Anibal Reñones - Profile on Academia.edu (original) (raw)
Papers by Anibal Reñones
CAPRI Industrial IoT Platform and Data Space - STEEL USE CASE
Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022
La investigación desarrollada en este artículo ha sido financiada por el Fondo Europeo de Desarro... more La investigación desarrollada en este artículo ha sido financiada por el Fondo Europeo de Desarrollo Regional FEDER a través del proyecto DISRUPTIVE (Dinamización de los Digital Innovation Hubs dentro de la región PocTep para el impulso de las TIC disruptivas y de última generación a través de la cooperación en la región transfronteriza) del Programa Interreg V-A España-Portugal (POCTEP) 2014-2020 (0677_DISRUPTIVE_2_E). Las opiniones son de exclusiva responsabilidad de los autores que las emiten.
Cognitive Solutions in Process Industry: H2020 CAPRI Project
CAPRI Smart decision support - ASPHALT USE CASE
Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022
Resumen: El proyecto AI REGIO «Regiones y DIHs para la transformacion digital impulsada por la IA... more Resumen: El proyecto AI REGIO «Regiones y DIHs para la transformacion digital impulsada por la IA de las PYMEs manufactureras europeas» tiene como objetivo principal apoyar la creación y el crecimiento sostenible de los Centros de Innovación Digital (DIH) centrados en Inteligencia Artificial (IA), para fomentar la integración de las innovaciones digitales en los procesos de transformación de las PYMEs manufactureras en Europa. El presente artículo presenta el estado actual de desarrollo de una de las plataforma abiertas que se estan desarrollando dentro del proyecto, a la que denominamos AI4Manufacturing Toolkit, un kit de herramientas y técnicas de analisis en la que los conjuntos de datos, preparados y depurados, pueden ser explotados utilizando técnicas de inteligencia artificial. Una de sus funciones principales será facilitar la integración de diferentes activos relevantes de IA en términos de algoritmos, marcos de software, herramientas de desarrollo y conjuntos de datos, en el desarrollo de nuevas soluciones inteligentes.
CAPRI Industrial Analytics Platform and Data Space - ASPHALT USE CASE
Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022
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...
CAPRI Industrial IoT Platform and Data Space - ASPHALT USE CASE
Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022
IFAC Proceedings Volumes, 2002
This paper describes a real application of fault diagnosis based on statistical signal processing... more This paper describes a real application of fault diagnosis based on statistical signal processing. The system monitories several state variables of the cutting process of a multi-tooth machine tool. However, the feed drive current has been chosen to detect and diagnose the most frequent faults. Experimental data have allowed to define statistical behaviour of the variables for non-fault conditions, tool wear and breakage. The goal of the system is to optimize the lifetime of each tool, while ensuring dimensional tolerance in the product. The machine tool that has been monitored, is a complex machine with five tool-holders and more than 250 inserts. This machine tool is an important element in the production line of crankshafts for an automobile industry.
CAPRI Smart knowledge and semantic data models - ASPHALT USE CASE
Article A Virtual Sensor for Online Fault Detection of Multitooth-Tools
sensors
European Big Data Value Association Position Paper on the Smart Manufacturing Industry
Enterprise Interoperability, 2018
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
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.
Sensors, 2015
Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as... more Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.
Prognosis of defects for machines working under large variation of speed and load conditions is a... more Prognosis of defects for machines working under large variation of speed and load conditions is a topic still under development. Wind turbines are recent examples of such kind of machines that need reliable diagnosis methods. Vibration analysis can be of very limited use when the speed variation is too high. An effective angular resampling method can be very valuable as the first step of vibration signal processing but it is important to know what are the appropriate variables to be monitored.
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.
Sensors, 2011
The installation of suitable sensors close to the tool tip on milling centres is not possible in ... more The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.
Mechanical Systems and Signal Processing, 2011
This work presents the development of an angular resampling algorithm for applying in conditions ... more This work presents the development of an angular resampling algorithm for applying in conditions of high speed variability, as occurs in wind turbines, and the results obtained when applied to simulated signals, bearings diagnostic test-beds and wind turbines. The results improve the accuracy of similar resampling algorithms offered by the consulted bibliography. This algorithm is part of the wind turbine diagnostic system developed by the authors.
