Ship aging management object screening method

Abstract

The invention discloses a ship aging management object screening method, and the method comprises the following steps: 1), obtaining a ship composition element diagram for a ship product to be subjected to aging management; 2) constructing an aging management demand degree comprehensive evaluation model; 3) obtaining the evaluation grade of each evaluation index of the evaluation object; 4) constructing an evaluation decision evaluation matrix, and performing aging management priority evaluation; 5) determining a solution with the highest priority for elements in the evaluation matrix; 6) calculating the similarity of the cloud model; 7) calculating a correlation coefficient Ri based on cloud model similarity; 8) performing aging management priority evaluation on the m evaluation objects in the ship composition element graph according to the correlation coefficient Ri based on the cloud model similarity; and 9) obtaining the sequence of the aging management priorities of the basic elements in the whole ship range. According to the ship aging management object screening method based on the cloud model theory, fuzziness and randomness generated in the subjective evaluation process are reduced.

Description

Technical Field

[0001] The invention relates to ship maintenance technology, and in particular to a method for screening ship aging management objects. Background Art

[0002] As an important part of the whole life cycle management in the nuclear power industry, aging management has accumulated a lot of experience in theory, technology and practice, and has become an important means to ensure the safe operation of nuclear power plants. In the shipbuilding industry, especially for special ships equipped with certain high-tech equipment, aging management is urgently needed to reduce the failure rate caused by aging. While ensuring the safety of ship navigation, it can also reduce the shutdown and maintenance caused by aging problems, improve the ship's sailing rate, and maximize the cost-effectiveness of ships.

[0003] The status of aging management in ship product assurance work is increasing day by day. Faced with the huge number of various equipment and components on ships with complex classification, how to scientifically and efficiently screen the aging management objects has become the key to restricting the development of aging management work. However, there is still a lack of experience in the research and practice of aging management with ship products as the object, and it is difficult to obtain relevant aging mechanism research data and statistical data. Therefore, it is urgent to design and establish a method for screening ship aging management objects. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for screening ship aging management objects in view of the defects in the prior art.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for screening ship aging management objects, comprising the following steps:

[0006] 1) For the ship products to be managed for aging, the ship is divided into components of each level from top to bottom according to the levels of system, subsystem, equipment and components to obtain the ship component element diagram;

[0007] 2) Construct a comprehensive evaluation model for aging management needs;

[0008] Set the target layer to aging management priority A,

[0009] The middle criteria layer is: importance of the evaluation object, maintenance cost of the evaluation object, and aging trend of the evaluation object;

[0010] The evaluation indicators of the indicator layer are: the impact of the evaluation object on the navigation safety of the ship B1, the impact of the evaluation object on the completion of the ship's mission B2; the maintenance cycle of the evaluation object B3, the maintenance cost of the evaluation object B4; the aging cycle of the evaluation object B5; the study of the aging mechanism of the evaluation object B6;

[0011] On this basis, determine the factor set B = [B1, B2, B3, B4, B5, B6];

[0012] 3) Obtain the evaluation level of each evaluation indicator of the evaluation object;

[0013] According to the actual situation, the evaluation grade value of each evaluation indicator of the evaluation object is obtained, and the evaluation grade value is in the range of [0,1];

[0014] 4) Construct an evaluation decision matrix to assess the priority of aging management;

[0015] There are m sub-elements in the constituent element diagram. The i-th evaluation object is evaluated against the index B in the factor set. j The evaluation results build the cloud model C ij , where i = 1, 2, ..., m; j = 1, 2, 3, 4, 5, 6; C ij Construct an evaluation matrix of m rows and 6 columns for the elements;

[0016] 5) Determine the solution with the highest priority for the elements in the evaluation matrix;

[0017] The larger the value of the element indicator result in the evaluation matrix, the higher the aging management priority represented; the calculation method is as follows:

