CN113050595B - Potential fault analysis method based on PFMEA and HRA method - Google Patents
Potential fault analysis method based on PFMEA and HRA method Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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Abstract
The invention provides a latent fault analysis method based on PFMEA and HRA methods, which comprises the following five steps: the method comprises the following steps: analyzing a task; step two: analyzing abnormal modes and abnormal reasons; step three: quantifying the occurrence probability of the abnormal function mode; step four: risk priority calculation and latent fault identification. Through the steps, the problems of man-machine separation and difficulty in quantifying the potential fault probability of the PFMEA method are solved, the man-machine ring coupling effect and the human error correlation are considered by combining the CREAM and THERP methods in the HRA, and the human error probability and the potential fault occurrence probability are quantified.
Description
Technical Field
The invention provides a latent fault Analysis method based on a PFMEA (pulse frequency membrane effect) and an HRA (high Reliability Analysis) method, namely a latent fault Analysis method considering Human-computer ring coupling characteristics and Human error correlation.
Background
With the rapid improvement of the scientific and technical level, the performance and the complexity of a human-computer system are continuously improved, the functional structure of equipment is increasingly complex, higher requirements are provided for the human-computer interaction process, and human-computer interaction errors become weak links of the reliability of the human-computer system. High performance ergonomic faults are affected by ergonomic ring coupling and, once a fault occurs, can result in significant loss of life and property. Therefore, attention must be paid to human-machine system reliability, especially human reliability during human-machine interaction.
For some high-performance complex man-machine systems, such as aerospace, weaponry, and the like, the high-performance complex man-machine systems often have obvious staged characteristics, and different stages correspond to different tasks. In the field of civil aviation, a flight task of an air passenger can be divided into four stages of sliding, taking off, cruising, landing and the like, and before the flight task, the flight task also comprises a ground maintenance and guarantee task. A multi-stage task may result in a new failure mode-latent failure, i.e. some failure modes in the task process will not appear in the current stage, but will be activated in subsequent related tasks and ultimately lead to serious safety consequences. The root cause of the nuclear power station accident of the American Trijima is that workers forget to open a control valve of a cooling system after overhauling, and the potential fault causes that water in a second loop is still in a cutoff state after the system is automatically put into use, thereby finally causing a serious accident. Statistically, the frequency of potential faults caused by aviation repair errors and ultimately induced aviation accidents is increasing, and 16.7% (1989-1998) of aviation accidents in China are induced by potential faults caused by repair errors.
The potential fault of the multi-task stage system is closely related to human, machine and ring elements in the task execution process, and a typical analysis method is PFMEA. The PFMEA is a method for applying a traditional Failure Mode and Effects Analysis (FMEA) to process Analysis, which decomposes a series of events in a process according to a time sequence and an event hierarchy relationship, and then completes abnormal state (Failure, human error or environmental disturbance, etc.) Analysis and abnormal state effect evaluation of each sub-process by analogy with the FMEA. The PFMEA can be used for the danger analysis in the processes of system operation, maintenance and the like, is an effective means for reliability analysis, and is widely applied. For example, in order to make ship-ship cargo transfer work more and more reliable, researchers at Athens national industry university adopt PFMEA technology to analyze the adverse effect of potential faults and make corresponding improvement measures; the researchers at Shanghai university of transportation use PFMEA technology in the production process of automobiles and related products for the process quality control.
Latent faults are often related to the behavior of designers, decision makers, and maintenance personnel, and therefore latent fault analysis requires significant human consideration. The conventional PFMEA method considers human errors in a task process, but is mainly experienced and limited only on a qualitative level, does not consider the human-computer ring coupling effect, and cannot realize quantitative calculation of human error probability. In addition, the operation actions of the personnel in the task process of each stage have a certain correlation relationship, so that the probability of human errors of subsequent related operations is greatly increased, the problem of insufficient consideration of the correlation of the human errors exists in the conventional PFMEA method, and the human error probability cannot be accurately quantized on the basis of considering the correlation of the human errors.
