Atomically dispersed low-valent Au boosts photocatalytic hydroxyl radical production.

Providing affordable, safe drinking water and universal sanitation poses a grand societal challenge. Here we developed atomically dispersed Au on potassium-incorporated polymeric carbon nitride material that could simultaneously boost photocatalytic generation of ·OH and H2O2 with an apparent quantum efficiency over 85% at 420 nm. Potassium introduction into the poly(heptazine imide) matrix formed strong K-N bonds and rendered Au with an oxidation number close to 0. Extensive experimental characterization and computational simulations revealed that the low-valent Au altered the materials' band structure to trap highly localized holes produced under photoexcitation. These highly localized holes could boost the 1e- water oxidation reaction to form highly oxidative ·OH and simultaneously dissociate the hydrogen atom in H2O, which greatly promoted the reduction of oxygen to H2O2. The photogenerated ·OH led to an efficiency enhancement for visible-light-response superhydrophilicity. Furthermore, photo-illumination in an onsite fixed-bed reactor could disinfect water at a rate of 66 L H2O m-2 per day.

A Novel Fault Diagnosis Method for Denoising Autoencoder Assisted by Digital Twin.

Digital twin (DT) is an important method to realize intelligent manufacturing. Traditional data-based fault diagnosis methods such as fractional-order fault feature extraction methods require sufficient data to train a diagnosis model, which is unrealistic in a dynamically changing production process. The ultrahigh-fidelity DT model can generate fault state data similar to the actual system, providing a new paradigm for fault diagnosis. This paper proposes a novel digital twin-assisted fault diagnosis method for denoising autoencoder. First, in order to solve the problem of limited or unavailable fault state data for machines in dynamically variable production scenarios, a DT model of the machine is established. The model can simulate a dynamically changing production process, thereby generating data for different failure states. Second, a novel denoising autoencoder (NDAE) with Mish as the activation function is proposed and trained using the source domain data generated by DT. Finally, in order to verify the effectiveness and feasibility of the proposed method, the method is applied to a fault diagnosis example of a triplex pump, and the results show that the method can realize intelligent fault diagnosis when the fault state data are limited or unavailable.

Application of an Improved Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Gearbox Composite Fault Diagnosis.

The fault feature extraction of gearbox is difficult to achieve under complex working conditions, and this paper presents a hybrid fault diagnosis method for gearbox based on the combining product function (CPF) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) methods. First, ensemble local mean decomposition (ELMD) is utilized to reduce the noise in original signal, and get a series of product functions (PFs), through the correlation coefficient method to remove false components and residual components. Then, multi-point kurtosis of the definition is achieved by calculating the multi-point kurtosis spectrum of each layer PF, and the fault feature period is extracted and the PFs without periodic impact are removed. After that, in order to maintain the integrity of the original signal, the PFs with the same period are recombined by the combined product function method. Finally, the different cycle interval is configured, reduce the noise through MOMEDA on the combined signal, to further extract the fault feature. The method is applied to the feature extraction of gear box composite fault to verify the feasibility of this method.

A New Compound Fault Feature Extraction Method Based on Multipoint Kurtosis and Variational Mode Decomposition.

Due to the weak entropy of the vibration signal in the strong noise environment, it is very difficult to extract compound fault features. EMD (Empirical Mode Decomposition), EEMD (Ensemble Empirical Mode Decomposition) and LMD (Local Mean Decomposition) are widely used in compound fault feature extraction. Although they can decompose different characteristic components into each IMF (Intrinsic Mode Function), there is still serious mode mixing because of the noise. VMD (Variational Mode Decomposition) is a rigorous mathematical theory that can alleviate the mode mixing. Each characteristic component of VMD contains a unique center frequency but it is a parametric decomposition method. An improper value of K will lead to over-decomposition or under-decomposition. So, the number of decomposition levels of VMD needs an adaptive determination. The commonly used adaptive methods are particle swarm optimization and ant colony algorithm but they consume a lot of computing time. This paper proposes a compound fault feature extraction method based on Multipoint Kurtosis (MKurt)-VMD. Firstly, MED (Minimum Entropy Deconvolution) denoises the vibration signal in the strong noise environment. Secondly, multipoint kurtosis extracts the periodic multiple faults and a multi-periodic vector is further constructed to determine the number of impulse periods which determine the K value of VMD. Thirdly, the noise-reduced signal is processed by VMD and the fault features are further determined by FFT. Finally, the proposed compound fault feature extraction method can alleviate the mode mixing in comparison with EEMD. The validity of this method is further confirmed by processing the measured signal and extracting the compound fault features such as the gear spalling and the roller fault, their fault periods are 22.4 and 111.2 respectively and the corresponding frequencies are 360 Hz and 72 Hz, respectively.