Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

High Piezo-photocatalytic Efficiency of CuS/ZnO Nanowires Using Both Solar and Mechanical Energy for Degrading Organic Dye

Abstract: High piezo-photocatalytic efficiency of degrading organic pollutants has been realized from CuS/ZnO nanowires using both solar and mechanical energy. CuS/ZnO heterostructured nanowire arrays are compactly/vertically aligned on stainless steel mesh by a simple two-step wet-chemical method. The mesh-supported nanocomposites can facilitate an efficient light harvesting due to the large surface area and can also be easily removed from the treated solution. Under both solar and ultrasonic irradiation, CuS/ZnO nanowires can rapidly degrade methylene blue (MB) in aqueous solution, and the recyclability is investigated. In this process, the ultrasonic assistance can greatly enhance the photocatalytic activity. Such a performance can be attributed to the coupling of the built-in electric field of heterostructures and the piezoelectric field of ZnO nanowires. The built-in electric field of the heterostructure can effectively separate the photogenerated electrons/holes and facilitate the carrier transportation. The CuS component can improve the visible light utilization. The piezoelectric field created by ZnO nanowires can further separate the photogenerated electrons/holes through driving them to migrate along opposite directions. The present results demonstrate a new water-pollution solution in green technologies for the environmental remediation at the industrial level.

Control Synthesis of Discrete-Time T–S Fuzzy Systems via a Multi-Instant Homogenous Polynomial Approach

Abstract: This paper deals with the problem of control synthesis of discrete-time Takagi–Sugeno fuzzy systems by employing a novel multiinstant homogenous polynomial approach. A new multiinstant fuzzy control scheme and a new class of fuzzy Lyapunov functions, which are homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions, are proposed for implementing the object of relaxed control synthesis. Then, relaxed stabilization conditions are derived with less conservatism than existing ones. Furthermore, the relaxation quality of obtained stabilization conditions is further ameliorated by developing an efficient slack variable approach, which presents a multipolynomial dependence on the normalized fuzzy weighting functions at the current and past instants of time. Two simulation examples are given to demonstrate the effectiveness and benefits of the results developed in this paper.

Deep residual learning-based fault diagnosis method for rotating machinery

Abstract: Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach.

The strengthening effect of inter-layer cold working and post-deposition heat treatment on the additively manufactured Al–6.3Cu alloy

Abstract: Wire + Arc Additive Manufacture (WAAM) attracts great interest from the aerospace industry for producing components with aluminum alloys, particularly Al–Cu alloy of the 2000 series such as 2219 alloy. However the application is restricted by the low strength properties of the as-deposited WAAM metal. In this study two strengthening methods were investigated – inter-layer cold working and post-deposition heat treatment. Straight wall samples were prepared with 2319 aluminum alloy wire. Inter-layer rolling with loads of 15 kN, 30 kN and 45 kN were employed during deposition. The ultimate tensile strength (UTS) and yield strength (YS) of the inter-layer rolled alloy with 45 kN load can achieve 314 MPa and 244 MPa respectively. The influence of post-deposition T6 heat treatment was investigated on the WAAM alloy with or without rolling. Compared with inter-layer rolling, post-deposition heat treatment can provide much greater enhancement of the strength. After T6 treatment, the UTS and YS of both of the as-deposited and 45 kN rolled alloys exceeded 450 MPa and 305 MPa respectively, which are higher than the properties of the wrought 2219−T6 alloy. The strengthening mechanisms of this additively manufactured Al–6.3Cu alloy were investigated through microstructure analysis.