Genetic Algorithm and its Applications to Mechanical Engineering: A Review

Comparative study of surrogate approaches while optimizing computationally expensive reaction networks

Multi-objective micro-geometry optimization of gear tooth supported by response surface methodology

Load sharing analysis and reliability prediction for planetary gear train of helicopter

Dynamic reliability analysis for planetary gear system in shearer mechanisms

Global optimization of reliability design for large ball mill gear transmission based on the Kriging model and genetic algorithm

Hinge-type loader dynamic center-of-gravity position detection system and hinge-type loader dynamic center-of-gravity position detection method

The utility model relates to a duct piece intelligent assembling system of a shield tunnelling machine, belonging to the technical field of mechanical engineering. A duct piece conveying and supplying module is arranged at the rear end of a duct piece positioning and installing module, a duct piece conveying trolley in the duct piece conveying and supplying module loads duct pieces, and arranged in an assembling, grasping and lifting range of the duct piece positioning and installing module in a moving way, a duct piece position identifying and analyzing module is fixedly connected on a lifting cylinder in the duct piece positioning and installing module, a three-dimensional visual system in the duct piece position identifying and analyzing module is fixed by aligning with duct pieces to be load and loaded duct pieces, and a neural network control system is connected with the lifting cylinder, a translation cylinder, a rotary motor, a deflection cylinder, a derricking cylinder and a rolling cylinder in the duct piece positioning and installing module. The duct piece intelligent assembling system can realize automatic duct piece conveying, duct piece position intelligent identification and automatic duct piece assembly of the shield tunnelling machine, and the duct piece assembly is safe, accurate and reliable, and high in efficiency and quality, thus effectively reducing accidents, and ensuring life safety of operation staff.

Duct piece intelligent assembling system of shield tunnelling machine

It is difficult to extract effective weak signals from complex noise environment in many engineering applications. To address the problem, an improved unscented Kalman filter (UKF) algorithm based on a radial basis function-neural network (RBF-NN) is proposed in this article. This algorithm achieves effective extraction of weak signals under the condition of low signal-to-noise ratio (SNR). It uses RBF-NN to establish a distributed system of state and observation models to replace the traditional UKF modeling method. Then, the RBF-NN is applied to build a constant coefficient model to modify the UKF filtering deviation (DE) value, and thus, the accuracy of the algorithm is improved. Finally, simulation experiments are conducted at different SNRs of surface nuclear magnetic resonance (SNMR) signals. Results show that effective weak signals can still be obtained when SNR = −10 dB, verifying the effectiveness of the algorithm.

An Improved UKF Algorithm for Extracting Weak Signals Based on RBF Neural Network

The utility model discloses an articulated loader dynamic barycenter position monitoring system. The articulated loader dynamic barycenter position monitoring system mainly comprises a working load testing module I, a working device attitude testing module II, a front vehicle body and back vehicle body relative position testing module III, a rear axle position testing module IV, a fuel balance testing module V, a driver weight testing module VI, a data processing module VII, and a radial type loading machine VIII. The working load testing module I, the working device attitude testing module II, the front vehicle body and back vehicle body relative position testing module III, the rear axle position testing module IV, the fuel balance testing module V, and the driver weight testing module VI are connected with the data processing module VII through cables. The data processing module VII is fixed in a cab d of the radial type loading machine VIII. The articulated loader dynamic barycenter position monitoring system overcomes the defect that the traditional technology only can detect static barycenter positions, is suitable for a variety of models, and possesses good universality.

Guanyu Zhang

Papers

Global optimization of reliability design for large ball mill gear transmission based on the Kriging model and genetic algorithm

Hinge-type loader dynamic center-of-gravity position detection system and hinge-type loader dynamic center-of-gravity position detection method

Duct piece intelligent assembling system of shield tunnelling machine

An Improved UKF Algorithm for Extracting Weak Signals Based on RBF Neural Network

Articulated loader dynamic barycenter position monitoring system