[0018] In C ij In the evaluation matrix of m rows and 6 columns constructed for the element, for each column of j = 1, 2, ..., 6, the cloud model with the largest priority of aging management in each column is selected, which is recorded as C maxj , C with j=1,2,……,6 maxj Combine them into vectors in order and record them as the solution with the highest priority D max ; In the process, the size of the cloud models is compared, and the expected value of the cloud model feature number is used to measure it. That is, the cloud model with the largest expected value is considered to be the largest cloud model;

[0019] C maxj =max[C 1j ,C 2j ,……,C mj ]=(Ex maxj ,En maxj ,He maxj ),(j=1,2,3,4,5,6)

[0020] D max =max[C max1 ,C max2 ,C max3 ,C max4 ,C max5 ,C max6 ];

[0021] Among them, (Ex maxj ,En maxj ,He maxj ) are the characteristic numbers of the cloud model, which are expectation Ex, entropy En and super entropy He respectively. The subscript maxj indicates the matrix position corresponding to the maximum value of the j-th column;

[0022] 6) Calculate the cloud model similarity;

[0023] Calculate the cloud model similarity, and use the cloud model similarity to measure the comparison between the evaluation result vector of each evaluation object and the solution with the highest priority;

[0024] The cloud model similarity is calculated as follows:

[0025] Assume that the feature numbers of the two cloud models are (Ex1, En1, He1) and (Ex2, En2, He2) respectively, and the similarity S between them is calculated as follows.

[0026]

[0027] S=S1×S2

[0028] 7) Calculate the similarity of the evaluation results of the six indicators between each evaluation object and the solution with the highest priority, and calculate the correlation coefficient R based on the cloud model similarity i ;

[0029] First, based on the evaluation objects in steps 4 and 5, the index B in the factor set j The evaluation result C ij And the highest priority solution D max Medium Index B j The evaluation result C maxj , calculate the similarity S of the cloud model ij ; Then, the correlation coefficient R is calculated based on the cloud model similarity i ;

[0030] From step 4, we can see that the i-th evaluation object is for the indicator B in the factor set j The evaluation result is cloud model C ij (Ex ij ,En ij ,He ij ),(i=1,2,……,m;j=1,2,3,4,5,6); From step 5, we can know that the solution with the highest priority D is determined max Medium Index B j The evaluation result is cloud model C maxj =(Ex maxj ,En maxj ,He maxj ), computing cloud model C ij With C maxjSimilarity S ij , the formula is as follows:

[0031] S ij =S ij1 ×S ij2

[0032]

[0033] Calculate the correlation coefficient R based on the cloud model similarity i , the formula is as follows:

[0034] P = max[max(S ij )], (i=1,2,...,m; j=1,2,3,4,5,6)

[0035] Q = min[min(S ij )], (i=1,2,...,m; j=1,2,3,4,5,6)

[0036]

[0037] Among them, ξ∈(0,1), here we take ξ=0.5, P is S ij The maximum value in row m and column j, Q is S ij The minimum value in the mth row and jth column, ξ is a preset constant, R i is the correlation coefficient of the i-th evaluation object.

[0038] 8) According to the correlation coefficient R based on the cloud model similarity i , sort the aging management priorities of the m evaluation objects, R i The larger the value, the higher the aging management priority of the corresponding evaluation object.

[0039] 9) Based on the methods of steps 4) to 8), the aging management priority of other parts in the ship component element diagram is evaluated in turn. After all the evaluations are completed, the weights of the aging management priority of the sub-elements are calculated. For each basic element in the lowest layer, its own weight is multiplied by the weight values ​​of each level in the upper layer from bottom to top, until the top layer, and the aging management priority weight value of each element (component) in the bottom layer of the ship element diagram within the entire ship is obtained, which is recorded as T x (x is the base element number of the lower layer). x Sort from large to small to obtain the order of aging management priority of the basic elements within the entire ship according to the decomposition of the ship component element diagram in step 1.

