YY/T 1813-2022 English PDF (YYT1813-2022)
YY/T 1813-2022 English PDF (YYT1813-2022)
YY/T 1813-2022: Methods for operational reliability information collection and evaluation of medical electrical equipment
PHARMACEUTICAL INDUSTRY STANDARD
OF THE PEOPLE’S REPUBLIC OF CHINA
CCS C 30
Method for Operational Reliability Information Collection
and Evaluation of Medical Electrical Equipment
ISSUED ON: MAY 18, 2022
IMPLEMENTED ON: JUNE 01, 2023
Issued by: National Medical Products Administration
Table of Contents
Foreword ... 3
1 Scope ... 4
2 Normative References ... 4
3 Terms and Definitions ... 4
4 Collection of Operational Reliability Information of ME Equipment and ME Systems ... 8
4.1 Sources of operational reliability information ... 8
4.2 Contents of operational reliability information ... 9
4.3 Fault judgment and record ... 12
5 Assessment of Operational Reliability of ME Equipment and ME Systems ... 14 5.1 Overview ... 14
5.2 Determining the assessment indicators of operational reliability ... 14 5.3 Determination of samples ... 14
5.4 Preliminary collation of operational reliability information ... 15 5.5 Inspection method of distribution type ... 16
5.6 Parameter estimation for continuous distributions ... 16
5.7 Preparation of assessment report of operational reliability ... 16
Appendix A (Informative) Collection Form of Operational Reliability Information of ME Equipment and ME Systems ... 17
Appendix B (Informative) Description of Other Relevant Operational Reliability Parameters for ME Equipment and ME Systems ... 19
Appendix C (Informative) Introduction to Relevant Distribution Models ... 24 Appendix D (informative) Inspection of Relevant Distribution Type ... 27 Bibliography ... 29
Method for Operational Reliability Information Collection
and Evaluation of Medical Electrical Equipment
This Document specifies the methods for collecting operational reliability information and assessing the operational reliability of medical electrical equipment and medical electrical systems (hereinafter referred to as ME equipment and ME systems) within the period of use specified by the manufacturer.
This Document is applicable to the collection of operational reliability information and assessment of operational reliability assessment of ME equipment and ME systems. 2 Normative References
The provisions in following documents become the essential provisions of this Document through reference in this Document. For the dated documents, only the versions with the dates indicated are applicable to this Document; for the undated documents, only the latest version (including all the amendments) is applicable to this Document.
GB 9706.1-2020 Medical Electrical Equipment - Part 1: General Requirements for Basic Safety and Essential Performance
YY/T 1837-2022 Medical Electrical Equipment - General Requirements for Reliability 3 Terms and Definitions
For the purposes of this Document, the terms and definitions given in GB 9706.1-2020 and YY/T 1837-2022 and the following apply.
3.1 Parameter estimation
Infer unknown total distribution parameters from a sample.
The process of estimating parameters or parameter functions used to characterize reliability indicators in the analysis of operational reliability data of ME equipment and ME systems. [SOURCE: GB/T 17560-1998, 3.1.1, modified]
At different times, the reliability shall have different reliability levels r. When the reliability of the ME equipment and the ME systems drop to a given reliability level r, this time is called the reliable life of the ME equipment and the ME systems, denoted as tr.
For example, the median life refers to the time corresponding to the reliability level r=0.5, that is, R (t0.5) = 0.5.
3.12 Useful life
The period of time between ME equipment and ME systems from their first use until they no longer meet the user's requirements due to economics of operation and maintenance or obsolescence.
[SOURCE: GB/T 2900.99-2016, 192-02-27, modified]
3.13 Life unit
Use duration units of measure for ME equipment and ME systems. Such as hours, kilometers, times, etc.
