本书围绕数控磨齿机可靠性技术中的一系列问题，在可靠性建模、可靠性评估、故障分析及可靠性风险评价等方面进行了研究，以达到提高国产数控磨齿机的可靠性水平的目的。本书首先确定了可靠性评价特征量及这些特征量之间的相互关系，为可靠性评估方法的研究奠定基础；其次对小样本可靠性评估方法进行了研究，建立了基于 RBF 神经网络的可靠性评估流程，同时对数控成形磨齿机故障和可靠性风险进行了综合分析，为提升机床装备可靠性奠定基础。 本书适合企业技术装备人员阅读，也可作为高等学校机械制造、智能制造相关专业在校本科生、研究生的教学参考用书。
王会良，副教授，硕士生导师，工学博士，中国机械工程学会会员，毕业于西北工业大学机械设计及理论专业。主要研究领域：齿轮数字化设计与智能制造技术、机器人控制技术、装备可靠性工程等。授权发明专利4项，出版专著3部，发表论文30余篇。
第 1 章 可靠性建模理论 ·····························································.1
1.1 可靠性建模与可靠性评价 ···············································.1
1.1.1 可靠性建模目的和意义 ·········································.1
1.1.2 可靠性评价函数 ··················································.2
1.1.3 可靠性评价指标 ··················································.4
1.2 可靠性分布模型简介 ·····················································.5
1.2.1 韦布尔分布模型 ··················································.5
1.2.2 指数分布模型 ·····················································.7
1.2.3 正态分布模型 ·····················································.9
1.2.4 对数正态分布模型 ···············································10
1.3 可靠性分布模型检验方法 ···············································12
1.3.1 统计假设检验 ·····················································12
1.3.2 拟合优度检验 ·····················································14
1.4 本章小结····································································16
第 2 章 数控磨齿机磨削原理························································17
2.1 磨削加工理论 ······························································17
2.1.1 磨削机理 ···························································17
2.1.2 磨削加工 ···························································17
2.1.3 磨削力计算 ························································19
2.2 成形磨齿机磨削原理 ·····················································20
2.2.1 成形磨齿原理 ·····················································20
2.2.2 成形数控磨齿机结构 ············································21
2.3 摆线轮磨削原理 ···························································22
2.3.1 摆线轮齿面形状 ··················································22
2.3.2 摆线轮啮合方程 ··················································25
2.3.3 摆线轮磨齿机结构 ···············································29
2.3.4 摆线轮磨削机理 ··················································32
2.4 本章小结····································································33
第 3 章 数控磨齿机故障综合分析 ··················································34
3.1 振动信号故障特征提取技术 ············································34
3.1.1 EMD 算法介绍····················································34
3.1.2 实验数据采集 ·····················································37
3.1.3 振动数据的处理与分析 ·········································39
3.2 数控磨齿机故障树分析（FTA） ·······································42
3.2.1 FTA 基本方法 ·····················································42
3.2.2 数控磨齿机故障树的建立 ······································43
3.2.3 数控磨齿机故障树定性分析 ···································45
3.3 基于 FTA-AHP 的数控磨齿机可靠性分析····························48
3.3.1 FTA-AHP···························································48
3.3.2 在数控磨齿机上应用 FTA-AHP ·······························49
3.3.3 故障因素层次总排序分析 ······································53
3.4 本章小结····································································55
第 4 章 基于 RBF 神经网络的数控磨齿机可靠性评估···························56
4.1 RBF 神经网络简介························································56
4.1.1 RBF 神经网络结构介绍·········································56
4.1.2 RBF 神经网络学习算法·········································58
4.2 基于 RBF 神经网络的扩充算法 ········································60
4.2.1 RBF 神经网络扩充算法的设计 ································60
4.2.2 RBF 神经网络可靠性评估流程 ································61
4.3 实例分析····································································63
4.3.1 原始故障间隔时间分布模型 ···································63
4.3.2 RBF 神经网络扩充数据及分析 ································67
4.3.3 数控磨齿机可靠性评估 ·········································71
4.4 本章小结····································································75
第 5 章 基于贝叶斯方法的数控磨齿机可靠性建模分析 ·························76
5.1 可靠性数据的收集 ························································76
5.1.1 可靠性数据分类 ··················································76
5.1.2 可靠性数据收集方法 ············································77
5.1.3 可靠性数据的筛选 ···············································78
5.2 数控磨齿机先验信息 ·····················································79
5.2.1 先验信息预处理 ··················································79
5.2.2 先验信息相容性检验 ············································79
5.3 数控磨齿机先验分布的确定 ············································82
5.3.1 常用的先验分布介绍 ············································82
5.3.2 自助法确定先验分布 ············································82
5.3.3 分布模型的假设检验 ············································85
5.4 后验分布计算 ······························································87
5.4.1 Win BUGS 软件计算·············································87
5.4.2 后验参数区间和 MTBF 计算···································94
5.5 本章小结····································································95
第 6 章 基于模糊 FMECA 的数控磨齿机可靠性评估····························97
6.1 传统 FMECA 方法介绍 ··················································97
6.2 模糊风险评价模型 ························································98
6.2.1 模糊语言变量 ·····················································99
6.2.2 模糊综合评判 ··················································.100
6.3 数控磨齿机子系统 FMEA 分析······································.102
6.4 数控磨齿机子系统模糊风险评价（CA） ·························.108
6.4.1 进给伺服子系统 ···············································.108
6.4.2 机床液压子系统 ···············································.110
6.4.3 磨削砂轮子系统 ···············································.111
6.4.4 电气控制子系统 ···············································.113
6.4.5 冷却润滑子系统 ···············································.114
6.5 本章小结·································································.116
第 7 章 总结与展望 ································································.118
7.1 研究工作总结 ···························································.118
7.2 展望·······································································.119
参考文献 ··············································································.120
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