CN111291452B - Ship electric propulsion system fault mode risk determination method and system - Google Patents
Ship electric propulsion system fault mode risk determination method and system Download PDFInfo
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Abstract
The invention discloses a fault mode risk determining method and system for a ship electric propulsion system, and relates to the field of fault mode and influence analysis. The method comprises the following steps: determining a fault mode and a risk factor through a functional analysis method, a hierarchical model of the ship electric propulsion system and FMEA; and calculating risk factors and weights thereof by using expert knowledge experience, fuzzy language term sets and fuzzy number theory to obtain fuzzy risk priority numbers, and after defuzzification, performing risk priority sequencing on fault modes to obtain a fault mode risk sequence table. According to the method, through introducing the weight of the risk factors and the fuzzy risk priority number, the assessment defect that the fuzzy risk priority number is the same can be effectively avoided, the sensitivity of risk factor change to fault mode risk sequencing is reduced, the dependence and subjectivity of expert knowledge experience are reduced, the accuracy of fault mode risk sequencing of the ship electric propulsion system is improved, and meanwhile subsequent maintenance of key equipment is facilitated.
Description
Technical Field
The invention relates to the field of fault mode and influence analysis, in particular to a fault mode risk determining method and system for a ship electric propulsion system.
Background
The ship electric propulsion system can supply power for various loads such as a propulsion system, regional loads, high-energy weapons and the like, integrates ship propulsion power consumption with other load power consumption, and has the advantages of reducing the weight and the volume of a power device, improving the operation reliability of the system, being convenient for comprehensive energy utilization, unified management and the like. The next generation of ship medium voltage direct current electric propulsion system can better exert regional power distribution advantages, meet the application requirements of high-power propulsion and high-energy weapons of all-electric ships, have the remarkable advantages of high power density, greatly reduced vibration noise, improved ship fight force and vitality, reduced full life cycle use and maintenance cost and the like, and become a preferred power form for the development of future ships. The ship electric propulsion system belongs to a large-scale system with high integration, complicated structure, diversified functions and control, has a severe offshore running environment, and generally has the characteristics of large temperature change, strong mechanical impact, high humidity, corrosion and explosive gas accompanying salt mist, oil mist, mold and the like. Therefore, the complex structure and working environment of the medium voltage direct current electric propulsion system of the ship can cause the increase of the failure rate of the system and serious safety threat, which provides great challenges for the risk control and safe and reliable operation of the electric propulsion system.
The failure mode and influence analysis is a system function structure optimization design method for improving safety and reliability, the severity, occurrence probability and detectable degree of each failure mode are evaluated by adopting a risk priority number technology, and measures such as maintenance, improvement and control are provided for key equipment according to the priority level ranking of the failure risks. However, the conventional risk priority technique is applied to failure modes and influence analysis of safety demanding systems such as ship electric propulsion, and has the following defects: the traditional risk priority is the same to the fault severity, occurrence probability and risk factor weight of the detectable measure by default, and does not accord with the actual situation of the safety demanding system; the traditional risk factor index description is expressed by adopting an accurate value, and seriously depends on expert knowledge experience and a system fault database, and does not meet the requirements of new development systems such as middle voltage direct current electric propulsion of ships. Therefore, the existing failure mode and influence analysis method has the problems of inconformity with actual conditions and great subjective influence, and influences the determination of the failure mode risk of the ship electric propulsion system, thereby influencing the subsequent maintenance of key equipment.
Disclosure of Invention
The invention aims to provide a fault mode risk determining method and system for a ship electric propulsion system, which solve the problems that the existing failure mode and influence analysis method are not in line with actual conditions and have large subjective influence, improve the accuracy of fault mode risk sequencing of the ship electric propulsion system, and facilitate subsequent maintenance of key equipment.
In order to achieve the above object, the present invention provides the following solutions:
a method of determining risk of failure modes of an electric propulsion system of a vessel, comprising:
acquiring a hierarchical model of a ship electric propulsion system;
determining a device with a functional failure in the ship electric propulsion system through a functional analysis method and the hierarchical model;
determining a fault mode of the ship electric propulsion system and a risk factor of the fault mode by using the equipment with the failed function and the failure mode and influence analysis;
calculating the risk factors and the weights of the risk factors by adopting expert knowledge experience, fuzzy language term sets and fuzzy number theory to obtain fuzzy risk priority numbers of the fault modes;
performing defuzzification on the fuzzy risk priority number by using a centroid method and an alpha-cut set theory to obtain a defuzzification value of the fuzzy risk priority number;
according to the disambiguation value, performing risk priority ranking on the fault modes by adopting a decision test and evaluation laboratory method to obtain a fault mode risk sequence table; the failure mode risk sequence list is used for checking and maintaining the ship electric propulsion system.
Optionally, the hierarchical model includes: an initial contract level, a contract level, and a lowest contract level; the initial appointment level is the ship comprehensive electric propulsion system;
The contract hierarchy includes: a power generation subsystem, a propulsion subsystem, a power distribution subsystem, an energy storage subsystem, and a residual load subsystem;
the lowest appointment hierarchy includes: gas turbine, diesel engine, synchronous motor, permanent magnet synchronous motor, screw, panel, battery and transformer module.
Optionally, calculating the risk factor and the weight of the risk factor by using expert knowledge experience, a fuzzy language term set and a fuzzy number theory to obtain the fuzzy risk priority of the fault mode, which specifically includes:
acquiring the expert knowledge experience; the expert knowledge experience includes: expert opinion of the failure mode;
calculating the credibility of the expert opinion by utilizing an information entropy theory and qualitative analysis;
calculating the risk factors and the weights of the risk factors by using the credibility, the fuzzy language term set and the fuzzy number theory to obtain a fuzzy comprehensive value; the fuzzy comprehensive value is a fuzzy comprehensive evaluation value of the risk factor and the weight of the risk factor;
and calculating a fuzzy weighted geometric mean of risk factors of the fault mode according to the fuzzy comprehensive value, and determining the fuzzy weighted geometric mean as a fuzzy risk priority number of the fault mode.
Optionally, the performing defuzzification on the fuzzy risk priority number by using a centroid method and an α -cutset theory to obtain a defuzzification value of the fuzzy risk priority number, which specifically includes:
calculating an alpha-cut set of the fuzzy risk priority number by using a benchmark adjustment search algorithm;
determining a fuzzy membership function of the fuzzy risk priority number according to the alpha-cut set;
and calculating to obtain a solution ambiguity value by using a centroid method and the fuzzy membership function.