[0040] The beneficial effects produced by the present invention are:

[0041] The present invention establishes a method for screening ship aging management objects based on cloud model theory, which reduces the ambiguity and randomness generated in the subjective evaluation process; at the same time, this method relies on less data and is suitable for the current situation where there is a lack of research and practical experience in aging management of ship products and relevant statistical data are difficult to obtain. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0043] Figure 1 is a method flow chart of an embodiment of the present invention;

[0044] Figure 2 is a diagram of ship components according to an embodiment of the present invention;

[0045] Figure 3 is an evaluation index diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] like Figure 1 As shown, a method for screening ship aging management objects comprises the following steps:

[0048] 1) For the ship products to be managed for aging, the ship is divided into components of each level from top to bottom according to the levels of system, subsystem, equipment and components to obtain the ship component element diagram;

[0049] The systems include hull structure, mechanical system and electrical system;

[0050] Subsystem division;

[0051] The hull structure is divided into: bottom structure, side structure, deck structure and bulkhead structure;

[0052] The mechanical system is divided into: power system, transmission system, execution system and control system;

[0053] The electrical system is divided into: power system, electric propulsion system, lighting system and communication and navigation system;

[0054] Then get the related equipment and its parts in turn; Figure 2 As shown;

[0055] 2) Construct a comprehensive evaluation model for aging management needs; the evaluation index diagram is as follows: Figure 3 As shown;

[0056] Set the target layer to aging management priority A,

[0057] The middle criteria layer is: importance of the evaluation object, maintenance cost of the evaluation object, and aging trend of the evaluation object;

[0058] The evaluation indicators of the indicator layer are: the impact of the evaluation object on the navigation safety of the ship B1, the impact of the evaluation object on the completion of the ship's mission B2; the maintenance cycle of the evaluation object B3, the maintenance cost of the evaluation object B4; the aging cycle of the evaluation object B5; the study of the aging mechanism of the evaluation object B5;

[0059] On this basis, determine the factor set B = [B1, B2, B3, B4, B5, B5];

[0060] 3) Obtain the evaluation level of each evaluation indicator of the evaluation object;

[0061] According to the actual situation, the evaluation grade value of each evaluation indicator of the evaluation object is obtained, and the evaluation grade value is in the range of [0,1];

[0062] 4) Construct an evaluation decision matrix to assess the priority of aging management;

[0063] There are m sub-elements in the constituent element diagram. The i-th evaluation object is evaluated against the index B in the factor set. j The evaluation results are used to construct the cloud model C through the reverse cloud generator ij , where i = 1, 2, ..., m; j = 1, 2, 3, 4, 5, 6; C ij Construct an evaluation matrix of m rows and 6 columns for the elements;

[0064] 5) Determine the solution with the highest priority;

[0065] It is judged whether each evaluation indicator in the factor set is a positive indicator or a negative indicator. The larger the value of the positive indicator result, the higher the aging management priority represented; the smaller the value of the negative indicator, the higher the aging management priority represented;

[0066] In C ij In the evaluation matrix of m rows and 6 columns constructed for the element, for each column of j = 1, 2, ..., 6, the cloud model with the largest priority of aging management in each column is selected, which is recorded as C maxj , C with j=1,2,……,6 maxj Combine them into vectors in order and record them as the solution with the highest priority D max ; In the process, the size of the cloud models is compared, and the expected value of the cloud model feature number is used to measure it. That is, the cloud model with the largest expected value is considered to be the largest cloud model;

[0067] C maxj =max[C1j ,C 2j ,……,C mj ]=(Ex maxj ,En maxj ,He maxj ),(j=1,2,3,4,5,6)

[0068] D max =max[C max1 ,C max2 ,C max3 ,C max4 ,C max5 ,C max6 ];

[0069] Among them, (Ex maxj ,En maxj ,He maxj ) are the characteristic numbers of the cloud model, which are expectation Ex, entropy En and super entropy He respectively. The subscript maxj indicates the matrix position corresponding to the maximum value of the j-th column;

