With the rising importance of energy efficiency, zero leakage power and instant-on capability are highly desired features in energy harvesting sensors, as well as “normally off” high performance processors. However, intermittent power in such systems requires nonvolatile memory (NVM) to hold intermediate data and avoid rollbacks. Previous work has adopted FeRAM and STT-MRAM to achieve zero-standby power and fast-restore nonvolatile processors (NVPs) [1-3]. Previous NVPs, however, suffer from several drawbacks: 1) Various power interrupt periods are not considered; 2) the 2-macro memory architecture slows access speed; 3) worst-case store/restore operations are always performed. We present a 65nm fully-CMOS-logic-compatible ReRAM-based NVP achieving time/space-adaptive data retention. A 1-macro nvSRAM with self-write-termination (SWT) is integrated to boost clock frequency and reduce store energy. The adaptive retention and SWT strategy relieve the ReRAM write endurance challenge (106-1012), making it sufficient for most applications. The NVP operates at 100MHz with 20ns/0.45nJ restore time (TRESTORE)/energy (ERESTORE), realizing 6× reduction in TRESTORE, >6000× reduction in ERESTORE and 4× higher clock frequency compared with existing designs.

Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN‐AGRU) is proposed. This method first inputs multiple time scales rolling bearing vibration signals into the convolutional neural network to train the model through multiscale data processing and then adds the gated recurrent unit network with an attention mechanism to make the model predictive. Finally, the reconstruction error between the actual value and the predicted value is used to detect the early fault. The training data of this method is only normal data. The early fault detection in the operating condition monitoring and performance degradation assessment of the rolling bearing is effectively solved. It uses a multiscale data processing method to make the features extracted by CNN more robust and uses a GRU network with an attention mechanism to make the predictive ability of this method not affected by the length of the data. Experimental results show that the MCNN‐AGRU rolling bearing early fault diagnosis method proposed in this paper can effectively detect the early fault of the rolling bearing and can effectively identify the type of rolling bearing fault.

Due to the relatively weak early fault characteristics of rolling bearings, the difficulty of early fault detection increases. For unsolving this problem, an incipient fault detection method based on deep empirical mode decomposition and principal component analysis (Deep EMD-PCA) is proposed. In this method, multiple data processing layers are created to extract weak incipient fault features, and EMD is used to decompose the vibration signal. This method establishes an accurate data mode, which can improve the incipient fault detection capability. It overcomes the difficulties of incipient fault detection, in which weak fault features can be extracted from the background of strong noise. From a theoretical point of view, this paper proves that the Deep EMD-PCA method can retain more variance information and has a good early fault detection ability. The experiment results indicate that the detection rate of Deep EMD-PCA is about 85%, and the failure detection delay time is almost zero. The incipient faults of rolling element bearings can be detected accurately and timely by Deep EMD-PCA. The method effectively improves the accuracy and timeliness of fault detection under actual working conditions and has good practical application value.

Increased temperature and surface wear of high-speed and heavy-load gears are inevitable. Thermal deformation and surface wear modify the position of the action line of the tooth surface and thus influence the dynamic characteristics of the gear mesh. In this study, the elastic modulus and tooth profile thermal deformation were calculated when the tooth contact temperature (TCT) increased. A dynamic wear calculation method was used to combine the dynamic mesh force and dynamic wear coefficient caused by the dynamic mesh force obtained in the nonlinear dynamics model with the quasi-static wear model to obtain the cumulative wear depth. The changed elastic modulus, the tooth profile thermal deformation and the wear depth are considered when calculating the mesh stiffness using the energy method. A nonlinear dynamics model was established by considering the effects of TCT and dynamic wear on the internal dynamic excitation of the gear transmission system. The effects of internal excitation variations such as mesh stiffness, STE and backlash on gear dynamics are analyzed, and the study showed their complex effects on gear dynamics. Comparing the bifurcation diagrams with or without considering TCT and dynamic wear reveals that the system enters chaos earlier after considering TCT and dynamic wear.