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In order to solve the problem of poor accuracy and low efficiency of traditional rolling bearing life prediction method. A prediction method based on bi-directional stack simple recurrent unit is proposed. Multiple time and frequency features are extracted from the original signal to construct a multi-dimensional data set to enhance the information representation and avoid the insufficient reflection of a single feature on the bearing degradation ability. Relying on the ability of recurrent neural network to process time series data,the SRU model is constructed,the traditional RNN serial operation structure is reconstructed,and the training efficiency is improved. On the basis of single layer SRU,it is stacked in reverse,so that the model can learn two-way information in time dimension,improve the accuracy of prediction. The experimental results show that the prediction accuracy and training efficiency of this method are improved.

The non‐homogeneous gamma process can exhibit a convex average trend with a non‐linear shape parameter, providing significant flexibility in fitting an accelerated degradation process. However, few studies have been conducted on the maintenance modeling of systems undergoing a non‐homogeneous gamma degradation process despite its versatility. Meanwhile, most maintenance studies adopt a periodic inspection policy to reveal the degradation level. However, it is reasonable to inspect less frequently when the degradation level of the system is low and to perform inspections more frequently when the degradation level increases. Therefore, we conduct a study on the inspection/replacement policy of systems undergoing a non‐homogeneous gamma degradation process, where three non‐periodic inspection policies are investigated. We formulate the problem as a SMDP framework and present a value iteration algorithm to obtain the optimal action at each decision epoch that minimizes the long‐term average cost. A comprehensive numerical study is provided to illustrate the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

With the rapid development of industry, oily wastewater has caused serious environmental problems. Highly efficient oil–water separation based on superhydrophobic materials holds promise; however, superhydrophobic surfaces often lose their superhydrophobicity when exposed to hot water. Consequently, oil–water separation under high temperature is rarely explored. Herein, a heat resistant superhydrophobic porous glass membrane is fabricated by the vapor phase growth of a network layer of silicone nanofilaments using economical ethyltrichlorosilane as the only surface modifier. The superhydrophobic porous glass membrane possesses hot water resistance and can be used for highly efficient separation of water from oil under high temperatures, regardless of the water/oil ratio, the composition of the oil, and the temperature. Taking advantage of the superior performance in oil–water separation at high temperatures, we successfully adopted such a superhydrophobic membrane for removing water from condensation reactions such as amidation and esterification reactions. It was demonstrated that both the reaction rate and the conversion can be improved significantly. The idea to use superhydrophobic oil–water separation materials for condensation reactions paves the way for industrial synthesis of amides and esters. [ABSTRACT FROM AUTHOR]

In this study, self-organized criticality theory was applied to analyze the unscheduled downtime (USDT) characteristic of a complex manufacturing system. The long-range correlation of the USDT series was verified through the Hurst exponent value. Then, we developed a model to depict the relationship between the frequency and USDT duration with the extreme value theory. The parameters of the model were estimated with the maximum likelihood method and updated with Bayesian inference when new data was available. Finally, the effectiveness of the proposed method was validated by the USDT data of the bottleneck system from a semiconductor assembly and test factory. [ABSTRACT FROM AUTHOR]