Anibal Reñones | Fundacion CARTIF (original) (raw)

Papers by Anibal Reñones

Research paper thumbnail of Visualización inteligente para maquinas-herramienta: soporte a la toma de decisiones

Research paper thumbnail of Cognitive Solutions in Process Industry: H2020 CAPRI Project

Research paper thumbnail of CAPRI Smart decision support - ASPHALT USE CASE

Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022

Research paper thumbnail of Ai4manufacturing Toolkit: The Ai Regiop Project’s Collection Of Artificial Intelligence

Research paper thumbnail of CAPRI Industrial Analytics Platform and Data Space - ASPHALT USE CASE

Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022

Research paper thumbnail of Vibration-Based Smart Sensor for High-Flow Dust Measurement

Sensors

Asphalt mixes comprise aggregates, additives and bitumen. The aggregates are of varying sizes, an... more Asphalt mixes comprise aggregates, additives and bitumen. The aggregates are of varying sizes, and the finest category, referred to as sands, encompasses the so-called filler particles present in the mixture, which are smaller than 0.063 mm. As part of the H2020 CAPRI project, the authors present a prototype for measuring filler flow, through vibration analysis. The vibrations are generated by the filler particles crashing to a slim steel bar capable of withstanding the challenging conditions of temperature and pressure within the aspiration pipe of an industrial baghouse. This paper presents a prototype developed to address the need for quantifying the amount of filler in cold aggregates, considering the unavailability of commercially viable sensors suitable for the conditions encountered during asphalt mix production. In laboratory settings, the prototype simulates the aspiration process of a baghouse in an asphalt plant, accurately reproducing particle concentration and mass flow...

Research paper thumbnail of CAPRI Industrial IoT Platform and Data Space - ASPHALT USE CASE

Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022

Research paper thumbnail of Fault Diagnosis of Multitooth Machine Tool Based on Statistical Signal Processing

IFAC Proceedings Volumes, 2002

Research paper thumbnail of CAPRI Smart knowledge and semantic data models - ASPHALT USE CASE

Research paper thumbnail of Article A Virtual Sensor for Online Fault Detection of Multitooth-Tools

Research paper thumbnail of European Big Data Value Association Position Paper on the Smart Manufacturing Industry

Enterprise Interoperability, 2018

Research paper thumbnail of F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms

83 Anibal Reñones and Marta Galende F.A.I.R. open dataset of brushed DC motor faults for testing ... more 83 Anibal Reñones and Marta Galende F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Regular Issue, Vol. 9 N. 4 (2020), 83-94 eISSN: 2255-2863 https://adcaij.usal.es Ediciones Universidad de Salamanca cc by-nc-nd F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms

Research paper thumbnail of Fault Detection in Multitooth Machine Tool Using Different Statistical Approaches

7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, 2009

ABSTRACT This paper describes a fault diagnosis method applied to a real multitooth machine tool.... more ABSTRACT This paper describes a fault diagnosis method applied to a real multitooth machine tool. Several statistical alternatives are used to diagnose the different faults that may appear such as insert breakage within multitooth tools. These complex tools are used for mass production of pieces in car industry, and the described application has been applied into different kinds of machining operations and cutting conditions.

Research paper thumbnail of An SVM-Based Solution for Fault Detection in Wind Turbines

Research paper thumbnail of Statistical vibration analysis for predictive maintenance of machines working under large variation of speed and load

Research paper thumbnail of Wind Turbines Fault Diagnosis Using Ensemble Classifiers

