The Experts below are selected from a list of 78534 Experts worldwide ranked by ideXlab platform

Anvar Valeev - One of the best experts on this subject based on the ideXlab platform.

  • Method of Defect Identification of Industrial Equipment via Remote Strain Gauge Analysis
    2020 International Russian Automation Conference (RusAutoCon), 2020
    Co-Authors: Anvar Valeev
    Abstract:

    The paper is devoted to a new approach for condition monitoring of Industrial Equipment. Future high-quality methods of condition monitoring and defect locating have to be automatic, remote, apply universal methods and sensors, low cost and be centralized. The suggested method is based on ability of calculation of defect location by information of dynamic forces in Equipment in real-time via strain gauge analysis. It gives information about defect location, frequency of defect and its intensity. In this paper analysis of non-destructive test is provided. Concept of new defect locating method is presented. Experimental prototype is shown and a method of identification and filtering of defect is presented. The suggested method is experimentally approved.

  • Diagnostics of Industrial Equipment by Locating and Identification of Defects Via Remote Strain Gauge Analysis
    2019 International Russian Automation Conference (RusAutoCon), 2019
    Co-Authors: Anvar Valeev, Artem Tokarev, Rinat M. Karimov
    Abstract:

    The paper is dedicated to theoretical and instrumental basement for diagnostics of Industrial Equipment by locating and identification of defects via remote strain gauge analysis. Various methods of diagnostics of Industrial Equipment are analyzed from a view point of their objectivity, reliability and ability for automation. It is established that all observed methods in reality are indirect; hence, initial data for analysis has some bias. The solution of this problem is based on the idea to use a remote strain gauge analysis, that means to analyze dynamic force in detail. The Equipment is usually made of steel, so the Equipment may be considered as an absolutely rigid body. Hence, the dynamic force from oscillating defects and parts is completely and without loss transferred to Equipment supports and strain gauges. According to the developed mathematical model is possible to identify the position of source of disturbing force, i.e. to get its real position. Also, instrumental basement is analyzed and prepared for implementing a methodology of locating and identifying defects of Industrial Equipment.

  • complex condition monitoring for Industrial Equipment via remote strain gauge diagnostics and vibration isolating metamaterials
    Vibroengineering PROCEDIA, 2019
    Co-Authors: Anvar Valeev
    Abstract:

    Complex condition monitoring for Industrial Equipment is offered in this paper. The complex monitoring consists of three parts. The first part contains careful diagnostics of defects. It was greatly improved by using a new method of identification of excitation sources. This method is based on defect location with the help of remote strain gauge control and detects the exact coordinates of the detected defects. The second part covers vibration isolation of Industrial Equipment. For this purpose, metamaterials with quasi-zero stiffness are offered. They can almost totally isolate dynamic forces passing through them. The third part is dedicated to decrease oscillations of the Equipment via dynamic absorbers. Their application can be improved by careful control of adjustment. It is offered to control the phase of oscillations on two ends of the spring.

Francesca Mangili - One of the best experts on this subject based on the ideXlab platform.

  • prediction of Industrial Equipment remaining useful life by fuzzy similarity and belief function theory
    Expert Systems With Applications, 2017
    Co-Authors: Piero Baraldi, Enrico Zio, Francesco Di Maio, Sameer Aldahidi, Francesca Mangili
    Abstract:

    We develop a novel prognostic method for estimating the RUL and its uncertainty.The novelty is the combination of fuzzy similarity and Belief Function Theory.The method is applied to simulated and real data in ferritic steel and condenser filters.Results show that the proposed method is superior to other alternative methods.The method aids the maintenance planner to confidently schedule maintenance actions. We develop a novel prognostic method for estimating the Remaining Useful Life (RUL) of Industrial Equipment and its uncertainty. The novelty of the work is the combined use of a fuzzy similarity method for the RUL prediction and of Belief Function Theory for uncertainty treatment. This latter allows estimating the uncertainty affecting the RUL predictions even in cases characterized by few available data, in which traditional uncertainty estimation methods tend to fail. From the practical point of view, the maintenance planner can define the maximum acceptable failure probability for the Equipment of interest and is informed by the proposed prognostic method of the time at which this probability is exceeded, allowing the adoption of a predictive maintenance approach which takes into account RUL uncertainty. The method is applied to simulated data of creep growth in ferritic steel and to real data of filter clogging taken from a Boiling Water Reactor (BWR) condenser. The obtained results show the effectiveness of the proposed method for uncertainty treatment and its superiority to the Kernel Density Estimation (KDE) and the Mean-Variance Estimation (MVE) methods in terms of reliability and precision of the RUL prediction intervals.