3.14 Mean time between failures; MTBF; TBF
A basic reliability parameter for repairable ME equipment and ME systems. Its measurement method is: the ratio of the total number life units to the total number of failures of ME equipment and ME systems under specified conditions and within a specified period. 3.15 Mean time between critical failures; MTBCF; TBCF
A reliability parameter that is related to a task. Its measurement method is: in a series of specified task profiles, the ratio of the total task time to the total number of critical faults of ME equipment and ME systems. Formerly known as task time between fatal faults. 3.16 Mean time between maintenances; MITBM; TBM
A reliability parameter that is related to maintenance policy. Its measurement method is: under specified conditions and within a specified time, the ratio of the total number of life units to the total number of planned/unplanned maintenance events of the ME equipment and ME systems. 3.17 Failure distribution density function
It is used to characterize the change of failure distribution with time interval. When the ME equipment and the ME systems work until the time t, the ratio between the number of failures per unit time and the total number is recorded as f(t).
4.2 Contents of operational reliability information
4.2.1 ME equipment and ME systems information
Names, model specifications, serial numbers, production batch numbers, and other information of equipment, systems, components, and accessories.
4.2.2 User information
After obtaining the relevant authorization, the relevant information of the user can be collected: a) address of use;
b) User organization information;
c) The frequency of movement within the place where the equipment and system are used, such as the location is fixed and basically does not move, and it moves every time it is used, etc.;
d) Information such as operators and operating habits.
4.2.3 Environmental condition information
Environmental conditions for ME equipment and ME systems include temperature, humidity, atmospheric pressure, etc. The following factors also need to be recorded if they affect the reliability of ME equipment and ME system.
a) Climate environment:
1) For ME equipment and ME systems working in a controlled environment, the climate environment factors mainly refer to the following information:
● Salt spray factor;
● Illumination and radiation factors: indirect solar radiation, ultraviolet lamp irradiation, incandescent lamp irradiation, etc.
2) For ME equipment and ME systems working in an uncontrolled environment, the climate environment factors may include the following aspects in addition to the above points:
● Acid rain factors: pH value, type, etc.;
● water factor;
● direct solar radiation;
● Air pollution factors: particle size and nature of dust and smog.
b) biochemical environment:
1) Insects, microorganisms, rodents, etc.;
2) Chemical atmosphere: cleaning agents, disinfectants, body fluids, excrement, chemical reagents, etc.
c) Mechanical environment:
1) Vibration factor: whether it is at or close to the vibration source; 2) Mechanical shock factor;
3) Noise factor.
d) Power supply and electromagnetic field:
1) Information such as power supply quality, power load capacity, and power outages, etc.;
2) Non-ionizing electromagnetic radiation disturbance.
e) High-energy radiation environment: ionizing radiation.
Refer to Appendix A for the collection form of operational reliability of ME Equipment and ME systems.
4.2.4 Operation condition
The information that can be collected from the operation condition of ME equipment and ME systems:
a) delivery and acceptance time;
b) storage time;
c) activation time;
d) Use time (number of times);
e) frequency of use;
g) the start time of maintenance;
h) the end time of maintenance;
i) the re-start time of use;
b) start and end time of maintenance;
c) re-start time of use;
d) maintenance level;
e) maintenance content;
f) The name, model, location and batch number of spare parts for maintenance and replacement;
g) maintenance personnel.
4.3 Fault judgment and record
4.3.1 Fault classification
The fault classification method of ME equipment and ME systems are as follows: a) According to the inevitability of the fault, it is divided into accidental fault (ME equipment and ME system fault caused by accidental factors, which can only be predicted by probability or statistical methods) and deterministic fault (fault of ME equipment and ME system which have a certain action to produce a certain response, such fault exhibits a constant response to all actions);
b) According to the degree of fault, it is divided into intermittent fault (after ME equipment and ME systems fault occurs, it recovers their functions within a limited time without repair) and permanent fault (persistent fault before the corrective maintenance of ME equipment and ME systems is completed);
c) According to the fault occurrence process, it is divided into sudden fault (fault that cannot be predicted by prior detection or monitoring) and gradual fault (fault in which the specified performance of ME equipment and ME systems gradually changes over time. Gradual fault can be predicted through prior detection or monitoring, and sometimes it can be avoided through preventive maintenance).
