Combinational Framework for Classification of Bearing Faults in Rotating Machines

Abstract

In rotating machines, roller bearings are important and prone to frequent faults. Hence, accurate classification of bearing faults is significant in the maintenance of machines. Toward this, a framework using the combination of signal processing, machine learning, and deep learning algorithms has been proposed in contrast to traditional approaches for the accurate identification of bearing faults. The benefits of each algorithm have been reaped in the proposed framework to overcome challenges met in fault identification. In this, ensemble empirical mode decomposition is applied on bearing vibration signals to reduce nonstationarity and noise. The 12 intrinsic mode function (IMF) signals of 24k length obtained for three bearing conditions at four different speeds constituted feature space of dimension [36*8*24,000]. IMFs that have the highest correlation coefficient with raw vibration signals are selected as features [3*8*24,000], and intelligent algorithms are applied. Application of principal component analysis on selected IMF feature space resulted in extraction of significant feature space retaining temporal characteristics along two major components [3*2*24,000]. Considering the temporal dependence of faults in signals, a stacked long short-term memory (LSTM) deep network is chosen and trained with extracted features to improve fault classification. The performance of this developed framework has been evaluated for different metrics of the stacked LSTM model. The proposed framework also satisfactorily surpassed the performance of the stacked LSTM model trained with raw data, capable of auto-feature learning. The comparative results inclusive of models in relevant literature illustrate the efficacy of developed combinational framework in handling dynamic vibration data for precise classification of bearing faults.