An AI-Driven Approach to Wind Turbine Bearing Fault Diagnosis from Acoustic Signals

This study aimed to develop a deep learning model for the classification of bearing faults in wind turbine generators from acoustic signals. A convolutional LSTM model was successfully constructed and trained by using audio data from five predefined fault types for both training and validation. To create the dataset, raw audio signal data was collected and processed in frames to capture time and frequency domain information. The model exhibited outstanding accuracy on training samples and demonstrated excellent generalization ability during validation, indicating its proficiency of generalization capability. On the test samples, the model achieved remarkable classification performance, with an overall accuracy exceeding 99.5%, and a false positive rate of less than 1% for normal status. The findings of this study provide essential support for the diagnosis and maintenance of bearing faults in wind turbine generators, with the potential to enhance the reliability and efficiency of wind power generation

Metal organic frameworks based materials for heterogeneous photocatalysis

The increase in environmental pollution due to the excessive use of fossil fuels has prompted the development of alternative and sustainable energy sources. As an abundant and sustainable energy, solar energy represents the most attractive and promising clean energy source for replacing fossil fuels. Metal organic frameworks (MOFs) are easily constructed and can be tailored towards favorable photocatalytic properties in pollution degradation, organic transformations, CO2 reduction and water splitting. In this review, we first summarize the different roles of MOF materials in the photoredox chemical systems. Then, the typical applications of MOF materials in heterogeneous photocatalysis are discussed in detail. Finally, the challenges and opportunities in this promising field are evaluated

Luminescent Lanthanide MOFs: A Unique Platform for Chemical Sensing

In recent years, lanthanide metal-organic frameworks (LnMOFs) have developed to be an interesting subclass of MOFs. The combination of the characteristic luminescent properties of Ln ions with the intriguing topological structures of MOFs opens up promising possibilities for the design of LnMOF-based chemical sensors. In this review, we present the most recent developments of LnMOFs as chemical sensors by briefly introducing the general luminescence features of LnMOFs, followed by a comprehensive investigation of the applications of LnMOF sensors for cations, anions, small molecules, nitroaromatic explosives, gases, vapors, pH, and temperature, as well as biomolecules

(E)-(2,4-Dichloro­phen­yl)[2-hydr­oxy-6-(methoxy­imino)cyclo­hex-1-en­yl]methanone

The title compound, C14H13Cl2NO3, was obtained as the product of an attempted synthesis of herbicidally active compounds containing oxime ether and cyclo­hexenone groups. In the crystal structure, the mol­ecule adopts an endocyclic enol tautomeric form and the cyclo­hexene ring adopts a distorted envelope form. The oxime ether group has an E configuration, with the meth­oxy group anti to the ortho-chloro substitutent. Intra­molecular O—H⋯O and inter­molecular C—H⋯O hydrogen bonds are found in the crystal structure

Rational design and synthesis of covalent triazine frameworks based on novel N-heteroaromatic building blocks for efficient CO2 and H2 capture and storage

Covalent Triazine Frameworks (CTFs), a nitrogen-rich subclass of Porous Organic Polymers (POPs), show large potential in applications including gas adsorption /separation and heterogeneous catalysis due to their distinctive large surface area, low skeleton density, good thermal and chemical stability combined with their rational tunability.1 Herein, we reported on a set of nitrogen-rich CTFs prepared by trimerization of 4,4',4'',4'''-(1,4-phenylenebis(pyridine-4,2,6-triyl))tetrabenzonitrile under ionothermal conditions. The influence of several parameters such as ZnCl2/monomer ratio and reaction temperature on the structure and porosity of the resulting frameworks was systematically examined. After a thorough characterization, their performance in CO2 and H2 adsorption as well as their selectivity of CO2 over N2 was assessed. Notably, the CTF obtained using 20 molar equiv. of ZnCl2 at a reaction temperature of 400 ºC exhibits an excellent CO2 adsorption capacity (3.48 mmol/g at 273 K and 1 bar) as well as a significant high H2 uptake (1.5 wt% at 77 K and 1 bar). These values are among the highest measured under identical conditions to date. In addition, the obtained CTFs also present a relatively high CO2/N2 selectivity (up to 36 at 298 K) making them promising adsorbents for gas sorption and separation

Fault diagnosis method of polymerization kettle equipment based on rough sets and BP neural network

Polyvinyl chloride (PVC) polymerizing production process is a typical complex controlled object, with complexity features, such as nonlinear, multivariable, strong coupling, and large time-delay. Aiming at the real-time fault diagnosis and optimized monitoring requirements of the large-scale key polymerization equipment of PVC production process, a real-time fault diagnosis strategy is proposed based on rough sets theory with the improved discernibility matrix and BP neural networks. The improved discernibility matrix is adopted to reduct the attributes of rough sets in order to decrease the input dimensionality of fault characteristics effectively. Levenberg-Marquardt BP neural network is trained to diagnose the polymerize faults according to the reducted decision table, which realizes the nonlinear mapping from fault symptom set to polymerize fault set. Simulation experiments are carried out combining with the industry history datum to show the effectiveness of the proposed rough set neural networks fault diagnosis method. The proposed strategy greatly increased the accuracy rate and efficiency of the polymerization fault diagnosis system

(S)-Benzyl 2-amino-3-(4-hydroxy­phen­yl)propanoate

The title compound, C16H17NO3, adopts a folded conformation in the crystal structure. The crystal packing is stabilized by inter­molecular O—H⋯O and N—H⋯O hydrogen-bonding inter­actions. The absolute configuration was assigned assuming that the absolute configuration of the starting material l-tyrosine was retained during the synthesis