Abstract
As rolling bearings are the key components in rotating machinery, bearing performance degradation directly affects machine running status. A tendency prognosis for bearing performance degradation is thus required to ensure the stability of operation. This paper proposes a novel strategy for bearing performance degradation trend prognosis, including health indicator construction techniques and a performance degradation trend prediction method. To more accurately represent the degradation trend, the multiscale deep bottleneck health indicator is proposed as a new synthesized health indicator to remove high-frequency detail signals from features, which can reduce possible fluctuations in conventional synthetic health indicators. A suitable method for selecting the statistical characteristics required for fusion is also presented to solve the problem of information redundancy that affects trend representation. In addition, a stacked autoencoder network is used for deep feature extraction of selected statistical features. A bidirectional long short-term memory network prediction model is also proposed for the prediction of degradation trend, which can make full use of historical and future information to improve prediction accuracy. Finally, experiments are carried out to verify the effectiveness of the proposed method.
Highlights
Rolling bearings are one of the most critical components in rotating machinery and their operating state will affect the overall performance of mechanical equipment [1,2,3]
From the perspective of prediction errors, compared with traditional methods, the prediction effect based on multiscale deep bottleneck health indicator (MDBHI) and bidirectional LSTM network (BiLSTM) is better than others. is result indicates that the health indicator (BHI) after removing detailed fluctuation components can successfully reflect the tendency of bearing degradation and will improve the final prediction effect
A method of bearing degradation trend prediction based on MDBHI and BiLSTM was proposed in this paper. e method can solve the problem that a simple feature cannot accurately describe the bearing degradation process
Summary
Rolling bearings are one of the most critical components in rotating machinery and their operating state will affect the overall performance of mechanical equipment [1,2,3]. Aye and Heyns [18] proposed an improved Gaussian process regression (GPR) for the prediction of remaining useful life (RUL) These traditional machine learning techniques cannot automatically learn multiple representations from the original input data and require hand-coded rules or domain knowledge. Malhi et al [22] used a continuous wavelet transform to preprocess the bearing vibration signals and employed recurrent neural networks (RNNs) to further predict the trend of degradation Deutsch and He [23] proposed a deep belief network-feedforward neural network (DBN-FNN) algorithm for RUL prediction, while Zhu et al [24] extracted time-frequency features by wavelet transform and used convolutional neural networks (CNNs) to estimate RUL.
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