Leakage analysis of gasketed flange joints under combined internap pressure and thermal loading

Leakage in Gasketed Flanged Joints (GFJs) have always been a great problem for the process industry. The sealing performance of a GFJ depends on its installation and applied loading conditions. This paper aims to finding the leak rate through ANSI class#150 flange joints using a compressed asbestos sheet (CAS) gasket under combined structural and thermal transient loading conditions using two different leak rate models and two different bolt-up levels. The first model is a Gasket Compressive Strain model in which strains are determined using finite element analysis. The other model is based on Porous Media Theory in which gasket is considered as porous media. Leakage rates are determined using both leak rate models and are compared against appropriate tightness classes and the effectiveness of each approach is presented

Novel failure prognostics approach with dynamic thresholds for machine degradation.

International audienceEstimating remaining useful life (RUL) of critical machinery is a challenging task. It is achieved through essential steps of data acquisition, data pre-processing and prognostics modeling. To estimate RUL of a degrading machinery, prognostics modeling phase requires precise knowledge about failure threshold (FT) (or failure definition). Practically, degrading machinery can have different levels (states) of degradation before failure, and prognostics can be quite complicated or even impossible when there is absence of prior knowledge about actual states of degrading machinery or FT. In this paper a novel approach is proposed to improve failure prognostics. In brief, the proposed prognostics model integrates two new algorithms, namely, a Summation Wavelet Extreme Learning Machine (SWELM) and Subtractive-Maximum Entropy Fuzzy Clustering (S-MEFC) to predict degrading behavior, automatically identify the states of degrading machinery, and to dynamically assign FT. Indeed, for practical reasons there is no interest in assuming FT for RUL estimation. The effectiveness of the approach is judged by applying it to real dataset in order to estimate future breakdown of a real machinery

SW-ELM : A summation wavelet extreme learning machine algorithm with a priori initialization.

International audienceCombining neural networks and wavelet theory as an approximation or prediction models appears to be an effective solution in many applicative areas. However, when building such systems, one has to face parsimony problem, i.e., to look for a compromise between the complexity of the learning phase and accuracy performances. Following that, the aim of this paper is to propose a new structure of connectionist network, the Summation Wavelet Extreme Learning Machine (SW-ELM) that enables good accuracy and generalization performances, while limiting the learning time and reducing the impact of random initialization procedure. SW-ELM is based on Extreme Learning Machine (ELM) algorithm for fast batch learning, but with dual activation functions in the hidden layer nodes. This enhances dealing with non-linearity in an efficient manner. The initialization phase of wavelets (of hidden nodes) and neural network parameters (of input-hidden layer) is performed a priori, even before data are presented to the model. The whole proposition is illustrated and discussed by performing tests on three issues related to time-series application: an "input-output" approximation problem, a one-step ahead prediction problem, and a multi-steps ahead prediction problem. Performances of SW-ELM are benchmarked with ELM, Levenberg Marquardt algorithm for Single Layer Feed Forward Network (SLFN) and ELMAN network on six industrial data sets. Results show the significance of performances achieved by SW-ELM

Features Selection Procedure for Prognostics: An Approach Based on Predictability

International audiencePrognostic aims at estimating the remaining useful life (RUL) of a degrading equipment, i.e at predicting the life time at which a component or a system will be unable to perform a desired function. This task is achieved through essential steps of data acquisition, feature extraction and selection, and prognostic modeling. This paper emphasizes on the selection phase and aims at showing that it should be performed according to the predictability of features: as there is no interest in retaining features that are hard to be predicted. Thereby, predictability is de ned and a feature selection procedure based on this concept is proposed. The e ectiveness of the approach is judged by applying it on a real-world case: through comparison is made in order to show that the better predictable features lead to better RUL estimation

A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling

International audiencePerformances of data-driven approaches are closely related to the form and trend of extracted features (that can be seen as time series health indicators). 1) Even if much of datadriven approaches are suitable to catch non-linearity in signals, features with monotonic trends (which is not always the case!) are likely to lead to better estimates. 2) Also, some classical extracted features do not show variation until a few time before failure occurs, which prevents performing RUL predictions in a timely manner to plan maintenance task. The aim of this paper is to present a novel feature extraction procedure to face with these two problems. Two aspects are considered. Firstly, the paper focuses on feature extraction in a new manner by utilizing trigonometric functions to extract features (health indicators) rather than typical statistic measures like RMS, etc. The proposed approach is applied on time-frequency analysis with Discrete Wavelet Transform (DWT). Secondly, a simple way of building new features based on cumulative functions is also proposed in order to transform time series into descriptors that depict accumulated wear. This approach can be extended to other types of features. The main idea of both developments is to map raw data with monotonic features with early trends, i.e., with descriptors that can be easily predicted. This methodology can enhance prognostics modeling and RUL prediction. The whole proposition is illustrated and discussed thanks to tests performed on vibration datasets from PRONOSTIA, an experimental platform that enables accelerated degradation of bearings

Improving data-driven prognostics by assessing predictability of features.