Optionally, according to the disambiguation value, performing risk prioritization on the fault mode by adopting a decision test and evaluation laboratory method to obtain a fault mode risk sequence table, which specifically includes:
acquiring a fault reason;
by using the saidResolving ambiguous values, constructing an initial direct influence relationship matrix between the failure mode and the failure cause
Wherein d vw Elements representing the v-th row and w-th column of the initial direct influence relationship matrix D; v represents the row number of the initial direct influence relation matrix D, v E M+B; w represents the column number of the initial direct influence relation matrix D, w is E M+B; d (D) 11 Representing the influence relation matrix of all the fault reasons on all the fault reasons, D 12 Representing the relation matrix of the influence of all the fault reasons on all the fault modes, D 21 Representing the influence relation matrix of all the fault modes on all the fault reasons, D 22 Representing an influence relation matrix of all the fault modes on all the fault modes; m represents the total number of the fault reasons; b represents the total number of failure modes; d (D) 12 The element in (a) is the disambiguation value; />
Normalizing the initial direct influence relation matrix to obtain a relative direct influence relation matrix
Wherein (1)>
Representing said relative direct influence relation matrix +.>
The elements of row v and column w,
using the relative direct influence relationship matrix according to the formula
Calculating a comprehensive influence matrix C of the fault mode and the fault cause; wherein E represents an identity matrixThe method comprises the steps of carrying out a first treatment on the surface of the Kappa represents a positive integer; c vw Elements representing the v-th row and w-th column of the comprehensive influence matrix C;
using the comprehensive influence matrix, according to the formula
Calculating an associated influence degree matrix C of the fault mode and the fault reason r (v) And an associated influence matrix C of the failure mode and the failure cause u (w)；
Using the associated influence matrix and the associated influence matrix according to the formula rea=c r (v)-C u (w) calculating a cause degree Rea of the failure mode and the failure cause;
and carrying out risk priority ranking on the fault modes according to the cause degree to obtain a fault mode risk sequence list.
A marine vessel electric propulsion system failure mode risk determination system, comprising:
the hierarchical model module is used for acquiring a hierarchical model of the ship electric propulsion system;
the functional failure module is used for determining equipment with functional failure in the ship electric propulsion system through a functional analysis method and the hierarchical model;
the risk factor module is used for determining a fault mode of the ship electric propulsion system and a risk factor of the fault mode by using the equipment with the failed function and failure mode and influence analysis;
the fuzzy risk priority number module is used for calculating the risk factors and the weights of the risk factors by adopting expert knowledge experience, fuzzy language term sets and fuzzy number theory to obtain fuzzy risk priority numbers of the fault modes;
the fuzzy value resolving module is used for resolving the fuzzy risk priority number by utilizing a centroid method and an alpha-cut set theory to obtain a fuzzy value of the fuzzy risk priority number;
The risk priority ordering module is used for performing risk priority ordering on the fault modes by adopting a decision test and evaluation laboratory method according to the disambiguation value to obtain a fault mode risk sequence table; the failure mode risk sequence list is used for checking and maintaining the ship electric propulsion system.
Optionally, the hierarchical model includes: an initial contract level, a contract level, and a lowest contract level; the initial appointment level is the ship comprehensive electric propulsion system;
the contract hierarchy includes: a power generation subsystem, a propulsion subsystem, a power distribution subsystem, an energy storage subsystem, and a residual load subsystem;
the lowest appointment hierarchy includes: gas turbine, diesel engine, synchronous motor, permanent magnet synchronous motor, screw, panel, battery and transformer module.
Optionally, the fuzzy risk priority module specifically includes:
an acquisition unit for acquiring the expert knowledge experience; the expert knowledge experience includes: expert opinion of the failure mode;
the credibility unit is used for calculating the credibility of the expert opinion by utilizing an information entropy theory and qualitative analysis;
the fuzzy comprehensive evaluation unit is used for calculating the risk factors and the weights of the risk factors by utilizing the credibility, the fuzzy language term set and the fuzzy number theory to obtain a fuzzy comprehensive value; the fuzzy comprehensive value is a fuzzy comprehensive evaluation value of the risk factor and the weight of the risk factor;
And the fuzzy risk priority number unit is used for calculating a fuzzy weighted geometric mean of risk factors of the fault mode according to the fuzzy comprehensive value, and determining the fuzzy weighted geometric mean as the fuzzy risk priority number of the fault mode.
Optionally, the disambiguation value module specifically includes:
the alpha-cut set unit is used for calculating the alpha-cut set of the fuzzy risk priority number by utilizing a reference adjustment search algorithm;
the fuzzy membership function unit is used for determining a fuzzy membership function of the fuzzy risk priority number according to the alpha-cut set;
and the fuzzy value solving unit is used for calculating and obtaining a fuzzy value by utilizing a centroid method and the fuzzy membership function.
Optionally, the risk prioritization module specifically includes:
the fault reason unit is used for acquiring a fault reason;
an initial direct influence relation matrix unit for constructing an initial direct influence relation matrix between the failure mode and the failure cause by using the disambiguation value
Wherein d vw Elements representing the v-th row and w-th column of the initial direct influence relationship matrix D; v represents the row number of the initial direct influence relation matrix D, v E M+B; w represents the column number of the initial direct influence relation matrix D, w is E M+B; d (D) 11 Representing the influence relation matrix of all the fault reasons on all the fault reasons, D 12 Representing the relation matrix of the influence of all the fault reasons on all the fault modes, D 21 Representing the influence relation matrix of all the fault modes on all the fault reasons, D 22 Representing an influence relation matrix of all the fault modes on all the fault modes; m represents the total number of the fault reasons; b represents the total number of failure modes; d (D) 12 The element in (a) is the disambiguation value;
a relative direct influence relation matrix unit for normalizing the initial direct influence relation matrix to obtain a relative direct influence relation matrix
Wherein (1)>
Representing said relative direct influence relation matrix +.>
V th row and w th column of the middleElement(s) of->
A comprehensive influence matrix unit for using the relative direct influence relation matrix according to the formula
Calculating a comprehensive influence matrix C of the fault mode and the fault cause; wherein E represents an identity matrix; kappa represents a positive integer; c vw Elements representing the v-th row and w-th column of the comprehensive influence matrix C;
a correlation influence matrix unit for using the comprehensive influence matrix according to the formula 
Calculating an associated influence degree matrix C of the fault mode and the fault reason r (v) And an associated influence matrix C of the failure mode and the failure cause u (w)；
A cause degree unit for using the associated influence degree matrix and the associated influence degree matrix according to the formula rea=c r (v)-C u (w) calculating a cause degree Rea of the failure mode and the failure cause;
and the risk priority ranking unit is used for performing risk priority ranking on the fault modes according to the cause degree to obtain a fault mode risk sequence list.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a fault mode risk determination method and system for a ship electric propulsion system. The method comprises the following steps: acquiring a hierarchical model of a ship electric propulsion system; determining equipment with functional failure in the ship electric propulsion system through a functional analysis method and a hierarchical model; determining a fault mode and a risk factor of the fault mode of the ship electric propulsion system by using equipment with a functional failure, a failure mode and influence analysis; calculating risk factors and weights of the risk factors by adopting expert knowledge experience, fuzzy language term sets and fuzzy number theory to obtain fuzzy risk priority numbers of fault modes; defuzzifying the fuzzy risk priority number by using a centroid method and an alpha-cut set theory to obtain a defuzzified value of the fuzzy risk priority number; according to the ambiguity resolution value, carrying out risk priority ranking on the fault modes by adopting a decision test and evaluation laboratory method to obtain a fault mode risk sequence table; the failure mode risk sequence list is used for checking and maintaining the ship electric propulsion system. The method adopts a functional analysis method and a hierarchical model to perform preliminary analysis on the functional failure of the ship electric propulsion system level, so that not only can the hardware failure mode of key equipment be considered, but also the functional failure mode of the ship electric propulsion system can be analyzed in a focusing manner; the hierarchical structure of the hierarchical model determines the detail degree of failure mode and impact analysis (Failure Mode and Effects Analysis, FMEA) implementation, and can