In view of the above, the present invention provides a latent fault analysis method based on PFMEA and HRA methods. The Method considers the influence of the situation environments such as machines, environments and the like on human errors in the task process based on a Cognitive Reliability and Error Analysis Method (CREAM) in the HRA Method, and solves the quantization problem of the human errors under the influence of man-machine ring coupling; based on the THERP (Human Error probability Prediction) analysis and quantification of Human Error correlation in the task process, the Human Error probability is corrected, and the quantification problem of potential faults is solved.
Disclosure of Invention
1. Purpose(s) to
Aiming at the problems of man-machine separation and difficult quantization of the traditional latent fault analysis method, the invention provides a latent fault analysis method based on PFMEA and HRA methods, which analyzes the occurrence mechanism of human errors under the coupling action of a man-machine ring based on a CREAM method, analyzes the correlation of the human errors in the task process based on a THERP method, corrects the probability of the human errors, comprehensively considers the occurrence probability of equipment faults and environmental disturbance, realizes the quantization and identification of the probability of the latent faults, forms the technical capability of systematically checking the latent faults of a man-machine system in the development stage, and supports the optimization of safety design.
2. Technical scheme
The invention relates to a latent fault analysis method based on PFMEA and HRA methods, a flow chart of the method is shown in FIG. 1, and the method comprises the following five steps:
the method comprises the following steps: and (3) task analysis: selecting a human-computer system to be researched, defining a task stage, completing task analysis of the stage by using a hierarchical task analysis method, and determining each basic activity required by completing the task;
step two: abnormal pattern and abnormal cause analysis: determining a function abnormal mode of basic activities according to a task analysis result, and carrying out human-computer system abnormal reason analysis in 3 aspects of human, machine and ring, wherein the reason analysis comprises human errors, equipment faults and environmental disturbance;
step three: quantifying the occurrence probability of the abnormal function mode: quantifying human error occurrence probability based on a CREAM method, determining human error correlation based on a THERP method, correcting the occurrence probability of human errors with correlation, and calculating the occurrence probability of the abnormal function mode according to the human error probability, equipment fault probability and environmental disturbance probability;
step four: risk priority calculation and latent fault identification: carrying out influence analysis and detection difficulty analysis on the potential abnormal function mode, determining the severity grade and the detection difficulty grade of the potential abnormal function mode, determining the occurrence probability grade based on the occurrence probability of the functional abnormal function mode, obtaining the risk priority number of the potential fault, completing PFMEA (pulse frequency membrane electrode assembly) form filling and identifying the potential fault;
through the steps, the potential fault analysis method based on the PFMEA and the HRA method is adopted, the problems of 'man-machine separation' of the PFMEA method and difficulty in quantifying the probability of the potential fault are solved, the CREAM and THERP methods in the HRA are combined, the man-machine ring coupling effect and the human error correlation are considered, and the quantification of the human error probability and the probability of the occurrence of the potential fault is realized.
The "task analysis" described in step one is described as follows:
(1) selecting task stage, defining human-machine ring elements
Aiming at a multi-task stage human-computer system, selecting a task stage and developing human-computer ring elements to clarify personnel, equipment and environment involved in a task process;
(2) developing task analysis based on hierarchical task analysis method and determining basic task activities
According to the hierarchical task analysis method manual, the steps of defining tasks, collecting data, determining the overall targets of the tasks, determining the secondary targets of the tasks, decomposing the secondary targets, analyzing the plans and the like are sequentially carried out, and basic activities needed by completing the tasks are determined.
The "abnormal pattern and abnormal cause analysis" described in step two is described as follows:
(1) carrying out abnormal mode analysis to determine the abnormal function mode of the basic unit;
analyzing abnormal patterns which may appear in each basic activity from a functional level by taking the basic activities of people as a unit on the basis of task analysis;
(2) carrying out abnormal reason analysis to clearly cause human errors, equipment faults and environmental disturbance of functional abnormality;
aiming at the abnormal mode of the function of the man-machine system, carrying out abnormal reason analysis from 3 aspects of man, machine and ring, including human error, equipment fault and environmental disturbance; wherein, human error analysis is carried out based on a CREAM method; the CREAM method generalizes the cognitive functions of people into 4 types of observation, explanation, planning and execution, wherein each type of cognitive function comprises a plurality of personal error modes, such as observation target error, error identification and the like.
