The Experts below are selected from a list of 21249 Experts worldwide ranked by ideXlab platform
Toni Blass - One of the best experts on this subject based on the ideXlab platform.
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further understanding of rolling contact fatigue in rolling element bearings a review
Tribology International, 2019Co-Authors: Mostafa El Laithy, L Wang, T J Harvey, Bernd Vierneusel, Martin Correns, Toni BlassAbstract:Abstract Rolling bearings are one of the most widely used components in Industrial Machinery. If suitably mounted, loaded, lubricated and isolated from contamination, rolling contact fatigue (RCF) is believed to be the most probable mode of failure resulting in subsurface originated failures. Over the past several decades many researchers have studied the failure and microstructural alterations in bearings such as dark etching regions and white etching bands and how operating conditions such as pressure, temperature and running time impact them during RCF. This paper aims to provide an overview of such alterations, their properties, formation mechanisms and impact on bearing failure.
Javier Gonzalez-jimenez - One of the best experts on this subject based on the ideXlab platform.
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A predictive model for the maintenance of Industrial Machinery in the context of industry 4.0
Engineering Applications of Artificial Intelligence, 2020Co-Authors: Jose-raul Ruiz-sarmiento, Javier Monroy, Francisco-angel Moreno, Cipriano Galindo, Jose-maria Bonelo, Javier Gonzalez-jimenezAbstract:Abstract The Industry 4.0 paradigm is being increasingly adopted in the production, distribution and commercialization chains worldwide. The integration of the cutting-edge techniques behind it entails a deep and complex revolution – changing from scheduled-based processes to smart, reactive ones – that has to be thoroughly applied at different levels. Aiming to shed some light on the path towards such evolution, this work presents an Industry 4.0 based approach for facing a key aspect within factories: the health assessment of critical assets. This work is framed in the context of the innovative project SiMoDiM, which pursues the design and integration of a predictive maintenance system for the stainless steel industry. As a case of study, it focuses on the Machinery involved in the production of high-quality steel sheets, i.e. the Hot Rolling Process, and concretely on predicting the degradation of the drums within the heating coilers of Steckel mills (parts with an expensive replacement that work under severe mechanical and thermal stresses). This paper describes a predictive model based on a Bayesian Filter, a tool from the Machine Learning field, to estimate and predict the gradual degradation of such Machinery, permitting the operators to make informed decisions regarding maintenance operations. For achieving that, the proposed model iteratively fuses expert knowledge with real time information coming from the hot rolling processes carried out in the factory. The predictive model has been fitted and evaluated with real data from ∼ 118k processes, proving its virtues for promoting the Industry 4.0 era.
Juan Antonio Gilmartinezabarca - One of the best experts on this subject based on the ideXlab platform.
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integration of business and manufacturing processes through Industrial Machinery as a service approach
IEEE International Conference on Services Computing, 2011Co-Authors: Virgilio Gilartiglesias, Francisco Maci Perez, Diego Marcosjorquera, Francisco J Morajimeno, Juan Antonio GilmartinezabarcaAbstract:In manufacturing organizations is difficult to reach the requirements of the new business models (agile and dynamic adaptation to changes) due to technological and conceptual constraints between elements located at different levels of the organization, which prevents the integration of business and manufacturing processes. In this paper, a new Industrial Machinery model that achieves this integration has been proposed. This model, named IMaaS, shows the Industrial Machinery as a set of business processes, removing the conceptual constraints, and exposed as services, removing technology constraints.
Mostafa El Laithy - One of the best experts on this subject based on the ideXlab platform.
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further understanding of rolling contact fatigue in rolling element bearings a review
Tribology International, 2019Co-Authors: Mostafa El Laithy, L Wang, T J Harvey, Bernd Vierneusel, Martin Correns, Toni BlassAbstract:Abstract Rolling bearings are one of the most widely used components in Industrial Machinery. If suitably mounted, loaded, lubricated and isolated from contamination, rolling contact fatigue (RCF) is believed to be the most probable mode of failure resulting in subsurface originated failures. Over the past several decades many researchers have studied the failure and microstructural alterations in bearings such as dark etching regions and white etching bands and how operating conditions such as pressure, temperature and running time impact them during RCF. This paper aims to provide an overview of such alterations, their properties, formation mechanisms and impact on bearing failure.
Peng Yan - One of the best experts on this subject based on the ideXlab platform.
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change detection in rotational speed of Industrial Machinery using bag of words based feature extraction from vibration signals
Measurement, 2019Co-Authors: Shaohua Yang, Aiqun Wang, Jie Liu, Peng YanAbstract:Abstract Detection of early changes in rotational speed is highly required in on-line process monitoring of Industrial manufacturing and numerical controlled machining. This paper proposes a Bag-of-Words (BoW) based feature extraction method that uses vibration signal with such a motivation. Initially, the BoW model is adopted to cluster a prior collection of vibration signals. Then, for a new vibration signal, two strategies: histogram-based encoding (HBE) and embedding-based encoding (EBE), are investigated respectively and comprehensively to encode it based on the BoW model, in order to extract its dynamic characteristic. The entropy is subsequently computed, supporting continuous analysis of dynamic machine status over time. Distance metric is finally adopted to make decision by hypothesis testing. The method is validated with both simulated and real-engineering signals. Results reveal excellent performance by using EBE, coupled with Kolmogorov distance, in the proposed method. Comparison with state-of-the-art competitors demonstrate the priority and robustness of the method.
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An integrated framework for statistical change detection in running status of Industrial Machinery under transient conditions
ISA transactions, 2019Co-Authors: Chen Guangyuan, Jie Liu, Peng YanAbstract:Abstract Early detection of changes in machine running status from sensor signals attracts increasing attention for the monitoring and assessment of complex Industrial machineries under transient conditions. This paper presents a detection method that integrates one-class SVM with a pre-defined Autoregressive Integrated Moving Average (ARIMA) regression process. Meanwhile, an automatic cyclic-analysis method is also developed as a preprocessing to suppress temporal non-stationarity in condition signal before feeding it to the monitoring process. As such, a novel framework of continuous monitoring of condition signal is finally presented to inspect whether an unexpected running status change occurs or not during continuous machine operations. The proposed framework is applied to three representative condition monitoring applications: external loading condition monitoring, bearing health condition assessment, and rotational speed condition monitoring. Comparisons with existing methods are also provided, where the proposed method demonstrates its significant improvements over others.