analyze, track and count the interaction of failure modes of the ship electric propulsion system and the hierarchical propagation of failure causes. According to the method, the weight of the risk factors and the fuzzy weighted geometric mean are introduced, so that the severity of the risk factors and the attention degree with higher occurrence probability can be given, the assessment defect that fuzzy risk priority values are the same is effectively avoided, the sensitivity of risk factor change to fault mode risk sequencing is reduced, the loss of risk information in the traditional risk priority number analysis process is avoided, the dependence and subjectivity of the fault mode on expert knowledge experience are reduced, the accuracy of fault mode risk sequencing of a ship electric propulsion system is improved, the actual situation of a ship electric propulsion and other safety demanding system is more met, and subsequent maintenance of key equipment is facilitated. The decision test and evaluation laboratory method of the method considers the correlation influence among a plurality of fault modes, a plurality of fault reasons and the fault modes and the fault reasons, can process a complex fault mechanism caused by a plurality of reasons and a common cause fault phenomenon caused by a plurality of faults, gives higher priority to the common cause fault modes, always focuses on the fault mode with higher risk degree, accords with the practical engineering application requirements, and can improve the accuracy of the risk sequencing of the fault modes of the ship electric propulsion system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a fault mode risk determination method for an electric propulsion system of a ship according to an embodiment of the present invention;
FIG. 2 is a hierarchical model diagram provided by an embodiment of the present invention;
FIG. 3 is a piecewise linear function diagram of fuzzy risk priority provided by an embodiment of the present invention;
fig. 4 is a block diagram of a fault mode risk determining system of an electric propulsion system of a ship according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a fault mode risk determining method and system for a ship electric propulsion system, which solve the problems that the existing failure mode and influence analysis method are not in line with actual conditions and have large subjective influence, improve the accuracy of fault mode risk sequencing of the ship electric propulsion system, and facilitate subsequent maintenance of key equipment.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
The embodiment provides a fault mode risk determination method for a ship electric propulsion system, and fig. 1 is a flowchart of the fault mode risk determination method for the ship electric propulsion system provided by the embodiment of the invention. The ship electric propulsion system is specifically an all-electric ship medium-voltage direct-current propulsion system. Referring to fig. 1, the ship electric propulsion system failure mode risk determination method includes:
Aiming at the typical fault mode and influence analysis of the full-power ship medium-voltage direct-current propulsion system, a top-down structural decomposition idea, namely a system-subsystem-device is adopted to decompose the ship electric propulsion system into a plurality of subsystems, and the subsystems are divided into a plurality of key devices. Based on the logic induction principle of cause and effect from bottom to top, the typical fault modes of key equipment such as a converter, a sensor and the like in the ship electric propulsion system are taken as the cut-in points, the system level is gradually tracked upwards, and the fault reasons of main components and the influence of the fault reasons on the level, the previous level and the whole system are analyzed.
The degree of detail in which failure modes and impact analysis are implemented depends on the hierarchical structure of the described hierarchical model. In order to facilitate fault mode analysis and system boundary definition, functional structure hierarchy division is carried out on the ship electric propulsion system according to an initial convention level, a convention level and a lowest convention level according to GJB/Z1391-2006 fault mode, influence and hazard analysis guidelines and U.S. military standard MIL-STD-1629A. Fig. 2 is a hierarchical model diagram provided in an embodiment of the present invention, referring to fig. 2, the hierarchical model includes: an initial contract level, a contract level, and a lowest contract level. The initial contract level is the ship comprehensive electric propulsion system. The contract hierarchy includes: a power generation subsystem, a propulsion subsystem, a power distribution subsystem, an energy storage subsystem, and a residual load subsystem. The lowest contract level includes: the system comprises a gas turbine, a diesel engine, a synchronous motor, an excitation voltage regulator, a rotating speed controller of a power generation subsystem, a diode rectifier, a permanent magnet synchronous motor, a propeller, a PWM (Pulse width modulation) inverter, a rotating speed controller of a propulsion subsystem, a distribution panel, a switch cabinet, a power transformation module of a power distribution subsystem, a cable, a super capacitor, a storage battery, a bidirectional DC/DC (Direct Current) converter, a power transformation module of a residual load subsystem, a daily load and a pulse load. It should be noted that this embodiment does not consider lower level structural decomposition and device-level failure analysis. In addition, the hierarchical structure of the hierarchical model determines the degree of detail in which failure modes and impact analysis (Failure Mode and Effects Analysis, FMEA) are implemented, enables analysis, tracking and statistics of interactions of typical failure modes of a marine electric propulsion system and hierarchical propagation of failure causes, and facilitates tracking and locating failure modes and examining interactions between failure modes and failure causes.
And 102, determining the equipment with the function failure in the ship electric propulsion system through a function analysis method and a hierarchical model. The functional analysis method is specifically adopted to determine the key equipment with functional failure in the hierarchical model according to different system functions.
And step 103, determining a fault mode and a risk factor of the fault mode of the ship electric propulsion system by using the equipment with the function failure and the failure mode and influence analysis. The risk factors include: severity, probability of occurrence, and detectable level.
And 104, calculating the risk factors and the weights of the risk factors by adopting expert knowledge experience, fuzzy language term sets and fuzzy number theory to obtain fuzzy risk priority numbers of the fault modes. In a real ship electric propulsion system, it is often difficult to accurately define and divide importance degrees of all risk factors of a fault mode, so that fuzzy contexts are introduced to improve a traditional risk priority assessment mechanism, information ambiguity and uncertainty of all risk factors are considered by combining expert knowledge experience, and the risk factors and weights of the risk factors are assessed by adopting a fuzzy language term set and a fuzzy number theory. And calculating the fuzzy weighted geometric mean of the risk factors of each fault mode according to the fuzzy comprehensive value of each risk factor and the weight thereof to obtain the fuzzy risk priority of the typical fault mode of the ship electric propulsion system.
Step 104 specifically includes:
acquiring expert knowledge experience; expert knowledge experience includes: expert opinion of failure mode.
And calculating the credibility of the expert opinion by utilizing the information entropy theory and the qualitative analysis. The method specifically comprises the following steps: the fault mode influence assessment team has V expert members, and the credibility weight of the expert opinion is h j Where j=1, 2, the above-mentioned components of the present invention, V, the confidence weight is used as the confidence. In the existing research, qualitative analysis is generally adopted to determine the credibility weight of the expert opinion, so that the evaluation result is not objective and accurate enough. Therefore, the embodiment adopts a comprehensive weight distribution method based on the combination of the information entropy theory and the qualitative analysis to determine the reliability weight of the expert, calculates the information entropy value and the entropy weight of each expert according to the scoring values of the expert members on the 3 performance indexes of the working experience, the technical field and the expertise level, and calculates the information entropy value of each expert according to the formula (1):
wherein r is jk A scoring value of the jth expert on the kth performance index; r is (r) j Pi is the total score value of the jth expert jk Represents the percentage of the score value of the jth expert in the kth performance index to the total score value, H (r j ) The information entropy value of the jth expert.
Calculating the total entropy value and entropy weight of the expert opinion according to the formula (2):
wherein E is e Representing the total entropy value of expert opinion; θ j And the entropy weight of the j-th expert opinion is represented.
Obtaining qualitative weight χ obtained by qualitative analysis j Qualitative analysis considers evaluation environment, membership and familiarity with the ship's electric propulsion system. Calculating the credibility weight of each expert opinion according to the formula (3), wherein the credibility weight is in a normalized form of the sum of the entropy weight and the qualitative weight:
calculating the weight of the risk factors by using the credibility, the fuzzy language term set and the fuzzy number theory to obtain a fuzzy comprehensive value; the fuzzy comprehensive value is a fuzzy comprehensive evaluation value of the risk factor and the weight of the risk factor.