International audienceWithin condition based maintenance (CBM), the whole aspect of prognostics is composed of various tasks from multidimensional data to remaining useful life (RUL) of the equipment. Apart from data acquisition phase, data-driven prognostics is achieved in three main steps: features extraction and selection, features prediction, and health-state classification. The main aim of this paper is to propose a way of improving existing data-driven procedure by assessing the predictability of features when selecting them. The underlying idea is that prognostics should take into account the ability of a practitioner (or its models) to perform long term predictions. A predictability measure is thereby defined and applied to temporal predictions during the learning phase, in order to reduce the set of selected features. The proposed methodology is tested on a real data set of bearings to analyze the effectiveness of the scheme. For illustration purpose, an adaptive neuro-fuzzy inference system is used as a prediction model, and classification aspect is met by the well known Fuzzy Cmeans algorithm. Both enable to perform RUL estimation and results appear to be improved by applying the proposed strategy

A STUDY ON BASIC KNOWLEDGE AND PRACTICES FOR ROAD TRAFFIC SAFETY MEASURES AMONG UNDERGRADUATE MEDICAL STUDENTS OF UTTAR PRADESH

Objectives: The objectives of this study were to assess the knowledge and practices of road traffic safety measures among undergraduate medical students and recommendation to prevent road traffic accident. Methods: It was an institutional-based cross-sectional study among undergraduate medical students who knew driving. Total 138 study subjects were purposively selected from three batches. A self-structured questionnaire based on knowledge and practices related to road traffic safety measures with Yes/No answer pattern. Data were collected, compiled, and analyzed using appropriate software. Results: Overall level of knowledge for road traffic safety measures was good/moderate among 37.7% of medical students each while poor among 24.6% students (more among 1st year/39.3% than 2nd year/19.0%, 3rd year/10.0%) (statistically significant X2 =13.304, p-value=0.01). Road traffic safety practices were followed by students (%), namely, wearing a seat belt while driving/seating in four-wheeler’ by 84.1% and neither keep specified speed limit on road/2.9% nor obey all traffic signals/lights/signs’/2.9%. Few students use mobile phone while driving, namely, 2nd year/19.0% and 3rd year/10.0%. Some students “Not Wear Helmet while driving a two-wheeler”/21.7%; “Wrong overtaking from left side”/17.4%; and “drive even when alcoholic”/4.3% of participants. Conclusions: Knowledge for road traffic rules/regulation was good/moderate among average number of students. Majority of medical students’ obey all traffic rules/light signal and signs while some students still did not follow it; use mobile phone while driving/not stop at zebra crossing and neither use indicators while turning nor keep valid DL. To improve the current scenario, road traffic safety rules/measures should be added in our medical curriculum and there is need of frequent awareness campaign related to road safety measures to change their behavior while driving and save their precious life

Career Development an Imperative of Job Satisfaction and Career Commitment: Empirical Evidence from Pakistani Employees in Banking Sector

The idea of strengthening human capital to beginning creativeness, business soul, and advancement through preparing the careers of institutional members using HRM policies and methods to develop different skills, mindsets and expertise with the ultimate aim to provide a range of innovative goods and services is gaining attention. The overall perspective for the research study was to discover the effects and outcomes of profession growth initiatives on companies and employees. The survey is conducted to collect data from the Banking sector in Islamabad and sample selected is of five major private banks. The data is analyzed by using SPSS and Amos to authenticate the model and propositions made by the researcher. Organizations invest resources in profession growth kinds of actions for recruiting, there tends to be less investment in similar kinds of actions for worker retention. This paper examines the link between profession preparing and profession control as antecedents of profession growth and job fulfillment, and profession dedication as its outcome. There is a significant link between the factors of profession preparing and profession control, and profession growth, and in turn, with job fulfillment and profession dedication. The paper converses about the significances of these conclusions for career development

Robust, reliable and applicable tool wear monitoring and prognostic : approach based on an Improved-Extreme Learning Machine.

International audienceAlthough efforts in this field are significant around the world, real prognostics systems are still scarce in industry. Indeed, it is hard to provide efficient approaches that are able to handle with the inherent uncertainty of prognostics and nobody is able to a priori ensure that an accurate prognostic model can be built. As for an example of remaining problems, consider datadriven prognostics approaches: how to ensure that a model will be able to face with inputs variation with respect to those ones that have been learned, how to ensure that a learned-model will face with unknown data, how to ensure convergence of algorithms, etc. In other words, robustness, reliability and applicability of a prognostic approach are still open areas. Following that, the aim of this paper is to address these challenges by proposing a new neural network (structure and algorithm) that enhances reliability of RUL estimates while improving applicability of the approach. Robustness, reliability and applicability aspects are first discussed and defined according to literature. On this basis, a new connexionist system is proposed for prognostics: the Improved-Extreme Learning machine (Imp-ELM). This neural network, based on complex activation functions, enables to reduce the influence of human choices and initial parameterization, while improving accuracy of estimates and speeding the learning phase. The whole proposition is illustrated by performing tests on a real industrial case of cutting tools from a Computer Numerical Control (CNC) machine. This is achieved by predicting tool condition (wear) in terms of remaining cuts successfully made. Thorough comparisons with adaptive neuro fuzzy inference system (ANFIS) and existing ELM algorithm are also given. Results show improved robustness, reliability and applicability performances

FPGA IMPLEMENTATION OF ADVANCE ENCRYPTION STANDARD WITH SINGLE KEY

Advanced Encryption Standard (AES), is known as most secured encryption standard now a days. Many researchers have implemented it in different languages like java, C and C++ with different algorithms. Recently the AES 128-bit has been implemented using Verilog on FPGA with equipped key being encrypted along with data input in whole process. In this paper the AES 128-bit encryption and decryption process with key which is only used for data input and is not encrypted throughout the encryption/decryption process. Results are same but our algorithm is slightly faster because only data is encrypted in the process of encryption, thus process time and area is optimized