中文摘要：

为了获得足够有效的切削过程状态信息，确保产品质量/系统零部件的安全可靠运行，应采用可靠的监测策略并对传感器进行优化布置。针对单工位多工步切削过程状态监测，基于多工位误差流（Stream of variation，SOV）理论构建单工位多工步信息流模型，通过状态空间变换和主成分分析确定故障/监测量信息传递系数π用来表征不同测点传感器的监测能力；考虑传感器以及故障/监测量之间的特性差异对于系统检测能力的影响，采用6Sigma的因果矩阵（Cause-effect matrix, CEM）和失效模式与影响分析（Failure mode and effect analysis, FMEA）工具分别对传感器以及故障/监测量特性进行量化表示；基于属性层次模型（Attribute hierarchical model, AHM）构建传感器，故障/检测量以及系统检测能力之间的因果关系，设定优化目标和约束条件，并采用元启发式算法-混合蛙跳算法（Shuffled frog leaping algorithm, SFLA）和遗传算法（Genetic algorithm, GA）用于优化计算。提出基于单工位状态监测的六步传感器优化布置策略。实例分析表明，在一定约束条件下，就优化目标而言， SFLA显示比GA更高的优化效率，为单工位状态监测的传感器布置优化提供实践参考。

英文摘要：

The impact of optimal sensor placement on the access to status information of the cutting process, the product quality and the operation safety of mechanical parts in manufacturing systems is significant. Aiming at multi-step status monitoring in single station, an optimal sensor layout is proposed for troubleshooting. The stream of multi-step information model is proposed based on the stream of variation （SOV） theory. The information transfer coefficientπ, which characterizes detectability of sensors in different measuring points, is derived from the state space transform and main component analysis. Considering the influence of the characteristics of sensor and fault/object on detectability of the system, the six sigma tools, the C＆amp;E matrix （CEM） and the failure mode and effect analysis （FMEA）, are employed to quantify the characteristics of sensor and fault/object, moreover, the causal relations between sensor, fault/object and detectability of system are developed based on the attribute hierarchical model （AHM）.The optimization goals and constraints are determined. The shuffled frog leaping algorithm （SFLA） and genetic algorithm （GA） are used for calculation. Six steps of the sensor deployment are proposed. Case analysis shows that, under some constraints, the efficiency of SFLA is higher than that of GA for optimization goal, which provides a practical reference for the status monitoring in single station.