And determining a fuzzy language term set and language description of the risk factors by referring to fuzzy evaluation criteria of the risk factors in the ship industry and combining the characteristics of the ship electric propulsion system, wherein the language description of the risk factors adopts a trapezoidal fuzzy number, and the language description of the weights of the risk factors adopts a triangular fuzzy number to represent. The fuzzy evaluation criteria and corresponding fuzzy numbers of each risk factor and the weight thereof are shown in tables 1-4.
TABLE 1 severity fuzzy evaluation criteria
TABLE 2 probability of occurrence fuzzy evaluation criteria
TABLE 3 detectability fuzzy evaluation criteria
TABLE 4 risk factor weight fuzzy evaluation criteria
Calculating risk factors and weights of the risk factors by using credibility, a fuzzy language term set and a fuzzy number theory to obtain fuzzy comprehensive values, wherein the fuzzy comprehensive values comprise fuzzy comprehensive values of each risk factor and fuzzy comprehensive values of weights of each risk factor: the method specifically comprises the following steps: calculating a fuzzy comprehensive value of each risk factor of the ith fault mode according to formula (4):
in the above, S i The trapezoidal blur number evaluation value for the severity of the ith failure mode, i.e. the blur integrated value for the severity,
evaluation of the number of ladder ambiguities for the severity of the ith failure mode for the jth expert,/>
First numerical value representing the evaluation of the number of trapezoidal ambiguities by the jth expert on the severity of the ith failure mode,/>
A second value representing the evaluation of the number of trapezoidal ambiguities by the jth expert on the severity of the ith failure mode,/>
Third numerical value representing the evaluation of the number of trapezoidal ambiguities by the jth expert on the severity of the ith failure mode, +.>
A fourth numerical value representing the evaluation of the number of trapezoidal ambiguities by the jth expert on the severity of the ith failure mode; o (O) i A step-like fuzzy number evaluation value for occurrence probability of the ith failure mode, namely fuzzy integrated value of occurrence probability, < ->
For the j-th expert, evaluating the number of trapezoidal ambiguities of the occurrence probability of the i-th failure mode,/>
First numerical value representing the evaluation of the number of trapezoidal ambiguities of the occurrence probability of the ith failure mode by the jth expert,/>
A second value representing the evaluation of the number of trapezoidal ambiguities of the occurrence probability of the ith failure mode by the jth expert,/>
Third numerical value representing evaluation of the number of ladder ambiguities of occurrence probability of the ith failure mode by the jth expert, +.>
A fourth numerical value representing the evaluation of the number of trapezoidal ambiguities of the occurrence probability of the ith failure mode by the jth expert; d (D) i A step of evaluating the number of the trapezoidal blur which is the detectable level of the ith fault mode, namely, a fuzzy comprehensive value of the detectable level,>
evaluation of the number of ladder ambiguities for the detectable level of the jth expert on the ith failure mode, for>
A first numerical value representing the evaluation of the number of trapezoidal ambiguities of the jth expert for the detectable degree of the ith failure mode,
a second value representing the evaluation of the number of trapezoidal ambiguities of the jth expert for the detectable level of the ith failure mode,/->
Third numerical value representing evaluation of the number of ladder ambiguities of the detectable degree of the jth expert on the ith failure mode,/ >
And a fourth value representing the evaluation of the number of trapezoidal ambiguities of the detectable degree of the ith failure mode by the jth expert.
Calculating a fuzzy comprehensive value of each risk factor weight according to a formula (5):
in the above, w S The triangular fuzzy number evaluation value of the severity weight for the ith failure mode, i.e. the fuzzy comprehensive value of the severity weight,
triangle ambiguity assessment for severity weight of jth expert on ith failure mode,/>
First numerical value representing the triangle fuzzy number evaluation of the severity weight by the jth expert,/>
Second numerical value representing the triangle fuzzy number evaluation of the severity weight by the jth expert,/>
A third numerical value representing a triangle fuzzy number evaluation of the severity weight by the jth expert; w (w) O Triangle fuzzy number evaluation value of occurrence probability weight of ith fault mode, namely fuzzy comprehensive value of occurrence probability weight,/>
For the jth expert to the ith failure modeTriangle ambiguity number evaluation of occurrence probability weight, +.>
First numerical value representing the evaluation of the triangle blur number of the occurrence probability weight by the jth expert,/>
Second numerical value representing the evaluation of the triangle blur number of the occurrence probability weight by the jth expert,/ >
A third numerical value representing the triangular fuzzy number evaluation of the occurrence probability weight by the jth expert; w (w) D Triangle fuzzy number evaluation value of the detectable degree weight of the ith fault mode, namely fuzzy comprehensive value of the detectable degree weight, +.>
Triangle ambiguity assessment for the jth expert on the detectability weight of the ith failure mode, +.>
First numerical value representing the triangular blur number evaluation of the j-th expert on the detectability weight,/->
A second value representing the triangular blur number evaluation of the detectable degree weight by the jth expert,/>
And a third numerical value representing the triangular blur number evaluation of the detectability weight by the jth expert.
And calculating a fuzzy weighted geometric mean of risk factors of the fault mode according to the fuzzy comprehensive value, and determining the fuzzy weighted geometric mean as a fuzzy risk priority number (Fuzzy Risk Priority Number, FRPN) of the fault mode. The method specifically comprises the following steps: calculating a fuzzy risk priority FRPN according to formula (6):
wherein R= [ R ] 1 R 2 R 3 ]＝[S i O i D i ]R is used in this embodiment 1 Instead of S i ，R 2 Instead of O i ，R 3 Instead of D i ；w＝[w 1 w 2 w 3 ]＝[w S w O w D ]In this embodiment, w is 1 Instead of w S ，w 2 Instead of w O ，w 3 Instead of w D 。
After the identity transformation processing is carried out on the formula (6), the fuzzy risk priority number of the ith fault mode is as follows:
wherein R is λ The trapezoidal fuzzy number is a fuzzy risk factor, namely the risk factor; w (w) λ Is a fuzzy risk factor weight, i.e. a triangular fuzzy number of risk factor weights, λ=1, 2,3.
And 105, defuzzifying the fuzzy risk priority number by using a centroid method and an alpha-cut set theory to obtain a defuzzified value of the fuzzy risk priority number.
Step 105 specifically includes: due to R λ And w λ The calculation cannot be directly performed in the equation, and the embodiment introduces an alpha-cut set theory, and the alpha-cut set of the fuzzy risk priority number is expressed as:
where α represents a confidence level;
an alpha-cut set representing a fuzzy risk priority; />
A lower limit value of an alpha-cut set representing a fuzzy risk priority number; />
An upper limit value of an alpha-cut set representing a fuzzy risk priority number; />
Lower limit value of alpha-cut set of triangle fuzzy number evaluation representing weight of each risk factor, ++>
Lower limit value of alpha-cut set representing ladder ambiguity number evaluation of each risk factor, ++>
An upper limit value of a-cut set of triangle fuzzy number evaluation representing each risk factor weight, ++>
The upper limit value of the alpha-cut set representing the trapezoidal blur number evaluation of each risk factor.