  • Prediction of Industrial Equipment Remaining Useful Life by fuzzy similarity and belief function theory
    Expert Systems with Applications, 2017
    Co-Authors: Piero Baraldi, Enrico Zio, Francesco Di Maio, Sameer Al-dahidi, Francesca Mangili
    Abstract:

    We develop a novel prognostic method for estimating the Remaining Useful Life (RUL) of Industrial Equipment and its uncertainty. The novelty of the work is the combined use of a fuzzy similarity method for the RUL prediction and of Belief Function Theory for uncertainty treatment. This latter allows estimating the uncertainty affecting the RUL predictions even in cases characterized by few available data, in which traditional uncertainty estimation methods tend to fail. From the practical point of view, the maintenance planner can define the maximum acceptable failure probability for the Equipment of interest and is informed by the proposed prognostic method of the time at which this probability is exceeded, allowing the adoption of a predictive maintenance approach which takes into account RUL uncertainty. The method is applied to simulated data of creep growth in ferritic steel and to real data of filter clogging taken from a Boiling Water Reactor (BWR) condenser. The obtained results show the effectiveness of the proposed method for uncertainty treatment and its superiority to the Kernel Density Estimation (KDE) and the Mean-Variance Estimation (MVE) methods in terms of reliability and precision of the RUL prediction intervals.

Piero Baraldi - One of the best experts on this subject based on the ideXlab platform.

  • Industrial Equipment reliability estimation: A Bayesian Weibull regression model with covariate selection
    Reliability Engineering & System Safety, 2020
    Co-Authors: Michele Compare, Piero Baraldi, I. Bani, Enrico Zio, Darren Mcdonnell
    Abstract:

    Abstract A three-state continuous-time semi-Markov process is used to model the degradation of an Industrial Equipment. The transition times are assumed Weibull-distributed and influenced by a set of covariates. A Weibull Regression Model is developed within the Bayesian probability framework, to account for the influence of these covariates and estimate the model parameters with the related uncertainty, on the basis of few data and expert judgment. The number of covariates is reduced by a two-step selection procedure derived from the condition monitoring engineering practice. The developed model enables estimating reliability and time-dependent state probabilities for a component degrading in given operational and ambient conditions, represented by a vector of covariates. The model is illustrated by way of a real case study concerning the degradation process affecting diaphragm valves used in the biopharmaceutical industry.

  • prediction of Industrial Equipment remaining useful life by fuzzy similarity and belief function theory
    Expert Systems With Applications, 2017
    Co-Authors: Piero Baraldi, Enrico Zio, Francesco Di Maio, Sameer Aldahidi, Francesca Mangili
    Abstract:

    We develop a novel prognostic method for estimating the RUL and its uncertainty.The novelty is the combination of fuzzy similarity and Belief Function Theory.The method is applied to simulated and real data in ferritic steel and condenser filters.Results show that the proposed method is superior to other alternative methods.The method aids the maintenance planner to confidently schedule maintenance actions. We develop a novel prognostic method for estimating the Remaining Useful Life (RUL) of Industrial Equipment and its uncertainty. The novelty of the work is the combined use of a fuzzy similarity method for the RUL prediction and of Belief Function Theory for uncertainty treatment. This latter allows estimating the uncertainty affecting the RUL predictions even in cases characterized by few available data, in which traditional uncertainty estimation methods tend to fail. From the practical point of view, the maintenance planner can define the maximum acceptable failure probability for the Equipment of interest and is informed by the proposed prognostic method of the time at which this probability is exceeded, allowing the adoption of a predictive maintenance approach which takes into account RUL uncertainty. The method is applied to simulated data of creep growth in ferritic steel and to real data of filter clogging taken from a Boiling Water Reactor (BWR) condenser. The obtained results show the effectiveness of the proposed method for uncertainty treatment and its superiority to the Kernel Density Estimation (KDE) and the Mean-Variance Estimation (MVE) methods in terms of reliability and precision of the RUL prediction intervals.

