Deep Spatiotemporal Convolutional-Neural-Network-Based Remaining Useful Life Estimation of Bearings

The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical systems.Owing to the rapid development of deep learning methods,a multitude of data-driven RUL estimation approaches have been proposed recently.However,the following problems remain in existing methods:1)Most network models use raw data or statistical features as input,which renders it difficult to extract complex fault-related information hidden in signals;2)for current observations,the dependence between current states is emphasized,but their complex dependence on previous states is often disregarded;3)the output of neural networks is directly used as the estimated RUL in most studies,resulting in extremely volatile prediction results that lack robustness.Hence,a novel prognostics approach is proposed based on a time-frequency representation(TFR)subsequence,three-dimensional convolutional neural network(3DCNN),and Gaussian process regression(GPR).The approach primarily comprises two aspects:construction of a health indicator(HI)using the TFR-subsequence-3DCNN model,and RUL estimation based on the GPR model.The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy.Subsequently,the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs.Finally,the RUL of the bearings is estimated using the GPR model,which can also define the probability distribution of the potential function and prediction confidence.Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence-3DCNN-GPR approach.The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.

Investigation Into Vibration Characteristic in Vibrothermography

Vibration characteristic plays an important role in vibrothermography, which directly affects the heating during the test. In this work, involving all the contacts in the vibrothermography, the "double-mass-thiree-spring" model is established to explore the vibration characteristic. The obtained results show that ultrasonic gun vibrates at fundamental frequency (FF), while the specimen vibrates at multi-frequencies including FF, 2FF, 3FF, and 4FF, which is validated through experimental investigation results. Additionally, the model proposed in this work reveals a high order harmonics in the vibrothermography test and makes the specimen conduct the steady vibration, which indicates that the model is closer to the practical equipment and can ensure the heating efficiency induced by vibration of specimen to improve the detection capability.