Wherein, the "quantization of occurrence probability of abnormal function pattern" in step three is described as follows:
(1) CREAM method based quantification human error mode occurrence probability, see CREAM method in detail
Analyzing each personal error pattern, and determining the most probable cognitive function error pattern and error probability basic value of each cognitive activity; evaluating the scene environment of each cognitive activity, considering the influence of equipment and environment on the cognitive function of people in the scene environment, evaluating the levels of 9 Common Performance Conditions (CPCs) and determining weights, and multiplying the weights of the 9 CPCs to obtain a total weight factor; multiplying the basic value of the error probability by the total weight factor to calculate the probability of human error occurrence;
(2) determining human error correlation grade based on THERP method, and correcting human error probability
According to the composition of two task personnel (whether the two task personnel are carried out by the same personnel), the task time interval (the time between two task behaviors), the task repeatability (whether the task is a repeated task) and the existence of prompt information (information such as alarm signals), the corresponding degree of correlation judgment is carried out on the rules of the task corresponding table 1 of the person such as the inspection and the likeBreaking; then, the human error probability P is corrected based on the correlation formulaHE: zero correlation, PME＝PHE(ii) a The correlation is low and the correlation is low,
the correlation of (1) is (a) to (b),
the correlation is high, and the correlation is high,
complete correlation, PME＝1；
TABLE 1 degree of human miscorrelation
(3) Determining the occurrence probability of equipment faults and environmental disturbances, and calculating the occurrence probability of abnormal function modes
Obtaining the equipment failure probability P causing a certain abnormal function mode according to the similar product data or statistical dataFAnd the probability of environmental disturbance PECombining the corrected human error occurrence probability P obtained in steps (1) and (2) of step threeMEBy the formula 1- (1-P)F)(1-PE)(1-PME) And calculating the occurrence probability P of the abnormal function mode.
The contents of the "risk priority calculation and latent fault identification" described in step four are described as follows:
(1) carrying out the analysis of the functional abnormal mode influence and calculating the risk priority number
Analyzing local influence, influence on an upper task process and influence on a whole task process (a subsequent task stage) caused by the current abnormal function mode, and evaluating the severity grade of the abnormal function mode according to a table 2; analyzing the number of the detection control methods of the abnormal function modes and whether an alarm exists, and evaluating the detection difficulty level according to a table 3; determining the occurrence probability grade of the abnormal function mode according to the occurrence probability of the abnormal function mode calculated in the step three and the table 4; multiplying the 3 grades to calculate the risk priority number;
TABLE 2 evaluation criteria for severity rating
TABLE 3 abnormal pattern detection and discovery difficulty level evaluation criteria
TABLE 4 probability of occurrence rating Scoring criteria
| Combined probability rating (OPR) | Possibility of occurrence of abnormal pattern | Abnormal pattern occurrence probability Pref range |
| 1 | Extremely low | P≤10-6 |
| 2,3 | Is lower than | 1*10-6<P≤1*10-4 |
| 4,5,6 | Medium grade | 1*10-4<P≤1*10-2 |
| 7,8 | High (a) | 1*10-2<P≤1*10-1 |
| 9,10 | Is very high | P>10-1 |
(2) According to the risk priority number, potential faults with higher priority are checked
When the risk priority level is greater than or equal to 217, the potential fault with higher priority of the abnormal function mode is judged.
3. The efficacy and the advantages of the invention
According to the method, according to the characteristics that the potential faults of the complex man-machine system are influenced by man-machine ring coupling and human error correlation, the potential faults of the man-machine system are analyzed by adopting a PFMEA (pulse frequency membrane electrode) method, the influence of equipment and environment states on the human error in a task scene environment is analyzed by utilizing a CREAM (credit rapid error rate) method, the human error probability is quantized, the correlation of tasks in a task process is analyzed by utilizing a THERP (total harmonic error rate) method, and the problems of man-machine separation and difficulty in accurate quantization existing in the traditional potential fault analysis method are solved; the method for analyzing the potential fault is scientific, has good manufacturability and has wide popularization and application values.
Drawings
FIG. 1 is a flow chart of a latent fault analysis method according to the present invention.
