Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM
Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from beari...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/15/5170 |
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| author | José Alberto Hernández-Muriel Jhon Bryan Bermeo-Ulloa Mauricio Holguin-Londoño Andrés Marino Álvarez-Meza Álvaro Angel Orozco-Gutiérrez |
| author_facet | José Alberto Hernández-Muriel Jhon Bryan Bermeo-Ulloa Mauricio Holguin-Londoño Andrés Marino Álvarez-Meza Álvaro Angel Orozco-Gutiérrez |
| author_sort | José Alberto Hernández-Muriel |
| collection | DOAJ |
| description | Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy. |
| first_indexed | 2024-03-10T18:10:06Z |
| format | Article |
| id | doaj.art-7385826a0bf249aba2750ffc6f5d5c3b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:10:06Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7385826a0bf249aba2750ffc6f5d5c3b2023-11-20T08:09:20ZengMDPI AGApplied Sciences2076-34172020-07-011015517010.3390/app10155170Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMMJosé Alberto Hernández-Muriel0Jhon Bryan Bermeo-Ulloa1Mauricio Holguin-Londoño2Andrés Marino Álvarez-Meza3Álvaro Angel Orozco-Gutiérrez4Automatics Research Group, Engineering Faculty, Universidad Tecnológica de Pereira, Pereira 660001, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia sede Manizales, Manizales 170001, ColombiaAutomatics Research Group, Engineering Faculty, Universidad Tecnológica de Pereira, Pereira 660001, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia sede Manizales, Manizales 170001, ColombiaAutomatics Research Group, Engineering Faculty, Universidad Tecnológica de Pereira, Pereira 660001, ColombiaNowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy.https://www.mdpi.com/2076-3417/10/15/5170bearing faultsvibration signalsmulti-domain featuresrelevance analysisHidden Markov Models |
| spellingShingle | José Alberto Hernández-Muriel Jhon Bryan Bermeo-Ulloa Mauricio Holguin-Londoño Andrés Marino Álvarez-Meza Álvaro Angel Orozco-Gutiérrez Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM Applied Sciences bearing faults vibration signals multi-domain features relevance analysis Hidden Markov Models |
| title | Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM |
| title_full | Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM |
| title_fullStr | Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM |
| title_full_unstemmed | Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM |
| title_short | Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM |
| title_sort | bearing health monitoring using relief f based feature relevance analysis and hmm |
| topic | bearing faults vibration signals multi-domain features relevance analysis Hidden Markov Models |
| url | https://www.mdpi.com/2076-3417/10/15/5170 |
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