The full-scale static testing of wind turbine blades is an effective means to verify the accuracy and rationality of the blade design, and it is an indispensable part in the blade certification process. In the full-scale static experiments, the strain of the wind turbine blade is related to the applied loads, loading positions, stiffness, deflection, and other factors. At present, researches focus on the analysis of blade failure causes, blade load-bearing capacity, and parameter measurement methods in addition to the correlation analysis between the strain and the applied loads primarily. However, they neglect the loading positions and blade displacements. The correlation among the strain and applied loads, loading positions, displacements, etc. is nonlinear; besides that, the number of design variables is numerous, and thus the calculation and prediction of the blade strain are quite complicated and difficult using traditional numerical methods. Moreover, in full-scale static testing, the number of measuring points and strain gauges are limited, so the test data have insufficient significance to the calibration of the blade design. This paper has performed a study on the new strain prediction method by introducing intelligent algorithms. Back propagation neural network (BPNN) improved by Particle Swarm Optimization (PSO) has significant advantages in dealing with non-linear fitting and multi-input parameters. Models based on BPNN improved by PSO (PSO-BPNN) have better robustness and accuracy. Based on the advantages of the neural network in dealing with complex problems, a strain-predictive PSO-BPNN model for full-scale static experiment of a certain wind turbine blade was established. In addition, the strain values for the unmeasured points were predicted. The accuracy of the PSO-BPNN prediction model was verified by comparing with the BPNN model and the simulation test. Both the applicability and usability of strain-predictive neural network models were verified by comparing the prediction results with simulation outcomes. The comparison results show that PSO-BPNN can be utilized to predict the strain of unmeasured points of wind turbine blades during static testing, and this provides more data for characteristic structural parameters calculation.

This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.

In the process of machining, the operator’s incorrect operation using the control human-computer interaction interface may lead to a disastrous effect on the whole production chain such as task failure and system failure. The eight types of using errors about human-computer interaction interface system (misperception, memory lapse, carelessness, improper decision-making, improper operation, improper interface layout design, improper icon and text display design, and improper prompt feedback) are proposed based on the cognition from users. According to the eight types of using error, the human reliability and safety evaluation method is proposed. Considering the variability objective and uncertainty subjective factors in the human-machine interaction process, the human reliability evaluation method is proposed based on objective and subjective comprehensive weighting under ergonomic principles. Firstly, based on the investigation of relevant machine tool interface design, the comprehensive evaluation index system for man-machine reliability of complex interface design is established. Secondly, an objective and subjective comprehensive weight assignment method as the composition of Theilʼs entropy weight method combined with TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and improved AHP (analytic hierarchy process) method combined with fuzzy mathematics is proposed. It can not only effectively avoid the impact of excessive subjective factors on empowerment but also fully excavate the real information in the survey data and improve the objectivity and accuracy of the weight data of evaluation indicators at all levels. Thirdly, taking the interface design of CNC machine tool equipment for example, the best human-computer interaction interface design schemes are chosen according to the human reliability evaluation method based on objective and subjective comprehensive method calculated by Entropy-TOPSIS-AHP. Finally, based on eye movement experiments, the use efficiency and accuracy of the human-computer interaction interface design scheme chosen by the assessment method are better than the other design schemes or original one to verify the assessment method validity.

The mechanical reliability problem of passenger car cockpit facilities layout is increasingly complex and has potential and uncertain risks for human safety while the number of private cars is increasing. A new system of layout design optimization is proposed to solve this problem. First, the optimization sequences of facilities are determined using a hybrid method of multiple-attribute decision-making and entropy. Second, the degree of feeling crowded in the cockpit layout can be adjusted based on customers’ preference. Third, an adapted particle swarm optimization algorithm is proposed to solve the problem of three-dimensional layout optimization in car cockpit human–machine interface according to the ergonomic principles, and the adapted algorithm called smoothing iteration particle swarm optimization is contrasted with those of other common algorithms to demonstrate its advantages. Finally, the optimized layout is analyzed by virtual simulations and compared with the original layout to show the feasibility and effectiveness of the proposed design system. Analysis results indicate that the optimized layout by the new particle swarm optimization can make the operation easier and safer than the original one to enhance ergonomic reliability.