Multi-skeleton model for top-down design of complex products

Generally, there are two alternative design approaches available to engineers: bottom-up and top-down. Considering the sharp increase in the complexity of most mechanical products, the top-down design approach is more widely adopted in the development of complex products. However, in traditional top-down design process, design parameters are communicated through single-skeleton models, and design units are strongly coupled due to the multi-dimensional complexity of products. Toward this end, a new top-down design approach based on multi-skeleton model is proposed in this article. First, in accordance with different kinds of design parameters, three major skeleton models are defined, including location skeleton model, published skeleton model, and design skeleton model. And the characteristics of multi-skeleton models are also described. Then, the top-down design process based on the multi-skeleton model is explored, especially in the multi-skeleton modeling phase. It is also illustrated in detail that how to realize design parameter transmission and design unit reuse. Subsequently, it elaborates the communicating way and structure optimization of design parameters to support parameters controlled publishing and design units reuse. Finally, a meteorological satellite and a crawler crane design cases are implemented to expound the feasibility and effectiveness of the proposed framework

Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM

Accurately predicting the remaining useful life of wind turbine gearbox bearing online is essential for ensuring the safe operation of the whole machine in the long run. In recent years, quite a few data-driven approaches have been proposed that use the sensor-collected data to deal with this problem, achieving good results. However, their effects are heavily dependent on the massive degradation data due to the nature of data-driven methods. In practice, the complete data collection is expensive and time-consuming, especially for newly built or small-scale wind farms, which brings the problem of using limited data into sharp focus. To this end, in this paper, a novel idea of first using the prior knowledge of an empirical model for data augmentation based on the raw limited samples and then using the deep neural network to learn from the augmented data is proposed. This helps the neural network to safely approach the degradation characteristics, avoiding overfitting. In addition, a new neural network, namely, pre-interaction long short-term memory (PI-LSTM), is designed, which is able to better capture the sequential features of time-series samples, especially in the periods in which the continuous features are interrupted. Finally, a fine-tuning process is conducted using the limited real data for eliminating the introduced knowledge bias. Through a case study based on real sensor data, both the idea and the PI-LSTM are proved to be effective and superior to the state-of-art

Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM

Accurately predicting the remaining useful life of wind turbine gearbox bearing online is essential for ensuring the safe operation of the whole machine in the long run. In recent years, quite a few data-driven approaches have been proposed that use the sensor-collected data to deal with this problem, achieving good results. However, their effects are heavily dependent on the massive degradation data due to the nature of data-driven methods. In practice, the complete data collection is expensive and time-consuming, especially for newly built or small-scale wind farms, which brings the problem of using limited data into sharp focus. To this end, in this paper, a novel idea of first using the prior knowledge of an empirical model for data augmentation based on the raw limited samples and then using the deep neural network to learn from the augmented data is proposed. This helps the neural network to safely approach the degradation characteristics, avoiding overfitting. In addition, a new neural network, namely, pre-interaction long short-term memory (PI-LSTM), is designed, which is able to better capture the sequential features of time-series samples, especially in the periods in which the continuous features are interrupted. Finally, a fine-tuning process is conducted using the limited real data for eliminating the introduced knowledge bias. Through a case study based on real sensor data, both the idea and the PI-LSTM are proved to be effective and superior to the state-of-art

Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach

A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan