基于相关失效理论的采煤机整机动态最优可靠性研究

Shearer is the core equipment of integrated mining work surface in the mining production, whose reliability is not only related to the coal production, but also related to the safety of personnel and equipment. However, up to now, the research of the reliability of the shearer is mostly based on the assumption that each component or each failure mode is independent of each other and the influence of the actual working load on the reliability of the shearer is usually neglected, as a result of which, the obtained reliability data is not consistent with the actual situation. This has become a leading edge and difficulty in the research of the reliability of the shearer in the world. This project therefore proposes the study on the dynamic optimal reliability of shearer based on common-cause failures theory: obtain the historical reliability data of the components based on the stress-strength interference theory of common-cause failures; establish historical reliability model and the reliability correction model under dynamic loading of the whole shearers based on Copula function and Sklar theorem; furthermore, an online correction strategy (OCS) based on intelligent optimization approach of particle swarms optimization (PSO) is proposed to study on optimal reliability of shearers and assess the real time dynamic optimal reliability of the shearers in the integrated mining process so as to provide the theoretical basis for the selection of optimal working conditions, spare parts reservation and maintenance project and finally achieve the goal of reducing accidents and loss of personnel and equipment and ensuring the safe, stable and efficient production.

作为煤矿综采工作面生产核心设备的采煤机，其可靠性不仅事关煤炭的产量，更事关工作面人员设备的安全；然而，迄今为止，国际上对采煤机可靠性的研究，基本建立在各部件或各失效模式间相互独立的假设下并忽略了实际工作载荷对可靠性的影响，导致所得到的预报结果误差较大甚至错误，成为国际上采煤机可靠性研究的一个前沿和难点。本项目，因此提出基于相关失效理论的采煤机整机动态最优可靠性研究：基于相关失效的应力-强度干涉理论，得到各零部件的历史可靠性数据；基于Copula函数和Sklar定理，建立采煤机整机的历史可靠性模型及其动态载荷下的修正模型；提出PSO智能优化的采煤机可靠性OCS在线校正策略，以克服预报模型参数人为选择因素对结果的影响和综采过程不稳定性所导致的模型失配问题，得到最优预报结果，为采煤机最优工况选择、备件储备、维修计划制定提供理论依据，最终达到减少事故发生和设备人员损失、确保安全稳定高效生产的目的。

作为煤矿综采工作面生产核心设备的采煤机，其可靠性不仅事关煤炭的产量，更事关工作面人员设备的安全；然而，长期以来采煤机事故在国内外屡有发生，除人为因素外，都是因为采煤机长期大载荷甚至超负荷工作，导致其有效寿命期内某些关键系统或零部件就已经濒临失效，从而导致采煤事故的发生，其本质原因在于：采煤机作为机械、电气、液压系统高度一体化的大型复杂设备，其各部件的失效具有普遍的相关性。忽略失效的相关性，进行零部件和系统的可靠性分析与计算将会导致较大的误差，甚至得出错误的结论；另外，采煤机的运行是一个典型的动态过程，传统的可靠性分析理论与方法大多针对的是系统的静态特性，而实际生产过程中，工作载荷的变化将直接影响到采煤机零部件与系统的可靠性。因此有必要在考虑相关失效的前提下，对随工作载荷而变化的采煤机动态可靠性进行研究。本项目，因此提出了基于相关失效理论的采煤机整机动态最优可靠性研究，并以采煤机运行最为重要的采煤机切削运行模式的可靠性为具体对象展开研究。首先，基于相关失效的应力-强度干涉理论、Copula函数和Sklar定理，建立了采煤机整机切削模式可靠性模型，然而研究结果表明其可靠性预报效果不理想，表明采煤机各切削模式可靠性之间的非线性十分强烈，使得基本相关失效理论的Copula-Sklar方法难以表征，混沌理论对各切削模式的非线性的研究结果也表明了其强烈的非线性特征。因此进一步引入了对非线性预报具有良好效果的非线性机器学习相关理论对可靠性进行研究，并进一步提出了群智能优化的采煤机切削运行可靠性OCS在线校正策略并结合了多种非线性模型参数辨识方法和动态优化寻优方法，以克服预报模型参数人为选择因素对结果的影响和采煤机切削运行过程不稳定性所导致的模型失配问题，实际生产数据运行结果和国际报道结果的比较研究都表明了所提出的动态最优可靠性预测方法的有效性。