Back to Search
Start Over
Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes.
- Source :
- Sensors (14248220); Mar2020, Vol. 20 Issue 5, p1298, 1p
- Publication Year :
- 2020
-
Abstract
- Bearing spall detection and predicting its size are great challenges. Model-based simulation is a well-known traditional approach to physically model the influence of the spall on the bearing. Building a physical model is challenging due to the bearing complexity and the expert knowledge required to build such a model. Obviously, building a partial physical model for some of the spall sizes is easier. In this paper, we propose a machine-learning algorithm, called Probability-Based Forest, that uses a partial physical model. First, the behavior of some of the spall sizes is physically modeled and a simulator based on this model generates scenarios for these spall sizes in different conditions. Then, the machine-learning algorithm trains these scenarios to generate a prediction model of spall sizes even for those that have not been modeled by the physical model. Feature extraction is a key factor in the success of this approach. We extract features using two traditional approaches: statistical and physical, and an additional new approach: Time Series FeatuRe Extraction based on Scalable Hypothesis tests (TSFRESH). Experimental evaluation with well-known physical model shows that our approach achieves high accuracy, even in cases that have not been modeled by the physical model. Also, we show that the TSFRESH feature-extraction approach achieves the highest accuracy. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
FEATURE extraction
ALGORITHMS
PREDICTION models
SIZE
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 20
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 142279873
- Full Text :
- https://doi.org/10.3390/s20051298