Equipment Dynamic Reliability Assessment And Fault Diagnosis Based On Process Capability Indexes

Dynamic reliability analysis,fault diagnosis,and maintenance are important equipment operation management tasks that are related to equipment operation safety and efficiency.Therefore,dynamic reliability and fault risk identification approaches need to be studied to assist maintenance decisions.This work will standardize equipment operation management.On the basis of the classic process capability indexes(PCIs)and fault tree analysis(FTA),this study proposes improved PCIs and fault risk indexes(FRIs).These improved PCIs,FRIs,FTA,and sensors’ data are combined to assess equipment dynamic reliability and diagnose faults.The main innovations and contributions of this study are shown as follows,which mainly contain three aspects:1.On the basis of the classical PCIs,three types of improved PCIs and FRIs are proposed and applied to equipment stability and fault risk assessment characterized by equipment operation data.On the basis of the traditional PCIs,three kinds of improved PCIs and FRIs,namely,Q,R,and η index(eta index),are proposed in this study.The Q index includes two improved PCIs and two FRIs.The Q index can identify the dynamic stability and control the results in the[0,1]interval,which overcomes the defects of traditional PCIs.The R index includes two improved PCIs and two FRIs.The R index is characterized by the intermediate variable,which constructs an exponential function to change and solve the defects of the traditional PCIs,thereby resulting in the R index being more flexible than the Q index.The η index comprises an improved PCI and an FRI.The η index defines a fault risk factor η,and introduces an intermediate variable,thereby ensuring better interpretability of the data stability analysis.2.PCIs and FRIs that integrate expert knowledge are proposed.As a result,the status and fault risk’s grade division by PCIs and FRIs are consistent with the results of experts’ assessment.To solve the controversy of the intermediate parameters of original R index andη index,this study proposes the R index and η index with expert knowledge,which obtains the PCIs and FRIs’ equations by marking a random number and verifies the accuracy of the equation by marking feature data.We select the feature data,which represent the operation characteristics of the equipment,to compare these FRIs.Therefore,we can select the optimal FRI for equipment status assessment and fault diagnosis.3.The fault risk reasoning and dynamic failure mode analysis(DFMA)approach based on FRIs and FTA are proposed to assess the equipment operation status,diagnose faults,and assist maintenance decisions.The FRIs are approximated as the fault probability of the FTA’s basic events.Therefore,FRIs can drive the FTA to reason the equipment’s dynamic fault risk.On the basis of the structural importance index,this study proposes the fault importance index.In combination with the FRIs,DFMA is proposed to calculate the dynamic fault risk of equipment.Moreover,a state partition method,which distinguishes the state of equipment to normal operation,component degradation,equipment performance degradation,and equipment fault,is proposed.A six-partition representation method of equipment status is adopted to assist the fault diagnosis and maintenance decision.To verify these methods,we applied them to the synchrotron cooling water system’s data analysis.In addition,we propose the assumption of multiple FRI cross-selected device state reasoning of FTA and DFMA.For the fault risk analysis of multicomponent equipment,the adapted FRI can be selected according to different components and data types,and the FTA fault risk reasoning and DFMA approach can be used in a variety of FRI modes.The approaches presented in this study combine sensors’ data,equipment structure,and expert knowledge.The proposed approaches have application and popularization value for equipment dynamic reliability assessment,fault diagnosis,and maintenance decision.