The upper and lower limits of the alpha-cut set for fuzzy number evaluation of the 3 risk factors and their weights can be expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
alpha-cut set, +.a.assessed for fuzzy number of 3 risk factors >
An alpha-cut set for fuzzy number evaluation of 3 risk factor weights; />
The first value representing the evaluation of the trapezoidal blur number of each risk factor, namely +.>
The second value representing the evaluation of the trapezoidal blur number of each risk factor, namely
A third value representing the evaluation of the trapezoidal blur number of each risk factor, namely
A fourth value representing the evaluation of the trapezoidal blur number of each risk factor, namely
The first value representing the evaluation of the triangular fuzzy number of each risk factor weight, namely
The second value representing the evaluation of the triangular fuzzy number of each risk factor weight, namely
Third numerical value representing the evaluation of the triangular fuzzy number of each risk factor weight, namely
And calculating an alpha-cut set of fuzzy risk priority numbers by using a benchmark adjustment search algorithm. The method specifically comprises the following steps:
step 1: the confidence level alpha is evenly and discretely distributed at the intervals of [0,1 ]]Within the range, i.e. confidence level set is a= { α 0 ,α 1 ,...,α N -wherein alpha 0 ＝0，α N ＝1，α 0 ＜α 1 ＜…＜α N And (2) andsatisfying the interval difference Δα=α l+1 -α l =1/N, N represents the total number of elements in the confidence level set, α l Represents the first element in the confidence level set, l represents the sequence number of any element in the confidence level set, l=0, 1.
Step 2: let α=α 0 Searching for the fuzzy risk factor R λ Alpha-cutset of (C)
Fuzzy risk factor weight w λ Alpha-cutset->
Where λ=1, 2,3.
Step 3: calculating an initial reference value according to formula (11):
wherein eta is 0 An initial reference value representing a lower limit of an alpha-cut set of fuzzy risk priority numbers; ρ 0 An initial reference value representing an upper limit of an alpha-cut set of fuzzy risk priority numbers;
step 4: adjusting the reference value eta p Calculating the lower limit value of the alpha-cut set of the fuzzy risk priority number according to the formula (12):
wherein eta is p Reference value η representing lower limit of a-cut set of fuzzy risk priority number p＝1 Reference value η representing lower limit of a-cut set of fuzzy risk priority number when p=1 p≥2 Indicating fuzzy risk priority number when p is more than or equal to 2A reference value for the lower limit of the α -cutset; i 0 The lower limit value of the alpha-cut set for the fuzzy risk factor is smaller than eta 0 Is used for the collection of lambda values of (c),
I＝{1,2,3}；ΔI p-1 representing set I p-2 And set I p-1 Is a complement of (a); p represents a positive integer greater than zero.
When (when)
Satisfying the optimal test, determine->
Otherwise, returning to Step 4 to continue calculation; ΔI p Representing set I p-1 And set I p Complement of DeltaI p ＝I p-1 \I p ；I p The lower limit value of the alpha-cut set for the fuzzy risk factor is smaller than eta p Lambda value set of->
I p-1 The lower limit value of the alpha-cut set representing the fuzzy risk factor is smaller than eta in logarithm p-1 Lambda value set of I p-2 The lower limit value of the alpha-cut set representing the fuzzy risk factor is smaller than eta in logarithm p-2 Is set of lambda values of (c).
Step 5: adjusting the reference value ρ q Calculating an upper limit value of the alpha-cut set for calculating the fuzzy risk priority according to the formula (13):
wherein ρ is q Reference value ρ representing upper limit of α -cut set of fuzzy risk priority number q＝1 Reference value, ρ, representing the lower limit of the α -cut set of the fuzzy risk priority number when q=1 q≥2 A reference value representing the lower limit of the α -cut set of the fuzzy risk priority number when q is equal to or greater than 2; j (J) 0 The upper limit value of the alpha-cut set for the fuzzy risk factor is larger than rho 0 Is used for the collection of lambda values of (c),
I＝{1,2,3}；ΔJ q-1 representing set J q-2 And set J q-1 Is a complement of (a); q represents a positive integer greater than zero.
When (when)
Satisfying the optimal test, determine->
Otherwise, returning to Step 5 to continue calculation; ΔJ q Representing set J q-1 And set J q Complement of DeltaJ q ＝J q-1 \J q ；J q The lower limit value of the alpha-cut set for the fuzzy risk factor is smaller than rho q Lambda value set of->
J q-1 The lower limit value of the alpha-cut set for the fuzzy risk factor is smaller than rho q-1 Lambda value set, J q-2 The lower limit value of the alpha-cut set for the fuzzy risk factor is smaller than rho q-2 Is set of lambda values of (c).
Step 6: let α=α respectively 1 ,...,α N Repeating Step 2-5, and calculating alpha-cut set of fuzzy risk priority numbers under each confidence level
Finally, the fuzzy risk priority number of the ith fault mode is determined according to the formula (8).
And determining a fuzzy membership function of the fuzzy risk priority number according to the alpha-cut set. The method specifically comprises the following steps:
since the fuzzy membership function of the fuzzy risk priority is unknown, but the α -cut set of the fuzzy risk priority under each confidence level is known, in this embodiment, it is assumed that the membership function of the fuzzy risk priority can be approximated by a piecewise linear function, and fig. 3 is a piecewise linear function diagram of the fuzzy risk priority provided by the embodiment of the present invention, and in fig. 3, the x-axis represents the fuzzy risk priority FRPN; referring to fig. 3, the piecewise linear membership function of the fuzzy risk priority number may be expressed as:
in the above, mu FRPN (x) A fuzzy membership function which is fuzzy risk priority number; x represents a fuzzy risk priority number, namely FRPN;
indicating a confidence level of alpha 0 The lower limit value of the alpha-cut set of the fuzzy risk priority number; />
Indicating a confidence level of alpha N The lower limit value of the alpha-cut set of the fuzzy risk priority number; />
Indicating a confidence level of alpha l+1 The lower limit value of the alpha-cut set of the fuzzy risk priority number; />
Indicating a confidence level of alpha l The lower limit value of the alpha-cut set of the fuzzy risk priority number; />
Indicating a confidence level of alpha N The upper limit value of the alpha-cut set of the fuzzy risk priority number; />
Indicating a confidence level of alpha l+1 The upper limit value of the alpha-cut set of the fuzzy risk priority number; />
Indicating a confidence level of alpha l The upper limit of the alpha-cut set of the fuzzy risk priority. In FIG. 3->
Indicating a confidence level of alpha 1 The lower limit value of the alpha-cut set of the fuzzy risk priority number; />
Indicating a confidence level of alpha 1 The upper limit value of the alpha-cut set of the fuzzy risk priority number; />
Indicating a confidence level of alpha 0 The upper limit of the alpha-cut set of the fuzzy risk priority.
And calculating by using a centroid method and a fuzzy membership function to obtain a fuzzy value.
Calculating a solution blur value using formula (15):
wherein x is 0 (FRPN) is a defuzzification value of the fuzzy risk priority,
the area surrounded by the fuzzy membership function and the x-axis of the fuzzy risk priority number FRPN is represented.
Then it is available according to equation (14):
since Δα=α l+1 -α l ＝1/N，α 0 ＝0，α N =1, then equation (16) can be expressed as:
in the same way, can be obtained
/>
In the above-mentioned method, the step of,
representation->
Square of>
Representation->
Square of>
Representation->
Square of>
Representation->
Square of>
Representation->
Is defined by the square of (a),
representation->
Square of (d).
