Study On Early Failure Monitoring Method And Application Of Tissue Processor

Tissue processor is a medical device commonly used in pathology department for processing samples before pathological analysis in a hospital.As the most important pre-tissue processing device in the pathological examination process,its safety,effectiveness and reliability not only play decisive role in the pathological diagnosis,but also have direct impact on the safety of operators and the surrounding environment due to the complexity of the device itself.Based on the research of reliability theory and safety related standards,combined with literature review and project experience,this paper constructs a model for fault identification,detection and control of the tissue processor throughout its life cycle.Firstly,because of its low-risk classification and no direct contacting with patients’ body,manufacturers and users generally do not pay much attention to the safety and reliability of the tissue processor.As a result,the early failure rate of the product was high,and the actual service life of the product was shortened.To solve this problem,this paper builds a model to monitor the failure of the product at all stages from design and development to production and product launch,with the manufacturer as the main body,the bathtub curve as the basis,and the purpose of reducing the early failure rate of the product as much as possible.For the main causes of early failures,design and production,the study combines the tools and methods in related fields,such as V model,FTA,human factors engineering,process validation,etc.,and proposes a method to analyze and optimize the reliability of tissue processor in design and production.Then,through the application of the model in the project of Company A,monitoring the early failure of the tissue processor and the statistical analysis of the post-marketing data,the operability of the model is verified,and various tools and methods practical application are elaborated in the process of design and production.214 potential early failure modes are identified and controlled.The product failure rate is reduced from 22% at early stage of product launch to 10% in two years.The results show that the model effectively reduces the early failure rate of the product and improves the product quality reliability in service life.The results of this study are expected to provide reference for failure monitoring of other similar type of active medical devices.