  • Prediction of Industrial Equipment Remaining Useful Life by fuzzy similarity and belief function theory
    Expert Systems with Applications, 2017
    Co-Authors: Piero Baraldi, Enrico Zio, Francesco Di Maio, Sameer Al-dahidi, Francesca Mangili
    Abstract:

    We develop a novel prognostic method for estimating the Remaining Useful Life (RUL) of Industrial Equipment and its uncertainty. The novelty of the work is the combined use of a fuzzy similarity method for the RUL prediction and of Belief Function Theory for uncertainty treatment. This latter allows estimating the uncertainty affecting the RUL predictions even in cases characterized by few available data, in which traditional uncertainty estimation methods tend to fail. From the practical point of view, the maintenance planner can define the maximum acceptable failure probability for the Equipment of interest and is informed by the proposed prognostic method of the time at which this probability is exceeded, allowing the adoption of a predictive maintenance approach which takes into account RUL uncertainty. The method is applied to simulated data of creep growth in ferritic steel and to real data of filter clogging taken from a Boiling Water Reactor (BWR) condenser. The obtained results show the effectiveness of the proposed method for uncertainty treatment and its superiority to the Kernel Density Estimation (KDE) and the Mean-Variance Estimation (MVE) methods in terms of reliability and precision of the RUL prediction intervals.

Burairah Hussin - One of the best experts on this subject based on the ideXlab platform.

  • A Data-driven Prognostic Model for Industrial Equipment using Time Series Prediction Methods
    Journal of Engineering and Technology, 2013
    Co-Authors: S.a. Azirah, Burairah Hussin, Mokhtar Mohd Yusof
    Abstract:

    Condition-based maintenance strategy is considered popular and received high demand in industry to ensure high availability and reliability of Equipment in the plant. Prognostic is one of an important functions in condition-based maintenance strategy which is used to predict the future condition of the observed and estimate the remaining useful lifetime (RUL) based on the current and historical condition data. Due to the fact that most of the current automated Equipment in industry has the capability to capture and store the condition and process data during operation, the research aimed to formulate a prognostic model based on the integration of the data and predict the series of future condition. This paper presents a data-driven prognostic model to predict the estimated RUL by using condition and process data which are taken from a single unit of Equipment. The structure of prognostic model is presented and two time series methods are employed namely Artifical Neural Network and Double Exponential Smoothing in prognostic process. The feasibility of this prognostic model was demonstrated with applying real data from Industrial Equipment. The result from the model shows that both of the methods are able to extrapolate the extimated  RUL  and  give  useful  information to the maintenance department to take an appropriate decision.

  • Application of multi-step time series prediction for Industrial Equipment prognostic
    2011 IEEE Conference on Open Systems, 2011
    Co-Authors: Siti Azirah Asmai, Rosmiza Wahida Abdullah, Abd. Samad Hasan Basari, Burairah Hussin
    Abstract:

    The use of prognostics is critically to be implemented in Industrial. This paper presents an application of multi-step time series prediction to support Industrial Equipment prognostic. An artificial neural network technique with sliding window is considered for the multi-step prediction which is able to predict the series of future Equipment condition. The structure of prognostic application is presented. The feasibility of this prediction application was demonstrated by applying real condition monitoring data of Industrial Equipment.

  • HIS - Neural network prognostics model for Industrial Equipment maintenance
    2011 11th International Conference on Hybrid Intelligent Systems (HIS), 2011
    Co-Authors: Siti Azirah Asmai, Abd. Samad Hasan Basari, Abdul Samad Shibghatullah, Nuzulha Khilwani Ibrahim, Burairah Hussin
    Abstract:

    This paper presents a new prognostics model based on neural network technique for supporting Industrial maintenance decision. In this study, the probabilities of failure based on the real condition Equipment are initially calculated by using logistic regression method. The failure probabilities are subsequently utilized as input for prognostics model to predict the future value of failure condition and then used to estimate remaining useful lifetime of Equipment. By having a time series of predicted failure probability, the failure distribution can be generated and used in the maintenance cost model to decide the optimal time to do maintenance. The proposed prognostic model is implemented in the Industrial Equipment known as autoclave burner. The result from the model reveals that it can give prior warnings and indication to the maintenance department to take an appropriate decision instead of dealing with the failures while the autoclave burner is still operating. This significant contribution provides new insights into the maintenance strategy which enables the use of existing condition data from Industrial Equipment and prognostics approach

Enrico Zio - One of the best experts on this subject based on the ideXlab platform.

