Historical statistics-based marine main engine task execution capability assessment method

Abstract

The invention provides a marine main engine task execution capability assessment method based on historical statistics. The method is used for solving the power interruption risk in the ship navigation process caused by uncertainty of faults of the ship main engine and unreasonable maintenance strategies. Based on the operation time historical data of the marine main engine, counting the previous fault interval time of the marine main engine, calculating the statistic index of the fault interval time, counting the last fault-free operation time and the recent fault frequency of the marine main engine, and calculating the task execution capability of the marine main engine based on the logarithmic normal distribution model and the fault influence coefficient. According to the method, on the basis of historical statistics and probability analysis thoughts, ship main engine task execution capability evaluation under uncertainty is realized, decision support is provided for operation and maintenance of a ship main engine, and the operation and maintenance efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of ship intelligent operation and maintenance, and in particular to a method for evaluating the task execution capability of a ship main engine based on historical statistics. Background Art

[0002] The main engine is a ship's power plant. Once a failure occurs, it may cause the ship's power to be interrupted, greatly affecting the efficiency of the navigation mission and increasing the difficulty of ship navigation control. Affected by factors such as the complexity of operating conditions, the variability of working scenarios, and the dispersion of ship main engine performance parameters, the failure pattern of the ship main engine during operation shows a certain randomness. At present, the operation and maintenance management of the main engine in the ship field mostly adopts a combination of planned maintenance and post-maintenance. Planned maintenance is based on the failure rate of key components of the ship main engine to carry out regular maintenance, and post-maintenance is to carry out inspection and maintenance of key components after the failure occurs. The existing maintenance strategy lacks adaptability and flexibility and cannot meet the growing maintenance needs of modern ships. Summary of the invention

[0003] The purpose of the present invention is to mine the failure patterns of ship main engines from historical data based on probability statistics theory, and comprehensively consider the historical failure patterns of ship main engines and recent alarm signal characteristics, complete the evaluation of ship main engine task execution capability, and provide auxiliary decision-making for the maintenance and predictive maintenance of ship main engines.

[0004] The present invention provides a method for evaluating the task execution capability of a ship main engine based on historical statistics, which is used to solve the risk of power interruption during the navigation of a ship caused by the uncertainty of the occurrence of ship main engine failures and unreasonable maintenance strategies. Based on the historical data of the operation time of the ship main engine, the time intervals between previous failures of the ship main engine are counted, the statistical indicators of the failure intervals are calculated, the most recent trouble-free operation time of the ship main engine and the recent failure frequency are counted, and the task execution capability of the ship main engine is calculated based on the log-normal distribution model and the failure influence coefficient. Based on the ideas of historical statistics and probability analysis, this method realizes the evaluation of the task execution capability of the ship main engine under uncertainty, provides decision support for the operation and maintenance of the ship main engine, and improves the operation and maintenance efficiency.

[0005] An embodiment of the present invention provides a method for evaluating the task execution capability of a ship main engine based on historical statistics, the method comprising:

[0006] Obtain the operating time history data of the ship's main engine;

[0007] Counting the time intervals between all failures of the ship's main engine according to the historical data;

[0008] Determine the statistic of the interval time between main engine failures of the ship according to the interval time between all the failures;

[0009] Determine the cumulative running time of the ship's main engine after the last fault recovery;

[0010] According to a certain length sliding time window, the frequency of the first, second and third level alarms of the ship's main engine is counted;

[0011] The mission execution capability of the ship's main engine is determined based on the log-normal distribution model and the fault influence coefficient.

[0012] In some embodiments, the historical data includes: the time point of each host startup, shutdown, and failure.

[0013] In some embodiments, the statistics of the time interval between failures of the main engine of the ship include a mean value μ and a standard deviation σ.

[0014] In some embodiments, determining the mission execution capability of the ship main engine based on the log-normal distribution model and the fault impact coefficient includes:

[0015] The mission execution capability R of the ship's main engine is determined according to the following formula:

[0016]

[0017] Among them, R ranges from [0,1], f(N1,N2,N3) is the fault influence coefficient, and its value range is (0,1], t is the cumulative running time of the ship main engine after the last fault recovery, N1, N2, N3 are the frequencies of the first, second, and third level alarms of the main engine according to the sliding time window T.