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).
CAPRI Industrial IoT Platform and Data Space - STEEL USE CASE
Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022
La investigación desarrollada en este artículo ha sido financiada por el Fondo Europeo de Desarro... more La investigación desarrollada en este artículo ha sido financiada por el Fondo Europeo de Desarrollo Regional FEDER a través del proyecto DISRUPTIVE (Dinamización de los Digital Innovation Hubs dentro de la región PocTep para el impulso de las TIC disruptivas y de última generación a través de la cooperación en la región transfronteriza) del Programa Interreg V-A España-Portugal (POCTEP) 2014-2020 (0677_DISRUPTIVE_2_E). Las opiniones son de exclusiva responsabilidad de los autores que las emiten.
Cognitive Solutions in Process Industry: H2020 CAPRI Project
CAPRI Smart decision support - ASPHALT USE CASE
Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022
Resumen: El proyecto AI REGIO «Regiones y DIHs para la transformacion digital impulsada por la IA... more Resumen: El proyecto AI REGIO «Regiones y DIHs para la transformacion digital impulsada por la IA de las PYMEs manufactureras europeas» tiene como objetivo principal apoyar la creación y el crecimiento sostenible de los Centros de Innovación Digital (DIH) centrados en Inteligencia Artificial (IA), para fomentar la integración de las innovaciones digitales en los procesos de transformación de las PYMEs manufactureras en Europa. El presente artículo presenta el estado actual de desarrollo de una de las plataforma abiertas que se estan desarrollando dentro del proyecto, a la que denominamos AI4Manufacturing Toolkit, un kit de herramientas y técnicas de analisis en la que los conjuntos de datos, preparados y depurados, pueden ser explotados utilizando técnicas de inteligencia artificial. Una de sus funciones principales será facilitar la integración de diferentes activos relevantes de IA en términos de algoritmos, marcos de software, herramientas de desarrollo y conjuntos de datos, en el desarrollo de nuevas soluciones inteligentes.
CAPRI Industrial Analytics Platform and Data Space - ASPHALT USE CASE
Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022
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...
CAPRI Industrial IoT Platform and Data Space - ASPHALT USE CASE
Zenodo (CERN European Organization for Nuclear Research), Apr 12, 2022
IFAC Proceedings Volumes, 2002
This paper describes a real application of fault diagnosis based on statistical signal processing... more This paper describes a real application of fault diagnosis based on statistical signal processing. The system monitories several state variables of the cutting process of a multi-tooth machine tool. However, the feed drive current has been chosen to detect and diagnose the most frequent faults. Experimental data have allowed to define statistical behaviour of the variables for non-fault conditions, tool wear and breakage. The goal of the system is to optimize the lifetime of each tool, while ensuring dimensional tolerance in the product. The machine tool that has been monitored, is a complex machine with five tool-holders and more than 250 inserts. This machine tool is an important element in the production line of crankshafts for an automobile industry.
CAPRI Smart knowledge and semantic data models - ASPHALT USE CASE
Article A Virtual Sensor for Online Fault Detection of Multitooth-Tools
sensors
European Big Data Value Association Position Paper on the Smart Manufacturing Industry
Enterprise Interoperability, 2018
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
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.
Sensors, 2015
Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as... more Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.
Prognosis of defects for machines working under large variation of speed and load conditions is a... more Prognosis of defects for machines working under large variation of speed and load conditions is a topic still under development. Wind turbines are recent examples of such kind of machines that need reliable diagnosis methods. Vibration analysis can be of very limited use when the speed variation is too high. An effective angular resampling method can be very valuable as the first step of vibration signal processing but it is important to know what are the appropriate variables to be monitored.
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.
Sensors, 2011
The installation of suitable sensors close to the tool tip on milling centres is not possible in ... more The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.
Mechanical Systems and Signal Processing, 2011
This work presents the development of an angular resampling algorithm for applying in conditions ... more This work presents the development of an angular resampling algorithm for applying in conditions of high speed variability, as occurs in wind turbines, and the results obtained when applied to simulated signals, bearings diagnostic test-beds and wind turbines. The results improve the accuracy of similar resampling algorithms offered by the consulted bibliography. This algorithm is part of the wind turbine diagnostic system developed by the authors.
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).