[0070] 6) Calculate the cloud model similarity;

[0071] Calculate the cloud model similarity, and use the cloud model similarity to measure the comparison between the evaluation result vector of each evaluation object and the solution with the highest priority;

[0072] Assume that the feature numbers of the two cloud models are (Ex1, En1, He1) and (Ex2, En2, He2), and the similarity S between them is calculated as follows:

[0073]

[0074] S=S1×S2

[0075] 7) Calculate the similarity of the evaluation results of the six indicators between each evaluation object and the solution with the highest priority, and calculate the correlation coefficient R based on the cloud model similarity i ;

[0076] First, based on the evaluation objects in steps 4 and 5, the index B in the factor set j The evaluation result C ij And the highest priority solution D max Medium Index B j The evaluation result C maxj , calculate the similarity S of the cloud model ij ; Then, the correlation coefficient R is calculated based on the cloud model similarity i .

[0077] From step 4, we can see that the i-th evaluation object is for the indicator B in the factor set j The evaluation result is cloud model C ij(Ex ij ,En ij ,He ij ),(i=1,2,……,m;j=1,2,3,4,5,6); From step 5, we can know that the solution with the highest priority D is determined max Medium Index B j The evaluation result is cloud model C maxj =(Ex maxj ,En maxj ,He maxj ), computing cloud model C ij With C maxj Similarity S ij , the formula is as follows:

[0078] S ij =S ij1 ×S ij2

[0079]

[0080] Calculate the correlation coefficient R based on the cloud model similarity i , the formula is as follows.

[0081] P = max[max(S ij )], (i=1,2,...,m; j=1,2,3,4,5,6)

[0082] Q = min[min(S ij )], (i=1,2,...,m; j=1,2,3,4,5,6)

[0083]

[0084] Among them, ξ∈(0,1), here we take ξ=0.5.

[0085] 8) According to the correlation coefficient R based on the cloud model similarity i , sort the aging management priorities of the m evaluation objects, R i The larger it is, the higher the aging management priority of the corresponding evaluation object;

[0086] 9) Based on the method described in steps 4) to 8), the aging management priority of other parts in the ship component element diagram is evaluated in turn. After all the evaluations are completed, the weights of the aging management priority of the sub-elements are calculated. For each basic element in the bottom layer, its own weight is multiplied by the weight values ​​of each level in the upper layer from bottom to top, until the top layer, and the aging management priority weight value of each element in the bottom layer of the ship element diagram within the entire ship is obtained, which is recorded as T x (x is the base element number of the lower layer). xSort from large to small, and according to the split of the ship's component element diagram, obtain the order of aging management priority of the basic elements within the entire ship.

[0087] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A method for screening ship aging management objects, characterized in that: The following steps are involved: 1) For the ship products to be managed for aging, the ship is divided into components of each level from top to bottom according to the levels of system, subsystem, equipment and components to obtain the ship component element diagram; 2) Construct a comprehensive evaluation model for aging management needs; Set the target layer to aging management priority A, The middle criteria layer is: importance of the evaluation object, maintenance cost of the evaluation object, and aging trend of the evaluation object; The evaluation indicators of the indicator layer are: the impact of the evaluation object on the navigation safety of the ship B1, the impact of the evaluation object on the completion of the ship's mission B2; the maintenance cycle of the evaluation object B3, the maintenance cost of the evaluation object B4; the aging cycle of the evaluation object B5; the study of the aging mechanism of the evaluation object B6; On this basis, determine the evaluation index factor set B = [B1, B2, B3, B4, B5, B6]; 3) Obtain the evaluation level of each evaluation indicator of the evaluation object; According to the actual situation, the evaluation grade value of each evaluation indicator of the evaluation object is obtained, and the evaluation grade value is in the range of [0,1]; 4) Construct an evaluation decision matrix to assess the priority of aging management; There are m sub-elements in the constituent element diagram. The i-th evaluation object is evaluated against the index B in the factor set. j The evaluation results build the cloud model C ij , where i = 1, 2, ..., m; j = 1, 2, 3, 4, 5, 6; C ij Construct an evaluation matrix of m rows and 6 columns for the elements; 5) Determine the solution with the highest priority for the elements in the evaluation matrix; 6) Calculate the cloud model similarity and use the cloud model similarity to measure the comparison between the evaluation results of each evaluation object and the solution with the highest priority; 7) Calculate the similarity of the evaluation results of the six indicators between each evaluation object and the solution with the highest priority, and calculate the correlation coefficient R based on the cloud model similarity i ; 8) According to the correlation coefficient R based on the cloud model similarity i , evaluate the aging management priority of m assessment objects in the ship component element diagram, R i The larger it is, the higher the aging management priority of the corresponding evaluation object; 9) Based on the aging management priority of m assessment objects, according to the aging management priority weight value T of each element x in the ship element diagram within the whole ship x , and obtain the order of aging management priority of each basic element within the entire ship.

