Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain
This paper presents an adaptive novelty detection methodology applied to a kinematic chain for the monitoring of faults. The proposed approach has the premise that only information of the healthy operation of the machine is initially available and fault scenarios will eventually develop. This approa...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/2417856 |
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| _version_ | 1849304619792990208 |
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| author | Jesus Adolfo Cariño-Corrales Juan Jose Saucedo-Dorantes Daniel Zurita-Millán Miguel Delgado-Prieto Juan Antonio Ortega-Redondo Roque Alfredo Osornio-Rios Rene de Jesus Romero-Troncoso |
| author_facet | Jesus Adolfo Cariño-Corrales Juan Jose Saucedo-Dorantes Daniel Zurita-Millán Miguel Delgado-Prieto Juan Antonio Ortega-Redondo Roque Alfredo Osornio-Rios Rene de Jesus Romero-Troncoso |
| author_sort | Jesus Adolfo Cariño-Corrales |
| collection | DOAJ |
| description | This paper presents an adaptive novelty detection methodology applied to a kinematic chain for the monitoring of faults. The proposed approach has the premise that only information of the healthy operation of the machine is initially available and fault scenarios will eventually develop. This approach aims to cover some of the challenges presented when condition monitoring is applied under a continuous learning framework. The structure of the method is divided into two recursive stages: first, an offline stage for initialization and retraining of the feature reduction and novelty detection modules and, second, an online monitoring stage to continuously assess the condition of the machine. Contrary to classical static feature reduction approaches, the proposed method reformulates the features by employing first a Laplacian Score ranking and then the Fisher Score ranking for retraining. The proposed methodology is validated experimentally by monitoring the vibration measurements of a kinematic chain driven by an induction motor. Two faults are induced in the motor to validate the method performance to detect anomalies and adapt the feature reduction and novelty detection modules to the new information. The obtained results show the advantages of employing an adaptive approach for novelty detection and feature reduction making the proposed method suitable for industrial machinery diagnosis applications. |
| format | Article |
| id | doaj-art-3b8a8c3aef014ff6b1dece0f5922b77b |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-3b8a8c3aef014ff6b1dece0f5922b77b2025-08-20T03:55:41ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/24178562417856Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic ChainJesus Adolfo Cariño-Corrales0Juan Jose Saucedo-Dorantes1Daniel Zurita-Millán2Miguel Delgado-Prieto3Juan Antonio Ortega-Redondo4Roque Alfredo Osornio-Rios5Rene de Jesus Romero-Troncoso6MCIA Research Center, Department of Electronic Engineering, Technical University of Catalonia (UPC), Rbla. San Nebridi 22, Gaia Research Building, Terrassa, 08222 Barcelona, SpainCA Mecatronica, Facultad de Ingenieria, Campus San Juan del Rio, Universidad Autonoma de Queretaro, Rio Moctezuma 249, Col. San Cayetano, 76807 San Juan del Rio, QRO, MexicoMCIA Research Center, Department of Electronic Engineering, Technical University of Catalonia (UPC), Rbla. San Nebridi 22, Gaia Research Building, Terrassa, 08222 Barcelona, SpainMCIA Research Center, Department of Electronic Engineering, Technical University of Catalonia (UPC), Rbla. San Nebridi 22, Gaia Research Building, Terrassa, 08222 Barcelona, SpainMCIA Research Center, Department of Electronic Engineering, Technical University of Catalonia (UPC), Rbla. San Nebridi 22, Gaia Research Building, Terrassa, 08222 Barcelona, SpainCA Mecatronica, Facultad de Ingenieria, Campus San Juan del Rio, Universidad Autonoma de Queretaro, Rio Moctezuma 249, Col. San Cayetano, 76807 San Juan del Rio, QRO, MexicoCA Telematica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle km 3.5 + 1.8, Palo Blanco, 36885 Salamanca, GTO, MexicoThis paper presents an adaptive novelty detection methodology applied to a kinematic chain for the monitoring of faults. The proposed approach has the premise that only information of the healthy operation of the machine is initially available and fault scenarios will eventually develop. This approach aims to cover some of the challenges presented when condition monitoring is applied under a continuous learning framework. The structure of the method is divided into two recursive stages: first, an offline stage for initialization and retraining of the feature reduction and novelty detection modules and, second, an online monitoring stage to continuously assess the condition of the machine. Contrary to classical static feature reduction approaches, the proposed method reformulates the features by employing first a Laplacian Score ranking and then the Fisher Score ranking for retraining. The proposed methodology is validated experimentally by monitoring the vibration measurements of a kinematic chain driven by an induction motor. Two faults are induced in the motor to validate the method performance to detect anomalies and adapt the feature reduction and novelty detection modules to the new information. The obtained results show the advantages of employing an adaptive approach for novelty detection and feature reduction making the proposed method suitable for industrial machinery diagnosis applications.http://dx.doi.org/10.1155/2016/2417856 |
| spellingShingle | Jesus Adolfo Cariño-Corrales Juan Jose Saucedo-Dorantes Daniel Zurita-Millán Miguel Delgado-Prieto Juan Antonio Ortega-Redondo Roque Alfredo Osornio-Rios Rene de Jesus Romero-Troncoso Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain Shock and Vibration |
| title | Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain |
| title_full | Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain |
| title_fullStr | Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain |
| title_full_unstemmed | Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain |
| title_short | Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain |
| title_sort | vibration based adaptive novelty detection method for monitoring faults in a kinematic chain |
| url | http://dx.doi.org/10.1155/2016/2417856 |
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