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Deterioration prognostics and forewarning for bearings using an embedded GRU-SVR approach

The response time for maintenance is extremely important in flow line production. However, it is difficult to estimate the malfunction of industrial bearings before it appears. A novel method combining an embedded gated recurrent unit (GRU) and support vector regression (SVR) is proposed to estimate a potential risk of failure at an early stage. GRU is first used to map data from the high-dimensional space to a lower-dimensional hidden space. Reconstructed data from the hidden state are extracted as features. Two indicators of the features, squared prediction error and principal components, can be fused as a one-dimensional statistic series for real-time monitoring, where the forewarning value is determined. SVR is proposed to estimate the life expectancy. The proposed embedded GRU-SVR approach is evaluated by a lifecycle bearing experiment from a public benchmark dataset. Results show that the present method is excellent in deterioration prognostics and forewarning.