4.3.2 Statistics principles of fault
According to the following principles, faults are classified, counted and judged. a) The statistics and counting principles of fault are as follows:
1) Only relevant faults are counted, and non-relevant faults are not counted; 2) The faults found at the end of the monitoring or random sampling are also counted in the statistics;
3) Planned maintenance such as replacement of consumables and necessary adjustments are not counted;
4) Faults caused by the same reason are recorded as one fault;
5) Faults caused by N independent fault reasons are recorded as N faults; 6) Intermittent faults with the same fault mode occurring multiple times at the same location are recorded as one fault.
b) The following situations can be judged as non-relevant faults of ME equipment and ME systems:
1) Fault caused by improper installation;
2) Fault caused by mis-operation;
3) Fault caused by faults of supporting facilities or instruments;
4) Fault caused by environmental conditions or working conditions exceeding the working limit or storage limit;
5) Fault introduced during maintenance;
6) Overdue use of a device with a limited and predictable useful life, causing the device to fail and its associated fault;
7) Unauthorized modifications;
8) Fault caused by other external factors.
c) Except for non-relevant faults, all other faults are judged as relevant faults, such as: 1) Fault caused by design defects or poor manufacturing process;
2) Fault caused by the failure of the component due to the potential defect of the component;
3) Fault due to software;
4) Intermittent fault;
5) Adjustment beyond the normal range of the specification;
6) Limits caused by all fault symptoms (not exceeding the performance limit) due to non- dependent fault reasons;
7) Anomalies whose cause cannot be proven for the time being.
5.4 Preliminary collation of operational reliability information
5.4.1 Primary and secondary and causal analysis of fault data
The primary and secondary and causal analysis of fault data can be carried out through permutation diagrams, causal diagrams, etc.
a) The collected fault data during the use of ME equipment and ME system can be used for primary and secondary analysis through the method of array chart; and according to the factors affecting the operational reliability, such as faulty parts, fault modes, users, and specific environmental conditions, etc., arrange them from left to right according to the frequency of occurrence, observe and analyze the main factors affecting the operational reliability.
b) Identify all the causes of the fault through the causality diagram method; conduct causal analysis of the fault data; and analyze the relationship between the causes of the faults, so as to analyze the root cause.
5.4.2 Analysis of sample empirical distribution
The analysis of sample empirical distribution should be carried out through the histogram. a) Histogram drawing.
b) Histogram analysis:
According to the shape of the drawn histogram, the distribution type of the fault data is preliminarily judged, so as to select the appropriate distribution for analysis and evaluation of operational reliability.
1) Analyze the shape of the histogram
Analyzing the shape of the histogram can determine whether the whole is normal or abnormal; and then seek the cause of the abnormality. The analysis shall focus on the overall shape.
2) Comparison between histogram and specification limits
The mean value of the distribution is in the middle of the specification limit, and the distribution is symmetrical, the dispersion is small; and the observed value can meet the specification requirements stably.
c) Empirical distribution of the sample.
The mathematical function expressions of the life distribution of ME equipment and ME systems usually include exponential distribution, normal distribution, lognormal distribution, Weibull distribution, etc. For a detailed introduction to each distribution model, see Appendix C.
5.5 Inspection method of distribution type
5.5.1 Inspection by graph estimation method
According to the type of sample distribution, the inspection by graph estimation method can refer to the following methods:
a) Refer to D.1 in Appendix D for the inspection by graph estimation method of exponential distribution;
b) Refer to Clause 5 of GB/T 4882-2001 for the inspection by graph estimation method of normal distribution and lognormal distribution;
c) Refer to Clause 7 of GB/T 34987-2017 for the inspection by graph estimation method of Weibull distribution.
5.5.2 Inspection by mathematical calculation method
According to the type of sample distribution, the inspection by mathematical calculation method can refer to the following methods:
a) Refer to D.2 and D.3 for procedures and methods of χ2 inspection and F inspection commonly used in exponential distribution;
b) Refer to GB/T 4882-2001 for the inspection methods of normal distribution and lognormal distribution;
c) Refer to GB/T 34987-2017 for the calculation method inspection of Weibull distribution. 5.6 Parameter estimation for continuous distributions
5.6.1 Refer to GB/T 5080.4-1985 for parameter estimation of exponential distribution. 5.6.2 Refer to GB/T 4889-2008 for parameter estimation of normal distribution and lognormal distribution mean and variance.
