Abstract
Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life (RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network (LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure. In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.
Highlights
Rotating machinery has been widely used in electric power, machinery, aviation, metallurgy, and some military industries
In [27], a deep learning model based on a one-dimensional convolutional neural network (CNN) and multi-layer long short-time memory network (LSTM) network with attention mechanism was presented to predict the remaining useful life (RUL) of rotatory machine by extracting the useful features form the original signal
The E-LSTM algorithm consists of an elastic net and LSTM, taking temporal-spatial correlation into consideration to deal with bearing degradation through the LSTM which is made up of a large number of memory units
Summary
Rotating machinery has been widely used in electric power, machinery, aviation, metallurgy, and some military industries. To deal with degraded raw data, the back-propagation neural network and weight application to failure times (WAFT) prediction technique are used to establish the rolling bearing prediction model. In [27], a deep learning model based on a one-dimensional convolutional neural network (CNN) and multi-layer LSTM network with attention mechanism was presented to predict the RUL of rotatory machine by extracting the useful features form the original signal. 3) Deep learning methods, such as LSTM, still have the problem of over fitting and may fall into a local minimum, leading to failure of RUL prediction For these reasons, a novel LSTM method called E-LSTM to forecast the RUL of rolling bearings is proposed in this paper. 2) To effectively represent the nonlinear and non-stationary characteristics of the rolling bearing fault data, based on the proposed E-LSTM model, the rolling bearings RUL forecasting algorithm is developed
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