From formulas (17) and (18), it can be seen that
And->
The value of (2) depends only on the α -cutset +.>
Step 106 specifically includes:
and acquiring a fault reason.
Constructing an initial direct influence relation matrix D E R between a fault mode and a fault cause by using a disambiguation value (M +B)×(M+B) ：
Wherein R is (M+B)×(M+B) Representation of (M+B)×(M+B) R represents a real number; d, d vw Elements representing the v-th row and w-th column of the initial direct influence relationship matrix D; v represents the row number of the initial direct influence relation matrix D, v ε M+B; w represents the column number of the initial direct influence relation matrix D, w E M+B, v and w are positive integers, and the total number of rows and the total number of columns of the initial direct influence relation matrix D are M+B; m represents the total number of failure causes, and B represents the total number of failure modes; d (D) 11 、D 12 、D 21 And D 22 Are all block matrixes; d (D) 11 Representing the influence relation matrix of all fault reasons on all fault reasons, D 11 ∈R M×M ；D 12 Representing the influence relation matrix of all fault reasons on all fault modes, D 12 ∈R M×B ；D 21 Representing the influence relation matrix of all fault modes on all fault reasons, D 21 ∈R B×M ；D 22 Representing the relation matrix of the influence of all fault modes on all fault modes, D 22 ∈R B×B ；D 12 The element in (a) is a disambiguation value. Wherein the influence degree of the fault reasons on the fault mode is determined by the definition result of the fuzzy risk priorityAnd the degree of influence of the fault reasons on the fault reasons, the fault modes on the fault reasons and the fault modes is determined through data such as fault simulation, expert experience knowledge base, and the use instruction of the equipment in the lowest appointed level. It should be noted that this embodiment only considers the associated influence of the failure cause on the failure mode, so D 11 、D 21 And D 22 All are zero matrix, D 12 Is a non-zero matrix, D 12 The elements of (2) depend on the sharpening result of the blurring risk priority, i.e. the de-blurring value. For example, the fuzzy risk priority of the 2 nd failure mode caused by the 3 rd failure cause is a matrix D 12 Row 3 and column 2 elements of the list.
Normalizing the initial direct influence relation matrix to obtain a relative direct influence relation matrix
Wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a relatively direct influence relation matrix->
The elements of row v and column w,
calculating a comprehensive influence matrix C, C epsilon R of the failure mode and the failure cause according to a formula (21) by using the relative direct influence relation matrix (M+B)×(M+B) ：
Wherein E represents an identity matrix;
representation->
To the kappa power of (2); kappa represents a positive integer greater than or equal to 1. c vw Representing the element of the w column of the v th row in the comprehensive influence matrix C, namely the v th fault mode or fault reason, and the comprehensive influence degree of the v th fault mode or fault reason; the comprehensive influence matrix C comprises a fault reason and a fault mode, v can represent the fault reason or the fault mode, when v is not more than M, the fault reason is represented, and when v is more than M, the fault mode is represented, and particularly, whether v represents the fault reason or the fault mode is determined according to the value of v; similarly, w may represent a fault cause or represent a fault mode, and when w is not greater than M, it represents a fault cause, and when w is greater than M, it represents a fault mode, specifically determining whether w represents a fault cause or a fault mode according to the value of w. Specifically, if v.ltoreq.M and w.ltoreq.M, c vw The comprehensive influence of the fault reason v on the fault reason w is represented; if v.ltoreq.M and w>M，c vw Representing the comprehensive influence of the fault cause v on the fault mode w; if v>M and w is less than or equal to M, c vw Representing the comprehensive influence of the fault mode v on the fault reason w; otherwise, c vw Indicating the combined effect of failure mode v on failure mode w.
Calculating an associated influence degree matrix C of the failure mode and the failure cause according to the formula (22) by using the comprehensive influence matrix r (v) Associated influence degree matrix C of fault mode and fault reason u (w). In order to measure the influence degree of a certain fault mode or fault cause on all other fault modes and fault causes, the embodiment introduces an associated influence degree matrix and an associated influence degree matrix, C r (v)∈R M+B ，C u (w)∈R M+B 。
Wherein C is r (v) Representing the associated influence degree matrix, i.e. the associated influence degree of the failure mode or the failure cause,the total influence intensity of the fault mode or the fault cause on all other fault modes and fault causes is equal; c (C) u (w) represents an associated influence degree matrix, i.e. an associated influence degree of the failure mode or the failure cause, i.e. the sum of the combined influence strengths of all other failure modes and failure causes.
Calculating the cause degree of the failure mode and the failure cause, rea E R, according to a formula (23) by using the association influence degree matrix and the associated influence degree matrix M+B 。
Rea＝C r (v)-C u (w)＝[Rea(1)… Rea(v)… Rea(M+B)] (23)
In the formula, rea (v) is a failure mode or a cause degree of a failure cause. The Rea belongs to a vector, represents all fault reasons and the reason degree of fault modes, the previous element in Rea represents the cause degree of the failure cause, the latter part of elements represent the cause degree of the fault mode, and the cause degree of the fault cause represented by the Rea (v) or the cause degree of the fault mode is determined according to the value of the v. When Rea (v) > 0, it means that the failure mode or failure cause can affect other failure modes and failure causes; when Rea (v) < 0, it means that the failure mode or failure cause is affected by other failure modes and failure causes.
And carrying out risk priority ranking on the fault modes according to the cause degree to obtain a fault mode risk sequence list. The order of the fault mode risk sequence table is that the higher the cause degree is, the higher the priority is, and the order is at the front.
The embodiment provides a fault mode risk determination system for a ship electric propulsion system, and fig. 4 is a structural diagram of the fault mode risk determination system for the ship electric propulsion system provided by the embodiment of the invention. Referring to fig. 4, the ship electric propulsion system failure mode risk determination system includes:
the hierarchical model module 201 is configured to obtain a hierarchical model of the ship electric propulsion system.
The hierarchical model includes: an initial contract level, a contract level, and a lowest contract level. The initial contract level is the ship comprehensive electric propulsion system. The contract hierarchy includes: a power generation subsystem, a propulsion subsystem, a power distribution subsystem, an energy storage subsystem, and a residual load subsystem. The lowest contract level includes: the system comprises a gas turbine, a diesel engine, a synchronous motor, an excitation voltage regulator, a rotating speed controller of a power generation subsystem, a diode rectifier, a permanent magnet synchronous motor, a propeller, a PWM inverter, a rotating speed controller of a propulsion subsystem, a distribution panel, a switch cabinet, a power transformation module of the power distribution subsystem, a cable, a super capacitor, a storage battery, a bidirectional DC/DC converter, a power transformation module of a residual load subsystem, a daily load and a pulse load.
A functional failure module 202 for determining a functional failure device in the marine electric propulsion system by functional analysis and hierarchical model.
The risk factor module 203 is configured to determine a failure mode of the ship electric propulsion system and a risk factor of the failure mode by using the equipment with failed functions and failure mode and impact analysis. The risk factors include: severity, probability of occurrence, and detectable level.
The fuzzy risk priority module 204 is configured to calculate the risk factor and the weight of the risk factor by using expert knowledge experience, a fuzzy language term set and a fuzzy number theory, so as to obtain a fuzzy risk priority of the fault mode.
The fuzzy risk priority module 204 specifically includes:
the acquisition unit is used for acquiring expert knowledge experience; expert knowledge experience includes: expert opinion of failure mode.