2. The method for screening ship aging management objects according to claim 1, characterized in that: In step 5), the solution with the highest priority is determined as follows: In C ij In the evaluation matrix of m rows and 6 columns constructed for the element, for each column of j = 1, 2, ..., 6, the cloud model with the largest priority of aging management in each column is selected, which is recorded as C maxj , C with j=1,2,……,6 maxj Combine them into vectors in order and record them as the solution with the highest priority D max ; In the process, the size of the cloud models is compared, and the expected value of the cloud model feature number is used to measure it. That is, the cloud model with the largest expected value is considered to be the largest cloud model; C maxj =max[C 1j ,C 2j ,……,C mj ]=(Ex maxj ,En maxj ,He maxj 0,(j=1,2,3,4,5,6) D max =max[C max1 ,C max2 ,C max3 ,C max4 ,C max5 ,C max6 ]; Among them, (Ex maxj ,En maxj ,He maxj ) are the characteristic numbers of the cloud model, which are expectation Ex, entropy En and super entropy He respectively. The subscript maxj represents the matrix position corresponding to the maximum value of the j-th column.

3. The method for screening ship aging management objects according to claim 1, characterized in that: In step 6), the cloud model similarity is calculated as follows: Assume that the feature numbers of the two cloud models are (Ex1, En1, He1) and (Ex2, En2, He2) respectively, and the similarity S between them is calculated as follows. S=S1×S2.

4. The method for screening ship aging management objects according to claim 1, characterized in that: In the step 7), based on the evaluation object, the index B in the factor set j The evaluation result C ij And the highest priority solution D max Medium Index B j The evaluation result C maxj , calculate the similarity S of the cloud model ij ; Then, the correlation coefficient R is calculated based on the cloud model similarity i .

5. The method for screening ship aging management objects according to claim 1, characterized in that: In step 7), the similarity S of the cloud model is calculated ij The formula used is as follows: The i-th evaluation object targets the indicator B in the factor set. j The evaluation result is cloud model C ij (Ex ij ,En ij ,He ij ),i=1,2,……,m;j=1,2,3,4,5,6;the highest priority solution is D max Medium Index B j The evaluation result is cloud model C maxj =(Ex maxj ,En maxj ,He maxj ), computing cloud model C ij With C maxj Similarity S ij , S ij =S ij1 ×S ij2 6. The method for screening ship aging management objects according to claim 1, characterized in that: In step 7), the correlation coefficient R based on the cloud model similarity is calculated i , the formula is as follows: P=max[max(S ij )],(i=1,2,……,m;j=1,2,3,4,5,6) Q=min[min(S ij )],(i=1,2,……,m;j=1,2,3,4,5,6) Where P is S ij The maximum value in row m and column j, Q is S ij The minimum value in the mth row and jth column, ξ is a preset constant, R i is the correlation coefficient of the i-th evaluation object.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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