Fig. 2 is a schematic diagram of the result of the task analysis in the mounting task phase of the airborne weapon system according to the present invention.
The foreign language symbols and symbols referred to in this specification are summarized as follows:
process Failure Mode and Effects Analysis, PFMEA — Process Failure Mode and impact Analysis;
human Reliability Analysis, HRA;
failure Mode and Effects Analysis, FMEA-Failure Mode and impact Analysis;
cognitive Reliability Error Analysis Method, CREAM-Cognitive Reliability and failure Analysis Method;
technique for Human Error Rate Prediction, THERP-Human Error probability Prediction Technique;
common Performance Condition, CPC, a Common Performance Condition.
PHE-human error probability; pME-the human error probability after correlation correction; pF-the probability of equipment failure occurrence;
PE-the probability of occurrence of an environmental disturbance; p-probability of occurrence of abnormal pattern of function.
Detailed Description
The invention relates to a latent fault analysis method based on PFMEA and HRA methods, which takes latent fault analysis of a weapon mounting task stage of a certain type of onboard weapon system as an example to explain the process. The airborne weapon system has typical multi-stage task characteristics, which are mainly divided into weapon system maintenance, mounting, hanging, launching and unloading stages. A complex man-machine interaction process exists in a maintenance or mounting stage, and the caused potential fault can be activated in stages of flying or launching and the like, so that a serious accident is finally caused.
The performance of the personnel of the tasks in the maintenance or mounting stage is influenced by the coupling of equipment and environmental states, and the operation actions of the personnel in the process have certain correlation.
The invention relates to a latent fault analysis method based on a PFMEA (pulse frequency membrane electrode assembly) and HRA (high resolution analysis) method, a flow chart of the method is shown in figure 1, and the method comprises the following four steps:
the method comprises the following steps: task analysis
(1) Selecting task stage, defining human-machine ring elements
And selecting a mounting stage to carry out latent fault analysis aiming at the airborne weapon system. The personnel involved in the mission process are mainly ordnance officers and are responsible for hanging and installing weapons and checking weapon systems; the involved machine devices are a weapon system, a mounting device and an inspection device; the weapon mounting task is finished indoors, and the related environmental elements mainly comprise the conditions of influencing human rhythm in the day and at night and the performance of equipment in the electromagnetic environment;
(2) developing task analysis based on hierarchical task analysis method and determining basic task activities
Defining a task target as each electric and mechanical interface connection is reliable and weapons can be launched in the air according to the mounting task and the man-machine ring elements related to the mounting task; collecting and sorting relevant data such as weapon system mounting operation manuals and the like; decomposing the secondary mounting task target into weapon preparation before mounting, hanger inspection, weapon system before mounting, weapon mounting and ground mounting inspection, and finally decomposing to basic activities; and finally, analyzing the task plan, determining that all the operations need to be completed in sequence, and outputting a task analysis table as shown in FIG. 2.
Because the mounting task involves excessive secondary tasks and basic activities, and the analysis table is too large, the invention intercepts the secondary task 5 in fig. 2, namely the ground mounting inspection, to explain the analysis process.
Step two: abnormal pattern and abnormal cause analysis
(1) Developing abnormal pattern analysis to determine the abnormal function pattern of the basic unit
Analyzing possible abnormal patterns of each basic activity from a function level based on basic activity units 5.1-5.7 in fig. 2, which are detailed in table 5;
(2) and (4) carrying out abnormal reason analysis, and determining human errors, equipment faults and environmental disturbance which cause functional abnormality.
Aiming at each function abnormal mode, the reasons causing the abnormal mode are analyzed, wherein the human error mode is confirmed according to a CREAM method, and the details are shown in a table 5.