And the credibility unit is used for calculating the credibility of the expert opinion by utilizing the information entropy theory and the qualitative analysis.
The fuzzy comprehensive evaluation unit is used for calculating the risk factors and the weights of the risk factors by utilizing the credibility, the fuzzy language term set and the fuzzy number theory to obtain a fuzzy comprehensive value; the fuzzy comprehensive value is a fuzzy comprehensive evaluation value of the risk factor and the weight of the risk factor.
And the fuzzy risk priority number unit is used for calculating a fuzzy weighted geometric mean of risk factors of the fault mode according to the fuzzy comprehensive value, and determining the fuzzy weighted geometric mean as the fuzzy risk priority number of the fault mode.
And the fuzzy value resolving module 205 is used for resolving the fuzzy risk priority number by utilizing a centroid method and an alpha-cut set theory to obtain a fuzzy value of the fuzzy risk priority number.
The disambiguation value module 205 specifically includes:
and the alpha-cut set unit is used for calculating the alpha-cut set of the fuzzy risk priority number by utilizing the reference adjustment search algorithm.
And the fuzzy membership function unit is used for determining fuzzy membership functions of fuzzy risk priority numbers according to the alpha-cut sets.
And the solution fuzzy value unit is used for calculating and obtaining a solution fuzzy value by using a centroid method and a fuzzy membership function.
The risk prioritization module 206 is configured to prioritize risks of the failure modes according to the ambiguity resolution value by using a decision test and an evaluation laboratory method, so as to obtain a failure mode risk sequence table; the failure mode risk sequence list is used for checking and maintaining the ship electric propulsion system.
The risk prioritization module 206 specifically includes:
and the fault reason unit is used for acquiring the fault reason.
An initial direct influence relation matrix unit for constructing an initial direct influence relation matrix between the failure mode and the failure cause by using the disambiguation value
Wherein d vw Elements representing the v-th row and w-th column of the initial direct influence relationship matrix D; v represents the row number of the initial direct influence relation matrix D, v ε M+B; w represents the column number of the initial direct influence relation matrix D, w E M+B, v and w are positive integers, and the total number of rows and the total number of columns of the initial direct influence relation matrix D are M+B; m represents the total number of failure causes, and B represents the total number of failure modes; d (D) 11 、D 12 、D 21 And D 22 Are all block matrixes; d (D) 11 Representing the influence relation matrix of all fault reasons on all fault reasons, D 11 ∈R M×M ；D 12 Representing the influence relation moment of all fault reasons on all fault modesArray, D 12 ∈R M×B ；D 21 Representing the influence relation matrix of all fault modes on all fault reasons, D 21 ∈R B×M ；D 22 Representing the relation matrix of the influence of all fault modes on all fault modes, D 22 ∈R B×B ；D 12 The element in (a) is a disambiguation value. It should be noted that this embodiment only considers the associated influence of the failure cause on the failure mode, so D 11 、D 21 And D 22 All are zero matrix, D 12 Is a non-zero matrix, D 12 The elements of (2) depend on the sharpening result of the blurring risk priority, i.e. the de-blurring value. For example, the fuzzy risk priority of the 2 nd failure mode caused by the 3 rd failure cause is a matrix D 12 Row 3 and column 2 elements of the list.
A relative direct influence relation matrix unit for normalizing the initial direct influence relation matrix to obtain a relative direct influence relation matrix
Wherein (1)>
Representing a relatively direct influence relation matrix->
Elements of row v and column w, < >>
A comprehensive influence matrix unit for using the relative direct influence relation matrix according to the formula
Calculating a comprehensive influence matrix C of the fault mode and the fault cause; wherein E represents an identity matrix; / >
Representation->
To the kappa power of (2); kappa represents a positive integer greater than or equal to 1. c vw Representing the elements of row v and column w of the composite influence matrix C.
The association influence degree matrix unit is used for utilizing the comprehensive influence matrix and according to a formula
Calculating the correlation influence degree matrix C of the fault mode and the fault reason r (v) Associated influence degree matrix C of fault mode and fault reason u (w)。
A cause degree unit for calculating the cause degree of the failure mode and the failure cause, rea e R, according to the formula (23) by using the associated influence degree matrix and the associated influence degree matrix M+B 。
Rea＝C r (v)-C u (w)＝[Rea(1)… Rea(v)… Rea(M+B)] (23)
In the formula, rea (v) is a failure mode or a cause degree of a failure cause. The Rea belongs to a vector, represents all fault reasons and the reason degree of fault modes, the previous element in Rea represents the cause degree of the failure cause, the latter part of elements represent the cause degree of the fault mode, and the cause degree of the fault cause represented by the Rea (v) or the cause degree of the fault mode is determined according to the value of the v. When Rea (v) > 0, it means that the failure mode or failure cause can affect other failure modes and failure causes; when Rea (v) < 0, it means that the failure mode or failure cause is affected by other failure modes and failure causes.
And the risk priority ordering unit is used for performing risk priority ordering on the fault modes according to the cause degree to obtain a fault mode risk sequence list. The order of the fault mode risk sequence table is that the higher the cause degree is, the higher the priority is, and the order is at the front.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. A method for determining risk of failure mode of an electric propulsion system of a ship, comprising:
acquiring a hierarchical model of a ship electric propulsion system;
determining a device with a functional failure in the ship electric propulsion system through a functional analysis method and the hierarchical model;
determining a fault mode of the ship electric propulsion system and a risk factor of the fault mode by using the equipment with the failed function and the failure mode and influence analysis;
Calculating the risk factors and the weights of the risk factors by adopting expert knowledge experience, fuzzy language term sets and fuzzy number theory to obtain fuzzy risk priority numbers of the fault modes;
performing defuzzification on the fuzzy risk priority number by using a centroid method and an alpha-cut set theory to obtain a defuzzification value of the fuzzy risk priority number;
according to the disambiguation value, performing risk priority ranking on the fault modes by adopting a decision test and evaluation laboratory method to obtain a fault mode risk sequence table; the fault mode risk sequence table is used for checking and maintaining the ship electric propulsion system; the method specifically comprises the following steps:
acquiring a fault reason;
constructing an initial direct influence relation matrix between the fault mode and the fault cause by using the disambiguation value
Wherein d vw Elements representing the v-th row and w-th column of the initial direct influence relationship matrix D; v represents the row number of the initial direct influence relation matrix D, v E M+B; w represents the column number of the initial direct influence relation matrix D, w is E M+B; d (D) 11 Representing the influence relation matrix of all the fault reasons on all the fault reasons, D 12 Representing the relation matrix of the influence of all the fault reasons on all the fault modes, D 21 Representing the influence relation matrix of all the fault modes on all the fault reasons, D 22 Representing an influence relation matrix of all the fault modes on all the fault modes; m represents the total number of the fault reasons; b represents the total number of failure modes; d (D) 12 The element in (a) is the disambiguation value;
normalizing the initial direct influence relation matrix to obtain a relative direct influence relation matrix
Wherein (1)>
Representing said relative direct influence relation matrix +.>
The elements of row v and column w,
using the relative direct influence relationship matrix according to the formula
Calculating a comprehensive influence matrix C of the fault mode and the fault cause; wherein E represents an identity matrix; kappa represents a positive integer; c vw Representation synthesisElements affecting the v-th row and w-th column of matrix C;
using the comprehensive influence matrix, according to the formula
Calculating an associated influence degree matrix C of the fault mode and the fault reason r (v) And an associated influence matrix C of the failure mode and the failure cause u (w)；
Using the associated influence matrix and the associated influence matrix according to the formula rea=c r (v)-C u (w) calculating a cause degree Rea of the failure mode and the failure cause;
and carrying out risk priority ranking on the fault modes according to the cause degree to obtain a fault mode risk sequence list.
