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Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics
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. Expand
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A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling
Performances 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 data-driven approachesExpand
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SW-ELM: A summation wavelet extreme learning machine algorithm with a priori parameter initialization
We 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 a random initialization procedure. Expand
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A robust and reliable data-driven prognostics approach based on Extreme Learning Machine and Fuzzy Clustering
Prognostics and Health Management (PHM) aims at extending the life cycle of engineerin gassets, while reducing exploitation and maintenance costs. For this reason,prognostics is considered as a keyExpand
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Prognostics of Proton Exchange Membrane Fuel Cells stack using an ensemble of constraints based connectionist networks
Proton Exchange Membrane Fuel Cell (PEMFC) is considered the most versatile among available fuel cell technologies, which qualify for diverse applications. However, the large-scale industrialExpand
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Features Selection Procedure for Prognostics: An Approach Based on Predictability
Abstract Prognostic 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 desiredExpand
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Robust, reliable and applicable tool wear monitoring and prognostic: Approach based on an improved-extreme learning machine
Although 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 withExpand
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Improving data-driven prognostics by assessing predictability of features.
Within 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 dataExpand
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PEM fuel cell prognostics under variable load: A data-driven ensemble with new incremental learning
This paper contributes the first application on data-driven prognostics of PEMFC stack under variable load for combined heat and power generation (μCHP) using Summation Wavelet-Extreme Learning Machine models. Expand
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State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels
Abstract Integrating prognostics to a real application requires a certain maturity level and for this reason there is a lack of success stories about development of a complete Prognostics and HealthExpand
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