by using two main approaches, namely model-based and data-driven approaches. The first approach is based on the utilization of physics of failure models of the degradation, while the second approach is based on the transformation of the data provided by the sensors into models that represent the behavior of the degradation. This paper deals with a data-driven prognostics method, where the RUL of the physical system is assessed depending on its critical component. Once the critical component is identified, and the appropriate sensors installed, the data provided by these sensors are exploited to model the degradation's behavior. For this purpose, Mixture of Gaussians Hidden Markov Models (MoG-HMMs), represented by Dynamic Bayesian Networks (DBNs), are used as a modeling tool. MoG-HMMs allow us to represent the evolution of the component's health condition by hidden states by using temporal or frequency features extracted from the raw signals provided by the sensors. The prognostics process is then done in two phases: a learning phase to generate the behavior model, and an exploitation phase to estimate the current health state and calculate the RUL. Furthermore, the performance of the proposed method is verified by implementing prognostics performance metrics, such as accuracy, precision, and prediction horizon. Finally, the proposed method is applied to real data corresponding to the accelerated life of bearings, and experimental results are discussed.

Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics.

Abstract: The performance of data-driven prognostics approaches is closely dependent on the form and trend of extracted features. Indeed, features that clearly reflect the machine degradation should lead to accurate prognostics, which is the global objective of this paper. This paper contributes a new approach for feature extraction/selection: The extraction is based on trigonometric functions and cumulative transformation, and the selection is performed by evaluating feature fitness using monotonicity and trendability characteristics. The proposition is applied to the time-frequency analysis of nonstationary signals using a discrete wavelet transform. The main idea is to map raw vibration data into monotonic features with early trends, which can be easily predicted. To show that, selected features are used to build a model with a data-driven approach, namely, the summation wavelet-extreme learning machine, that enables good balance between model accuracy and complexity. For validation and generalization purposes, the vibration data from two real applications of prognostics and health management challenges are used: (1) cutting tools from a computer numerical control machine (2010); and (2) bearings from the platform PRONOSTIA (2012). The performance of the proposed approach is thoroughly compared with the classical approach by performing feature fitness analysis, cutting-tool wear “estimation”, and bearings' “long-term prediction” tasks, which validates the proposition.

Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

Abstract: An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.