Aiming at the problem of predicting the remaining life of rolling bearings, a method for predicting the remaining life of bearings based on the arithmetic optimization algorithm (AOA) and the long-short-term memory network (LSTM) fusion algorithm is proposed. First, use the random forest algorithm to analyze the importance of the extracted time-domain and frequency-domain feature indicators, and build a degradation feature decision table; then, use the AOA optimization algorithm to optimize the hyperparameters in the LSTM, and select the optimal hyperparameters to establish predictions model; finally, the degradation features are input into the prediction model for prediction, and the prediction model is evaluated by root mean square error (RMSE) and mean absolute error (MAE). The proposed research method is experimentally verified by the XJTU-SY dataset. The experimental results show that the RMSE and MAE of the proposed research method are 5.56% and 4.37%, respectively. Compared with the MLP model, the RMSE and MAE are reduced by 31.58% and 29.61%, respectively. Compared with the RNN model, the RMSE and MAE are reduced by 24.66% and 25.49%, respectively, which verifies the effectiveness of the research method.
标准粒子群优化算法(PSO)易在局部求得最优解,为解决这一问题,采用自适应权重,在迭代中调整惯性,确保在每一次迭代中使用最佳权重,较好地解决局部最优的问题.该方式可改善传统PSO算法,与实践经验整定法、标准粒子群算法及一般自适应粒子群算法的寻优结果对比.结果显示:其超调量为3.2％,调节时间减少到0.165s,与其他3种方法相比,振荡更低,且收敛速度更快.采用该方法调整,系统参数确保比例、积分、微分(PID)控制器的性能指标更好,以及控制系统更稳定.
数控机床作为机械制造业中先进加工设备,由于设备复杂造价昂贵,其状态监测尤为重要.本文结合数字孪生技术,以LG Mazak数控机床为研究对象,建立了机床的三维模型,基于Uniyt3D软件构建了数控机床数字孪生系统.首先明确了数字孪生系统的五维架构;其次分析了搭建数字孪生系统的关键技术和功能设定,搭建了包括车间漫游、数据可视化、切削过程仿真、刀具碰撞预警、数据查询系统等功能在内的服务平台;最后完成了系统各功能模块进行集成.本系统为数控机床虚实交互操作和数据可视化提供新思路,同时对数控机床操作培训提供便利.
为解决电池成组无法克服的一致性问题,获得最优的电池组寿命,设计可提升电池组一致性的均衡方案,对其实行干预,并验证该策略的可靠性.结果表明:在各工作区间内,经循环处理的电池表现出不同的容量衰减速率,逐渐提升放电深度(DOD)时,电池容量衰减更快.高荷电状态(SOC)区间与高DOD循环,容量衰减进一步加快.以相同均衡方法调节此单体的放电起点SOC为80％,确保容量衰减速率相对最差单体更慢,实现充分放电,确保控制组内所有单体电池一致.电池组的一致性获得了显著改善,循环处理250次后,单体电池达到相近容量,容量一致性也由0.006 3减小为0.001 2,均衡效果良好.设计的电池寿命均衡策略优化了长时间尺度范围的电池组容量一致性.
当前采用综合方量油耗指标评价泵车油耗,但该指标无法反映油耗异常的具体原因.根据影响泵车油耗的因素,从设备性能、行为和环境3个维度,建立泵车燃油消耗数学模型.该模型基于边缘计算系统分析各个设备不同维度的油耗数据,与云平台结合,建立云边一体的泵车油耗指标评价体系,分析多个设备的油耗数据和设备油耗异常的原因,并对分析结果进行推送.实践证明:该方法对分析掌握设备的油耗状况、进行针对性的改进有很大帮助,从而降低设备整体燃油损耗.
