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.
Highlights
Rotating systems in general, and bearings, are essential elements in many mechanical systems such as vehicles, aircrafts, and industrial plants [1]
We present experiments where we compare Random Forest classifier performance to other classifiers when draining with samples that include only labels of some of the spall sizes (T = {1, 2.3, 3.6, 5}), and testing with the whole size (T UT = {1.6, 3.0, 4.3})
In the rest of the experiments, we focus on the Random Forest performance
Summary
Bearings, are essential elements in many mechanical systems such as vehicles, aircrafts, and industrial plants [1]. Failure in these systems can cause great economical loss or even risk of human life. A rolling-element bearing is a key element in rotating machines. This term refers to various forms of bearings that use rolling of balls or rollers to reduce friction to a minimum, while enabling independent movement of two races. The most common failure mode is spalling [6]. We choose to focus on the outer race, but the same process can be implemented for the inner race
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