Search results

Feature extraction is a key issue to machine condition monitoring and fault diagnosis. The features must contain the necessary discriminative information for the fault classifier to have any chance of accurate classification. This paper presents a study that uses principal component analysis to reduce dimensionality of the feature space and to get an optimal subspace for machine fault classification. Industrial gearbox vibration signals measured from different operating conditions are analyzed using the above method. The experimental results indicate that the method extracts diagnostic information effectively for gear fault classification and has a good potential for application in practice.

In the high-speed trains (HSTs) production process, assembly sequence planning (ASP) problems is an extremely core issue. ASP problems influence the economic cost, amount of workers and the working time in the assembly process, seriously. In the design process of HSTs, the assembly sequence is usually given by experience, and the correctness of the assembly sequence is difficult to guarantee by experience and low effectiveness. The ASP based on improved discrete particle swarm optimization (IDPSO) algorithm was proposed to address these issues.

In view of the local convergence problem with basic DPSO in ASP, this paper presents an IDPSO, in which a chosen strategy of global optimal particle is introduced in, to solve the ASP problems in the assembly process of HSTs operation panel. The geometric feasibility, the assembly stability, and the number of assembly orientation changes of the assembly are chosen to be the optimization objective. Furthermore, the influences of the population size, the weight coefficient, and the learning factors to the stability and efficiency of IDPSO algorithm were discussed.

The results show that the IDPSO algorithm can obtain the global optimum efficiently, which is proved to be a more useful method for solving ASP problems than basic DPSO. The IDPSO approach could reduce the working time and economic cost of ASP problems in HSTs significantly.

The method may save the economic cost, reduce the amount of workers and save the time in the assembly process of HSTs. And also may change the method of ASP in design and manufacturing process, and make the production process in HSTs more efficiently.

A chosen strategy of global optimal particle is presented, which can overcome the local convergence problem with basic DPSO for solving ASP problems.

To solve the problems of short battery life and low transportation safety of logistics drones, this paper aims to propose a design of logistics unmanned aerial vehicles (UAV) wing with a composite ducted rotor, which combines fixed wing and rotary-wing.

This UAV adopts tiltable ducted rotor combined with fixed wing, which has the characteristics of fast flight speed, large carrying capacity and long endurance. At the same time, it has the hovering and vertical take-off and landing capabilities of the rotary-wing UAV. In addition, aerodynamic simulation analysis of the composite model with a fixed wing and a ducted rotor was carried out, and the aerodynamic influence of the composite model on the UAV was analyzed under different speeds, fixed wing angles of attack and ducted rotor speeds.

The results were as follows: when the speed of the ducted rotor is 2,500 rpm, CL and K both reach maximum values. But when the speed exceeds 3,000 rpm, the lift will decrease; when the angle of attack of the fixed wing is 10° and the rotational speed of the ducted rotor is about 3,000 rpm, the aerodynamic characteristics of the wing are better.

The novelty of this work comes from a composite wing design of a fixed wing combined with a tiltable ducted rotor applied to the logistics UAVs, and the aerodynamic characteristics of the design wing are analyzed.