5.6.3 Refer to GB/T 34987-2017 for parameter estimation of Weibull distribution. 5.7 Preparation of assessment report of operational reliability
The content of the text of the operational reliability assessment report shall include elements such as ME equipment and ME systems information, data range, assessment method, assessment process, and assessment results, etc.
Inspection of Relevant Distribution Type
D.1 Graph-estimation distribution inspection (exponential distribution) D.1.1 Estimate by the point given on one-sided logarithmic paper, only applicable when first- time failure and the number of failures is at least 4.
D.1.2 Estimate by the point given in graph-estimation method and can also indicate signs of departure from a constant failure rate.
D.1.3 The number of tested ME equipment and ME systems is n; and the number of observed failures is γ. For each failed equipment, record the relevant failure time ti. D.1.4 Arranged in the order of failure time to obtain t1< t2< ...< ti.
D.1.5 On one-sided logarithmic paper, the ti value is on the abscissa according to the linear scale point; while is on the ordinate according to the logarithmic scale point. Thereof, is the 50% median rank of ti, the value of which can be checked through the table.
D.1.6 If the assumption of constant failure rate can be established, then these points fit well with a straight line passing through ti=0 and with a ratio of 1.
D.1.7 When drawing a graph line, the slope is mainly determined based on some points in the middle.
D.1.8 If these points are near the straight line, then the mean life is the time coordinate t corresponding to 2.72 on the ordinate. The estimate of the failure rate is the reciprocal of the t- value.
D.2 χ2 inspection
D.2.1 Let the distribution function of the population X be F(x); and inspect the null hypothesis based on the samples from the population. Inspect the hypothesis H0: F(x)=F0(x). Divide the value range of the overall X into k intervals (α0, α1], (α1, α2], ..., (αk-1, αk], α0 can take -∞, αk can take +∞. The probability that X falls into the ith interval pi=F0(αi) - F0(αi-1). i = 1, 2, …, k, αi are continuous points of F0(x).
D.2.2 If the sample size is n, then npi is the theoretical frequency falling in (αi-1, αi] interval; if the actual frequency of n observation values falling into (αi-1, αi] is ni, then when H0 is established, when n→∞ , the limit distribution of statistic is the χ2
distribution with k-1 degrees of freedom.
D.2.3 In most cases, θ = (θ1, θ2, ..., θm) in the parent distribution F0(x; θ) to be tested is an m- dimensional unknown parameter. To calculate pi in the statistic χ2, use the extreme large likelihood estimate 𝜃 replaces θ, that is, ̂i = F0(αi; 𝜃) - F0(αi-1; 𝜃), i=1, 2, …, k. At this time, the selected test statistic is , the limit distribution of such statistic is the χ2 distribution with k-m-1 degrees of freedom.
D.2.4 For a given significance level α, the critical value χ2 1-α (k-m-1) can be obtained from the quantile point of the χ2 distribution. When the observed value of χො2 is greater than the critical value χ2 1-α (k-m-1), reject the null hypothesis.
D.2.5 Statistics distribution value table of χ2 distribution can refer to GB/T 4086.2-1983. D.3 F-inspection
D.3.1 Suppose the samples of random variables X and Y are respectively x1, x2, ..., xn1 and y1, y2, ..., yn2, and their sample variances are DX and DY respectively. Inspect whether the population variance DX of X is equal to the population variance DY of Y. Suppose H0: DX = DY=σ2. D.3.2 According to the statistical theory, when the assumption is established, the statistical quantity obeys the F distribution with the first degree of freedom n1-1 and the second degree of freedom n2-1.
D.3.3 Given the significance level α in advance, check the F distribution value table to get Fα/2. If the calculated F value is less than Fα/2, the assumption is valid, otherwise the assumption is unreasonable.
D.3.4 Statistics distribution value table of F distribution can refer to GB/T 4086.4-1983.