Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

Abstract

For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert-Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

“…This method used a raw time-domain signal to extract directly the representative fault features via DBN. By putting the raw vibration signals into a convolutional neural network, Mao et al [13] extract deep features of bearing fault with good representation ability. These works demonstrate the promising performance of deep neural network on fault diagnosis problems in terms of adaptive and automatic feature extraction.…”

Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

“…This section elaborates on deep learning methods [30][31][32][33][34][35][36][37][38][39] for predicting the bearing RUL, whose characteristics are shown in Table 1. PRONOSTIA datasets [40] are first described before the methods are detailed, as all the methods presented in this section apply PRONOTIA datasets for prediction accuracy evaluation.…”

“…Mao et al [33] proposed a bearing RUL estimation method using the concept of the Hilbert-Huang transform (HHT) [43], CNN, and LSTM. First, the HHT is applied to vibration signals to obtain the HHT marginal spectrum as the input of the CNN.…”

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

“…Optimization problems have been one of the most important research topics in recent years. They exist in many domains, such as scheduling [1,2], image processing [3][4][5][6], feature selection [7][8][9] and detection [10], path planning [11,12], feature selection [13], cyber-physical social system [14,15], texture discrimination [16], saliency detection [17], classification [18,19], object extraction [20], shape design [21], big data and large-scale optimization [22,23], multi-objective optimization [24], knapsack problem [25][26][27], fault diagnosis [28][29][30], and test-sheet composition [31]. Metaheuristic algorithms [32], a theoretical tool, are based on nature-inspired ideas, which have been extensively used to solve highly non-linear complex multi-objective optimization problems [33][34][35].…”