2. The ship electric propulsion system failure mode risk determination method according to claim 1, wherein the hierarchical model includes: an initial contract level, a contract level, and a lowest contract level; the initial appointment level is a ship comprehensive electric propulsion system;
the contract hierarchy includes: a power generation subsystem, a propulsion subsystem, a power distribution subsystem, an energy storage subsystem, and a residual load subsystem;
the lowest appointment hierarchy includes: gas turbine, diesel engine, synchronous motor, permanent magnet synchronous motor, screw, panel, battery and transformer module.
3. The method for determining risk of failure mode of electric propulsion system of ship according to claim 2, wherein the calculating the risk factor and the weight of the risk factor by using expert knowledge experience, fuzzy language term set and fuzzy number theory to obtain fuzzy risk priority of the failure mode specifically comprises:
acquiring the expert knowledge experience; the expert knowledge experience includes: expert opinion of the failure mode;
Calculating the credibility of the expert opinion by utilizing an information entropy theory and qualitative analysis;
calculating the risk factors and the weights of the risk factors by using the credibility, the fuzzy language term set and the fuzzy number theory to obtain a fuzzy comprehensive value; the fuzzy comprehensive value is a fuzzy comprehensive evaluation value of the risk factor and the weight of the risk factor;
and calculating a fuzzy weighted geometric mean of risk factors of the fault mode according to the fuzzy comprehensive value, and determining the fuzzy weighted geometric mean as a fuzzy risk priority number of the fault mode.
4. The method for determining risk of failure mode of electric propulsion system of ship according to claim 3, wherein the defuzzifying the fuzzy risk priority number by using centroid method and α -cut set theory to obtain a defuzzified value of the fuzzy risk priority number specifically comprises:
calculating an alpha-cut set of the fuzzy risk priority number by using a benchmark adjustment search algorithm;
determining a fuzzy membership function of the fuzzy risk priority number according to the alpha-cut set;
and calculating to obtain a solution ambiguity value by using a centroid method and the fuzzy membership function.
5. A marine vessel electric propulsion system failure mode risk determination system, comprising:
The hierarchical model module is used for acquiring a hierarchical model of the ship electric propulsion system;
the functional failure module is used for determining equipment with functional failure in the ship electric propulsion system through a functional analysis method and the hierarchical model;
the risk factor module is used for determining a fault mode of the ship electric propulsion system and a risk factor of the fault mode by using the equipment with the failed function and failure mode and influence analysis;
the fuzzy risk priority number module is used for calculating the risk factors and the weights of the risk factors by adopting expert knowledge experience, fuzzy language term sets and fuzzy number theory to obtain fuzzy risk priority numbers of the fault modes;
the fuzzy value resolving module is used for resolving the fuzzy risk priority number by utilizing a centroid method and an alpha-cut set theory to obtain a fuzzy value of the fuzzy risk priority number;
the risk priority ordering module is used for performing risk priority ordering on the fault modes by adopting a decision test and evaluation laboratory method according to the disambiguation value to obtain a fault mode risk sequence table; the fault mode risk sequence table is used for checking and maintaining the ship electric propulsion system; the method specifically comprises the following steps:
The fault reason unit is used for acquiring a fault reason;
an initial direct influence relation matrix unit for constructing an initial direct influence relation matrix between the failure mode and the failure cause by using the disambiguation value
Wherein d vw Elements representing the v-th row and w-th column of the initial direct influence relationship matrix D; v represents the row number of the initial direct influence relation matrix D, v E M+B; w represents the column number of the initial direct influence relation matrix D, w is E M+B; d (D) 11 Representing the influence relation matrix of all the fault reasons on all the fault reasons, D 12 Representing the relation matrix of the influence of all the fault reasons on all the fault modes, D 21 Representing the influence relation matrix of all the fault modes on all the fault reasons, D 22 Representing an influence relation matrix of all the fault modes on all the fault modes; m represents the total number of the fault reasons; b represents the total number of failure modes; d (D) 12 The element in (a) is the disambiguation value;
a relative direct influence relation matrix unit for normalizing the initial direct influence relation matrix to obtain a relative direct influence relation matrix
Wherein (1) >
Representing said relative direct influence relation matrix +.>
Elements of row v and column w, < >>
A comprehensive influence matrix unit for using the relative direct influence relation matrix according to the formula
Calculating a comprehensive influence matrix C of the fault mode and the fault cause; wherein E represents an identity matrix; kappa represents a positive integer; c vw Elements representing the v-th row and w-th column of the comprehensive influence matrix C;
a correlation influence matrix unit for using the comprehensive influence matrix according to the formula
Calculating an associated influence degree matrix C of the fault mode and the fault reason r (v) And an associated influence matrix C of the failure mode and the failure cause u (w)；
A cause degree unit for using the associated influence degree matrix and the associated influence degree matrix according to the formula rea=c r (v)-C u (w) calculating a cause degree Rea of the failure mode and the failure cause;
and the risk priority ranking unit is used for performing risk priority ranking on the fault modes according to the cause degree to obtain a fault mode risk sequence list.
6. The marine electric propulsion system failure mode risk determination system of claim 5, wherein the hierarchical model comprises: an initial contract level, a contract level, and a lowest contract level; the initial appointment level is a ship comprehensive electric propulsion system;
The contract hierarchy includes: a power generation subsystem, a propulsion subsystem, a power distribution subsystem, an energy storage subsystem, and a residual load subsystem;
the lowest appointment hierarchy includes: gas turbine, diesel engine, synchronous motor, permanent magnet synchronous motor, screw, panel, battery and transformer module.
7. The marine vessel electric propulsion system failure mode risk determination system of claim 6, wherein the fuzzy risk priority module specifically comprises:
an acquisition unit for acquiring the expert knowledge experience; the expert knowledge experience includes: expert opinion of the failure mode;
the credibility unit is used for calculating the credibility of the expert opinion by utilizing an information entropy theory and qualitative analysis;
the fuzzy comprehensive evaluation unit is used for calculating the risk factors and the weights of the risk factors by utilizing the credibility, the fuzzy language term set and the fuzzy number theory to obtain a fuzzy comprehensive value; the fuzzy comprehensive value is a fuzzy comprehensive evaluation value of the risk factor and the weight of the risk factor;
and the fuzzy risk priority number unit is used for calculating a fuzzy weighted geometric mean of risk factors of the fault mode according to the fuzzy comprehensive value, and determining the fuzzy weighted geometric mean as the fuzzy risk priority number of the fault mode.
8. The ship electric propulsion system fault mode risk determination system of claim 7, wherein the disambiguation value module specifically comprises:
the alpha-cut set unit is used for calculating the alpha-cut set of the fuzzy risk priority number by utilizing a reference adjustment search algorithm;
the fuzzy membership function unit is used for determining a fuzzy membership function of the fuzzy risk priority number according to the alpha-cut set;
and the fuzzy value solving unit is used for calculating and obtaining a fuzzy value by utilizing a centroid method and the fuzzy membership function.
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