Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM
| Autor: | Andrés Marino Álvarez-Meza, Álvaro Orozco-Gutiérrez, Jhon Bryan Bermeo-Ulloa, José Alberto Hernández-Muriel, Mauricio Holguin-Londono |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Electric motor
Computer science multi-domain features Feature selection 02 engineering and technology computer.software_genre Fault (power engineering) lcsh:Technology law.invention lcsh:Chemistry law 0202 electrical engineering electronic engineering information engineering General Materials Science bearing faults Hidden Markov model lcsh:QH301-705.5 Instrumentation Hidden Markov Models Fluid Flow and Transfer Processes Bearing (mechanical) relevance analysis lcsh:T Process Chemistry and Technology 020208 electrical & electronic engineering General Engineering Mode (statistics) lcsh:QC1-999 Computer Science Applications Vibration lcsh:Biology (General) lcsh:QD1-999 Ranking lcsh:TA1-2040 vibration signals 020201 artificial intelligence & image processing Data mining lcsh:Engineering (General). Civil engineering (General) computer lcsh:Physics |
| Zdroj: | Applied Sciences Volume 10 Issue 15 Applied Sciences, Vol 10, Iss 5170, p 5170 (2020) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10155170 |
| Popis: | Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries&rsquo 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&ndash 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|