Lecture Notes in Computer Science, 2012

ABSTRACT Fault diagnosis in machines that work under a wide range of speeds and loads is currentl... more ABSTRACT Fault diagnosis in machines that work under a wide range of speeds and loads is currently an active area of research. Wind turbines are one of the most recent examples of these machines in industry. Conventional vibration analysis applied to machines throughout their operation is of limited utility when the speed variation is too high. This work proposes an alternative methodology for fault diagnosis in machines: the combination of angular resampling techniques for vibration signal processing and the use of data mining techniques for the classification of the operational state of wind turbines. The methodology has been validated over a test-bed with a large variation of speeds and loads which simulates, on a smaller scale, the real conditions of wind turbines. Over this test-bed two of the most common typologies of faults in wind turbines have been generated: imbalance and misalignment. Several data mining techniques have been used to analyze the dataset obtained by order analysis, having previously processed signals with angular resampling technique. Specifically, the methods used are ensemble classifiers built with Bagging, Adaboost, Geneneral Boosting Projection and Rotation Forest; the best results having been achieved with Adaboost using C4.5 decision trees as base classifiers.

Research paper thumbnail of A Virtual Sensor for Online Fault Detection of Multitooth-Tools

Research paper thumbnail of Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation

Mechanical Systems and Signal Processing, 2011

Research paper thumbnail of Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load

Mechanical Systems and Signal Processing, 2012

ABSTRACT In this paper the authors are dealing with the detection of different mechanical faults ... more ABSTRACT In this paper the authors are dealing with the detection of different mechanical faults (unbalance and misalignment) under a wide range of working conditions of speed and load. The conditions tested in a test bench are similar to the ones that can be found in different kinds of machines like for example wind turbines. The authors demonstrate how to take advantage of the information on vibrations from the mechanical system under study in a wide range of load and speed conditions. Using such information the prognosis and detection of faults is faster and more reliable than the one obtained from an analysis over a restricted range of working conditions (e.g. nominal).

Research paper thumbnail of Experimental analysis of change detection algorithms for multitooth machine tool fault detection

Mechanical Systems and Signal Processing, 2009

This paper describes an industrial application of fault diagnosis method for a multitooth machine... more This paper describes an industrial application of fault diagnosis method for a multitooth machine tool. Different statistical approaches have been used to detect and diagnose insert breakage in multitooth tools based on the analysis of electrical power consumption of the ...

Research paper thumbnail of Visualización inteligente para maquinas-herramienta: soporte a la toma de decisiones

Research paper thumbnail of Cognitive Solutions in Process Industry: H2020 CAPRI Project

Research paper thumbnail of CAPRI Smart decision support - ASPHALT USE CASE

Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022

Research paper thumbnail of Ai4manufacturing Toolkit: The Ai Regiop Project’s Collection Of Artificial Intelligence

Research paper thumbnail of CAPRI Industrial Analytics Platform and Data Space - ASPHALT USE CASE

Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022

Research paper thumbnail of Vibration-Based Smart Sensor for High-Flow Dust Measurement

Sensors

Asphalt mixes comprise aggregates, additives and bitumen. The aggregates are of varying sizes, an... more Asphalt mixes comprise aggregates, additives and bitumen. The aggregates are of varying sizes, and the finest category, referred to as sands, encompasses the so-called filler particles present in the mixture, which are smaller than 0.063 mm. As part of the H2020 CAPRI project, the authors present a prototype for measuring filler flow, through vibration analysis. The vibrations are generated by the filler particles crashing to a slim steel bar capable of withstanding the challenging conditions of temperature and pressure within the aspiration pipe of an industrial baghouse. This paper presents a prototype developed to address the need for quantifying the amount of filler in cold aggregates, considering the unavailability of commercially viable sensors suitable for the conditions encountered during asphalt mix production. In laboratory settings, the prototype simulates the aspiration process of a baghouse in an asphalt plant, accurately reproducing particle concentration and mass flow...

