故障模式和影响分析(failure mode and effect analysis, FMEA)是一种评价复杂装备产品设计风险的方法。针对该方法中忽略风险因子权重和风险优先数难以准确评价故障模式风险优先级的难题,提出了一种基于Fuzzy-TOPSIS-FMEA的复杂装备产...故障模式和影响分析(failure mode and effect analysis, FMEA)是一种评价复杂装备产品设计风险的方法。针对该方法中忽略风险因子权重和风险优先数难以准确评价故障模式风险优先级的难题,提出了一种基于Fuzzy-TOPSIS-FMEA的复杂装备产品设计风险评价模型。该模型基于三角模糊数对专家评价进行量化处理,建立故障模式评价信息的标准化矩阵。在此基础上,将主、客观权重相结合进而分配风险因子权重,获取其综合权重;基于逼近理想解排序法(technique for order preference by similarity to ideal solution, TOPSIS)测度故障模式的相对贴近度,依据相对贴近度的大小确定故障模式的风险水平。最后,通过数控机床主轴进行案例研究,验证了文中方法的可行性和有效性。展开更多
目的探究故障模式与失效分析(Failure Mode and Effects Analysis,FMEA)理论结合持续质量改进循环管理(Find-Organize-Clarify-Understand-Select-Plan-Do-Check-Act,FOCUS-PDCA)模式对消毒供应中心复用医疗器械管理质量的影响。方法以...目的探究故障模式与失效分析(Failure Mode and Effects Analysis,FMEA)理论结合持续质量改进循环管理(Find-Organize-Clarify-Understand-Select-Plan-Do-Check-Act,FOCUS-PDCA)模式对消毒供应中心复用医疗器械管理质量的影响。方法以本院2023年1—4月采用常规清洗消毒管理模式进行管理的4562件手术器械为对照组,2023年5—8月行FMEA理论结合FOCUS-PDCA模式后管理的手术器械5628件为观察组,通过FMEA理论识别管理过程中的故障模式,并通过FOCUS-PDCA持续进行针对性的改进,对比2组器械管理不合格率、丢失损坏率、环境卫生学检测情况、团队合作情况、使用满意度、院内感染发生率的差异。结果观察组的器械管理不合格率和丢失损坏率显著低于对照组,环境卫生学检测情况、团队合作情况和手术器械使用满意度显著优于对照组,且院内感染发生率显著低于对照组,差异均有统计学意义(P<0.05)。结论FMEA理论结合FOCUS-PDCA模式能够显著提升消毒供应中心复用医疗器械的管理质量,明显改善消毒供应中心环境卫生情况,还能够提升消毒供应中心工作人员之间的团队合作能力和医生对医疗器械的满意度,降低院内感染的发生率。展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
文摘故障模式和影响分析(failure mode and effect analysis, FMEA)是一种评价复杂装备产品设计风险的方法。针对该方法中忽略风险因子权重和风险优先数难以准确评价故障模式风险优先级的难题,提出了一种基于Fuzzy-TOPSIS-FMEA的复杂装备产品设计风险评价模型。该模型基于三角模糊数对专家评价进行量化处理,建立故障模式评价信息的标准化矩阵。在此基础上,将主、客观权重相结合进而分配风险因子权重,获取其综合权重;基于逼近理想解排序法(technique for order preference by similarity to ideal solution, TOPSIS)测度故障模式的相对贴近度,依据相对贴近度的大小确定故障模式的风险水平。最后,通过数控机床主轴进行案例研究,验证了文中方法的可行性和有效性。
文摘目的探究故障模式与失效分析(Failure Mode and Effects Analysis,FMEA)理论结合持续质量改进循环管理(Find-Organize-Clarify-Understand-Select-Plan-Do-Check-Act,FOCUS-PDCA)模式对消毒供应中心复用医疗器械管理质量的影响。方法以本院2023年1—4月采用常规清洗消毒管理模式进行管理的4562件手术器械为对照组,2023年5—8月行FMEA理论结合FOCUS-PDCA模式后管理的手术器械5628件为观察组,通过FMEA理论识别管理过程中的故障模式,并通过FOCUS-PDCA持续进行针对性的改进,对比2组器械管理不合格率、丢失损坏率、环境卫生学检测情况、团队合作情况、使用满意度、院内感染发生率的差异。结果观察组的器械管理不合格率和丢失损坏率显著低于对照组,环境卫生学检测情况、团队合作情况和手术器械使用满意度显著优于对照组,且院内感染发生率显著低于对照组,差异均有统计学意义(P<0.05)。结论FMEA理论结合FOCUS-PDCA模式能够显著提升消毒供应中心复用医疗器械的管理质量,明显改善消毒供应中心环境卫生情况,还能够提升消毒供应中心工作人员之间的团队合作能力和医生对医疗器械的满意度,降低院内感染的发生率。
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