TABLE 5 mounting phase PFMEA analysis
Step three: quantification of abnormal functional pattern occurrence probability
(1) CREAM method based quantification human error mode occurrence probability, see CREAM method in detail
Quantifying the human error pattern in each abnormal reason in the table 5 based on a CREAM method, and determining the basic value of the occurrence probability of the human error pattern. And looking up a CREAM method human error occurrence probability basic value table to determine that the occurrence probability basic values of action omission and error identification are 0.003 and 0.007 respectively. The CPC factor levels and the weights of the CPC factor levels are determined by referring to a CREAM method CPC factor level and weight table, and the CPC of the basic activity units 5.1-5.7 which are mistakenly taken by people is the same when the mounting task in the case occurs in night battles, as shown in a table 6. The action omission belongs to an execution type error, the total weight factor is 2.4, and the human error probability is 0.0084; the "error identification" belongs to the observation type error, and the total weight factor is 0.021.
TABLE 6 CPC factor levels and weight tables
(2) Determining human error correlation grade based on THERP method, and correcting human error probability
The basic activity units 5.3 and 5.6 are used for the ordnance to place the weapon control switch in the simulation position and the combat position respectively, the operation process is similar, and therefore the human error mode occurrence probability corresponding to 5.6 needs to be corrected. Judging that the human error pattern corresponding to the basic activity unit 5.6 is highly correlated with the human error pattern corresponding to the basic activity unit 5.3 according to the rules of the table 1, if the human error pattern corresponding to the basic activity unit 5.3 has occurredIf the probability of occurrence of human error pattern corresponding to 5.6 is
If the human error pattern corresponding to 5.3 has occurred, the human error pattern occurrence probability corresponding to 5.6 is 0.0084, therefore, the human error pattern occurrence probability corresponding to 5.6 can pass through the formula
Obtained, corrected to 0.01256.
(3) Determining the occurrence probability of equipment faults and environmental disturbances, and calculating the occurrence probability of abnormal function modes
Determining the equipment failure probability of causing the basic active units 5.1-5.7 to fail, and according to experience and statistical data, the failure probability of mechanical products is 10E-6The failure probability of the electronic product is 10E-4. The probability of occurrence of abnormal functional patterns in combination with the probability of occurrence of human error is shown in table 7.
Table 7 probability table of occurrence of abnormal function pattern
Step four: risk priority calculation and latent fault identification
(1) Carrying out fault influence analysis and calculating risk priority number
For the abnormal function mode, the local influence, the influence on the upper layer task process and the influence on the whole task process (the subsequent task stage) caused by the abnormal function mode are analyzed, and the method is shown in table 5. And determining the severity grade and the detection difficulty grade according to a table 3, determining the occurrence probability grade based on the abnormal mode occurrence probability and the table 4, and finally multiplying the 3 grades to calculate the risk priority number, wherein the specific numerical values are detailed in a table 5.
(2) According to the risk priority number, potential faults with higher priority are checked
The functional abnormality mode risk priorities corresponding to the basic active units 5.3, 5.4, 5.5 are 288, 336, 216, respectively, and are determined as potential faults. The potential faults of the airborne weapon system hanging in stages are therefore: the weapon system is not switched to the simulation mode, and the thermal battery is activated by mistake; the failure of the sending and controlling circuit is not found; the test of the transmission function is not performed completely.
Claims (1)
1. A latent fault analysis method based on PFMEA and HRA methods is characterized in that: the method comprises the following five steps:
the method comprises the following steps: and (3) task analysis: selecting a human-computer system to be researched, defining a task stage, completing task analysis of the stage by using a hierarchical task analysis method, and determining various basic activities required by task completion;
step two: abnormal pattern and abnormal cause analysis: determining a function abnormal mode of basic activities according to a task analysis result, and carrying out human-machine system abnormal reason analysis in 3 aspects of human, machine and ring, wherein the human-machine system abnormal reason analysis comprises human errors, equipment faults and environmental disturbance;
step three: quantifying the occurrence probability of the abnormal function mode: quantifying human error occurrence probability based on a CREAM method, determining human error correlation based on a THERP method, correcting the occurrence probability of human errors with correlation, and calculating the occurrence probability of the abnormal function mode according to the human error probability, equipment fault probability and environmental disturbance probability;