  • Industrial Equipment reliability estimation: A Bayesian Weibull regression model with covariate selection
    Reliability Engineering & System Safety, 2020
    Co-Authors: Michele Compare, Piero Baraldi, I. Bani, Enrico Zio, Darren Mcdonnell
    Abstract:

    Abstract A three-state continuous-time semi-Markov process is used to model the degradation of an Industrial Equipment. The transition times are assumed Weibull-distributed and influenced by a set of covariates. A Weibull Regression Model is developed within the Bayesian probability framework, to account for the influence of these covariates and estimate the model parameters with the related uncertainty, on the basis of few data and expert judgment. The number of covariates is reduced by a two-step selection procedure derived from the condition monitoring engineering practice. The developed model enables estimating reliability and time-dependent state probabilities for a component degrading in given operational and ambient conditions, represented by a vector of covariates. The model is illustrated by way of a real case study concerning the degradation process affecting diaphragm valves used in the biopharmaceutical industry.

  • prediction of Industrial Equipment remaining useful life by fuzzy similarity and belief function theory
    Expert Systems With Applications, 2017
    Co-Authors: Piero Baraldi, Enrico Zio, Francesco Di Maio, Sameer Aldahidi, Francesca Mangili
    Abstract:

    We develop a novel prognostic method for estimating the RUL and its uncertainty.The novelty is the combination of fuzzy similarity and Belief Function Theory.The method is applied to simulated and real data in ferritic steel and condenser filters.Results show that the proposed method is superior to other alternative methods.The method aids the maintenance planner to confidently schedule maintenance actions. We develop a novel prognostic method for estimating the Remaining Useful Life (RUL) of Industrial Equipment and its uncertainty. The novelty of the work is the combined use of a fuzzy similarity method for the RUL prediction and of Belief Function Theory for uncertainty treatment. This latter allows estimating the uncertainty affecting the RUL predictions even in cases characterized by few available data, in which traditional uncertainty estimation methods tend to fail. From the practical point of view, the maintenance planner can define the maximum acceptable failure probability for the Equipment of interest and is informed by the proposed prognostic method of the time at which this probability is exceeded, allowing the adoption of a predictive maintenance approach which takes into account RUL uncertainty. The method is applied to simulated data of creep growth in ferritic steel and to real data of filter clogging taken from a Boiling Water Reactor (BWR) condenser. The obtained results show the effectiveness of the proposed method for uncertainty treatment and its superiority to the Kernel Density Estimation (KDE) and the Mean-Variance Estimation (MVE) methods in terms of reliability and precision of the RUL prediction intervals.

  • Prediction of Industrial Equipment Remaining Useful Life by fuzzy similarity and belief function theory
    Expert Systems with Applications, 2017
    Co-Authors: Piero Baraldi, Enrico Zio, Francesco Di Maio, Sameer Al-dahidi, Francesca Mangili
    Abstract:

    We develop a novel prognostic method for estimating the Remaining Useful Life (RUL) of Industrial Equipment and its uncertainty. The novelty of the work is the combined use of a fuzzy similarity method for the RUL prediction and of Belief Function Theory for uncertainty treatment. This latter allows estimating the uncertainty affecting the RUL predictions even in cases characterized by few available data, in which traditional uncertainty estimation methods tend to fail. From the practical point of view, the maintenance planner can define the maximum acceptable failure probability for the Equipment of interest and is informed by the proposed prognostic method of the time at which this probability is exceeded, allowing the adoption of a predictive maintenance approach which takes into account RUL uncertainty. The method is applied to simulated data of creep growth in ferritic steel and to real data of filter clogging taken from a Boiling Water Reactor (BWR) condenser. The obtained results show the effectiveness of the proposed method for uncertainty treatment and its superiority to the Kernel Density Estimation (KDE) and the Mean-Variance Estimation (MVE) methods in terms of reliability and precision of the RUL prediction intervals.

  • Prognostics and Health Management of Industrial Equipment
    2012
    Co-Authors: Enrico Zio, Enrico Zio
    Abstract:

    Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an Equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of Equipment failure occurrence and associated residual useful life (RUL).