Research paper thumbnail of CAPRI Industrial IoT Platform and Data Space - ASPHALT USE CASE

Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022

Research paper thumbnail of Fault Diagnosis of Multitooth Machine Tool Based on Statistical Signal Processing

IFAC Proceedings Volumes, 2002

Research paper thumbnail of CAPRI Smart knowledge and semantic data models - ASPHALT USE CASE

Research paper thumbnail of Article A Virtual Sensor for Online Fault Detection of Multitooth-Tools

Research paper thumbnail of European Big Data Value Association Position Paper on the Smart Manufacturing Industry

Enterprise Interoperability, 2018

Research paper thumbnail of F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms

83 Anibal Reñones and Marta Galende F.A.I.R. open dataset of brushed DC motor faults for testing ... more 83 Anibal Reñones and Marta Galende F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Regular Issue, Vol. 9 N. 4 (2020), 83-94 eISSN: 2255-2863 https://adcaij.usal.es Ediciones Universidad de Salamanca cc by-nc-nd F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms

Research paper thumbnail of Fault Detection in Multitooth Machine Tool Using Different Statistical Approaches

7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, 2009

ABSTRACT This paper describes a fault diagnosis method applied to a real multitooth machine tool.... more ABSTRACT This paper describes a fault diagnosis method applied to a real multitooth machine tool. Several statistical alternatives are used to diagnose the different faults that may appear such as insert breakage within multitooth tools. These complex tools are used for mass production of pieces in car industry, and the described application has been applied into different kinds of machining operations and cutting conditions.

Research paper thumbnail of An SVM-Based Solution for Fault Detection in Wind Turbines

Research paper thumbnail of Statistical vibration analysis for predictive maintenance of machines working under large variation of speed and load

Research paper thumbnail of Wind Turbines Fault Diagnosis Using Ensemble Classifiers

Lecture Notes in Computer Science, 2012

ABSTRACT Fault diagnosis in machines that work under a wide range of speeds and loads is currentl... more ABSTRACT Fault diagnosis in machines that work under a wide range of speeds and loads is currently an active area of research. Wind turbines are one of the most recent examples of these machines in industry. Conventional vibration analysis applied to machines throughout their operation is of limited utility when the speed variation is too high. This work proposes an alternative methodology for fault diagnosis in machines: the combination of angular resampling techniques for vibration signal processing and the use of data mining techniques for the classification of the operational state of wind turbines. The methodology has been validated over a test-bed with a large variation of speeds and loads which simulates, on a smaller scale, the real conditions of wind turbines. Over this test-bed two of the most common typologies of faults in wind turbines have been generated: imbalance and misalignment. Several data mining techniques have been used to analyze the dataset obtained by order analysis, having previously processed signals with angular resampling technique. Specifically, the methods used are ensemble classifiers built with Bagging, Adaboost, Geneneral Boosting Projection and Rotation Forest; the best results having been achieved with Adaboost using C4.5 decision trees as base classifiers.

Research paper thumbnail of A Virtual Sensor for Online Fault Detection of Multitooth-Tools

Research paper thumbnail of Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation

Mechanical Systems and Signal Processing, 2011

Research paper thumbnail of Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load

Mechanical Systems and Signal Processing, 2012

ABSTRACT In this paper the authors are dealing with the detection of different mechanical faults ... more ABSTRACT In this paper the authors are dealing with the detection of different mechanical faults (unbalance and misalignment) under a wide range of working conditions of speed and load. The conditions tested in a test bench are similar to the ones that can be found in different kinds of machines like for example wind turbines. The authors demonstrate how to take advantage of the information on vibrations from the mechanical system under study in a wide range of load and speed conditions. Using such information the prognosis and detection of faults is faster and more reliable than the one obtained from an analysis over a restricted range of working conditions (e.g. nominal).

Research paper thumbnail of Experimental analysis of change detection algorithms for multitooth machine tool fault detection

Mechanical Systems and Signal Processing, 2009

This paper describes an industrial application of fault diagnosis method for a multitooth machine... more This paper describes an industrial application of fault diagnosis method for a multitooth machine tool. Different statistical approaches have been used to detect and diagnose insert breakage in multitooth tools based on the analysis of electrical power consumption of the ...