TY - JOUR
T1 - Roller bearing degradation assessment based on a deep MLP convolution neural network considering outlier regions
AU - Zhang, Dingcheng
AU - Stewart, Edward
AU - Ye, Jiaqi
AU - Entezami, Mani
AU - Roberts, Clive
PY - 2019/7/18
Y1 - 2019/7/18
N2 - Roller bearings are one of the most safety-critical components in many machines. Predicting the vibration based remaining useful life (RUL) of roller bearings allows operators to make informed maintenance decisions and to guarantee reliability and safety. The health indices (HIs) for degradation assessment are constructed by extracting feature information from the collected data, which significantly influences the prognosis result. Conventional HI construction methods rely heavily on expert knowledge and also have limited capacity for learning health information from the raw data from roller bearings. Furthermore, outlier regions often occur in HIs developed by those methods, and these can easily result in false alarms. To address these problems, a novel HI construction method based on a deep multilayer perceptron (MLP) convolution neural network (DMLPCNN) model, which also considers outlier regions, is proposed in this paper. In the proposed model, a 1-D MLP convolution (Mlpconv) block, consisting of a convolution layer and a micro network, is applied to learn features directly from vibrational data. The learned features are then mapped into an HI using a global average pooling layer and a logistic regression layer. Finally, an outlier region correction method, based on sliding thresholds, is proposed to detect and remove outliers in the HI. The outlier region correction method is able to enhance the interpretability of the constructed HI. The effectiveness of the proposed method is verified using whole-life datasets of 17 bearings. The experimental results demonstrate that the proposed method outperforms conventional methods.
AB - Roller bearings are one of the most safety-critical components in many machines. Predicting the vibration based remaining useful life (RUL) of roller bearings allows operators to make informed maintenance decisions and to guarantee reliability and safety. The health indices (HIs) for degradation assessment are constructed by extracting feature information from the collected data, which significantly influences the prognosis result. Conventional HI construction methods rely heavily on expert knowledge and also have limited capacity for learning health information from the raw data from roller bearings. Furthermore, outlier regions often occur in HIs developed by those methods, and these can easily result in false alarms. To address these problems, a novel HI construction method based on a deep multilayer perceptron (MLP) convolution neural network (DMLPCNN) model, which also considers outlier regions, is proposed in this paper. In the proposed model, a 1-D MLP convolution (Mlpconv) block, consisting of a convolution layer and a micro network, is applied to learn features directly from vibrational data. The learned features are then mapped into an HI using a global average pooling layer and a logistic regression layer. Finally, an outlier region correction method, based on sliding thresholds, is proposed to detect and remove outliers in the HI. The outlier region correction method is able to enhance the interpretability of the constructed HI. The effectiveness of the proposed method is verified using whole-life datasets of 17 bearings. The experimental results demonstrate that the proposed method outperforms conventional methods.
KW - Roller bearings
KW - RUL prediction
KW - degradation assessment
KW - deep MLP convolution neural network
KW - outlier region correction
U2 - 10.1109/TIM.2019.2929669
DO - 10.1109/TIM.2019.2929669
M3 - Article
SN - 0018-9456
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
ER -