step four: risk priority calculation and latent fault identification: carrying out influence analysis and detection difficulty analysis on the potential abnormal function mode, determining the severity grade and the detection difficulty grade of the potential abnormal function mode, determining the occurrence probability grade based on the occurrence probability of the functional abnormal function mode, obtaining the risk priority number of the potential fault, completing PFMEA (pulse frequency membrane electrode assembly) form filling and identifying the potential fault;
in the first step:
(1) selecting a task stage, and determining human-machine ring elements;
aiming at a multi-task stage human-computer system, selecting a task stage and developing human-computer ring elements to clarify personnel, equipment and environment involved in a task process;
(2) performing task analysis based on a hierarchical task analysis method to determine basic activities of the tasks;
according to a hierarchical task analysis method manual, sequentially developing the planning steps of defining tasks, collecting data, determining the overall target of the tasks, determining the secondary target of the tasks, decomposing and analyzing the secondary target, and determining the basic activities required by the tasks;
in the second step:
(1) carrying out abnormal mode analysis to determine the abnormal function mode of the basic unit;
analyzing abnormal patterns which may appear in each basic activity from a functional level by taking the basic activities of people as a unit on the basis of task analysis;
(2) carrying out abnormal reason analysis to clearly cause human errors, equipment faults and environmental disturbance of functional abnormality;
aiming at the abnormal mode of the function of the man-machine system, carrying out abnormal reason analysis from 3 aspects of man, machine and ring, including human error, equipment fault and environmental disturbance; wherein, human error analysis is carried out based on a CREAM method; the CREAM method generalizes the cognitive functions of people into 4 types of observation, explanation, planning and execution, wherein each type of cognitive function comprises a plurality of personal error modes including error observation target and error identification;
in the third step:
(1) quantifying the occurrence probability of human error modes based on a CREAM method;
analyzing each personal error pattern, and determining the cognitive function error pattern and the error probability basic value which are most likely to occur in each cognitive activity; evaluating the scene environment of each cognitive activity, considering the influence of equipment and environment on the cognitive function of people in the scene environment, evaluating the levels of CPCs (cognitive pilot channel) of 9 common performance conditions, determining weights, and multiplying the weights of the levels of the CPCs of 9 types to obtain a total weight factor; multiplying the basic value of the error probability by the total weight factor to calculate the probability of human error occurrence;
(2) determining a human error correlation grade based on a THERP method, and correcting the human error probability;
according to the personnel composition of the two tasks, the task time interval, the task repeatability and the presence or absence of prompt information, making corresponding relevance degree judgment on the rules of the task corresponding table 1 of the inspectors; then, the human error probability P is corrected based on the correlation formulaHE: zero correlation, PME＝PHE(ii) a The correlation is low and the correlation is low,
the correlation of (1) is (a) to (b),
the correlation is high, and the correlation is high,
complete correlation, PME＝1；
TABLE 1 degree of human miscorrelation
(3) Determining the occurrence probability of equipment faults and environmental disturbance, and calculating the occurrence probability of the abnormal function mode;
obtaining the equipment failure probability P causing a certain abnormal function mode according to the similar product data or statistical dataFAnd the probability of environmental disturbance PECombining the corrected human error occurrence probability P obtained in steps (1) and (2) of step threeMEBy the formula 1- (1-P)F)(1-PE)(1-PME) Calculating the occurrence probability P of the abnormal function mode;
wherein, in the fourth step:
(1) carrying out functional abnormality mode influence analysis and calculating risk priority;
analyzing local influence, influence on an upper task process and influence on a whole task process caused by the current abnormal function mode, and evaluating the severity grade of the abnormal function mode according to a table 2; analyzing the number of the detection control methods of the abnormal function modes and whether an alarm exists, and evaluating the detection difficulty level according to a table 3; determining the occurrence probability grade of the abnormal function mode according to the occurrence probability of the abnormal function mode calculated in the step three and the table 4; multiplying the 3 grades to calculate the risk priority number;
TABLE 2 evaluation criteria for severity rating
TABLE 3 abnormal pattern detection and discovery difficulty level evaluation criteria
TABLE 4 probability of occurrence rating Scoring criteria
(2) According to the risk priority number, potential faults with higher priority are checked out;
when the risk priority level is greater than or equal to 217, the potential fault with higher priority of the abnormal function mode is judged.
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| CN112288291A (en) * | 2020-11-03 | 2021-01-29 | 集美大学 | Ship pilot human factor reliability prediction method based on improved CREAM |
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