[0018] In some embodiments, the counting of the intervals between all failures of the ship main engine according to the historical data includes:

[0019] For the time interval between the ship's main engine from being put into use to the occurrence of n failures [T1, T2, ..., T n The calculation method of T1 is as follows: The calculation method of T1 is to count the cumulative running time of the host from the time the host is put into use to the time when the first failure occurs, T i The calculation method of (2≤i≤n) is to count the cumulative running time of the host from the time the host recovered from the i-1th fault to the time the i-th fault occurred;

[0020] Average time between failures of the host Standard deviation of the host failure interval

[0021] In some embodiments, the length T of the sliding time window for statistical failure frequency is defined according to actual needs.

[0022] In some embodiments, the length T of the sliding time window for statistical failure frequency is less than μ.

[0023] In some embodiments, the calculation formula of the fault influence coefficient f(N1, N2, N3) is:

[0024] f(N1,N2,N3)=α1×k(N1)+α2×k(N2)+α3×k(N3)

[0025] Among them, α1, α2, and α3 are weight coefficients, which respectively represent the influence of the first-level, second-level, and third-level alarms on the mission execution capability of the ship's main engine, α1<α2<α3 and α1+α2+α3=1;

[0026] k(N i ) is the risk index of the first, second and third level alarms (1≤i≤3), and the calculation formula is:

[0027]

[0028] Among them, N i is the number of level i alarms that occur during the sliding time window T of the ship’s main engine, β i Calculate the risk index parameters for level i alarm.

[0029] In some embodiments, the method further comprises: i Conduct parameter adjustment training.

[0030] In some embodiments, the basic criterion for assigning values ​​of α1, α2, α3, β1, β2, and β3 parameters is: the more alarm times and the higher the alarm level, the smaller the value of the fault influence coefficient f(N1, N2, N3).

[0031] The beneficial effects of the above embodiments of the present invention include:

[0032] Compared with the traditional reliability assessment method using mean time between failures and mean time to recovery, this method uses the ship main engine operation data to calculate the statistics of the failure time interval, constructs a log-normal distribution model to characterize the randomness and uncertainty of the ship main engine failure, and on this basis, evaluates the task execution capability of the ship main engine, which has higher flexibility and adaptability.

[0033] This method introduces a fault risk coefficient in the process of ship main engine task execution capability assessment to describe the impact of recent alarms on the attenuation of the ship main engine's task execution capability. The more recent alarms occur and the higher the alarm level, the more the ship main engine's task execution capability decreases, thereby more accurately characterizing the current capability of the ship main engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings illustrate generally, by way of example and not limitation, various embodiments discussed herein.

[0035] Figure 1 The present invention is a flowchart of a method for evaluating the task execution capability of a ship main engine based on historical statistics according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to enable a more detailed understanding of the features and technical contents of the embodiments of the present application, the implementation of the embodiments of the present application is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present application.

[0037] In the embodiments of the present application, it should be noted that, unless otherwise specified and limited, the term "connection" should be understood in a broad sense. For example, it can be an electrical connection or a connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0038] It should be noted that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that the specific order or sequence of "first\second\third" can be interchanged where permitted. It should be understood that the objects distinguished by "first\second\third" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0039] The present invention provides a method for evaluating the task execution capability of a ship main engine based on historical statistics, which is used to solve the risk of power interruption during the navigation of a ship caused by the uncertainty of the occurrence of ship main engine failures and unreasonable maintenance strategies. Based on the historical data of the operation time of the ship main engine, the time intervals between previous failures of the ship main engine are counted, the statistical indicators of the failure intervals are calculated, the most recent trouble-free operation time of the ship main engine and the recent failure frequency are counted, and the task execution capability of the ship main engine is calculated based on the log-normal distribution model and the failure influence coefficient. Based on the ideas of historical statistics and probability analysis, this method realizes the evaluation of the task execution capability of the ship main engine under uncertainty, provides decision support for the operation and maintenance of the ship main engine, and improves the operation and maintenance efficiency.

[0040] like Figure 1 As shown, a method for evaluating the task execution capability of a ship host based on historical statistics of the present invention comprises the following steps S1 to S4.

[0041] Step S1: Obtain the historical data of the operation time of the ship main engine, including the time point of each main engine startup, shutdown and failure, count the time intervals between failures of the ship main engine, and calculate the statistics of the time intervals between failures of the ship main engine, including the mean value μ and the standard deviation σ.

[0042] For the time interval between the ship's main engine from being put into use to the occurrence of n failures [T1, T2, ..., T n The calculation method of t1 is as follows: The calculation method of t1 is to count the cumulative running time of the host from the time the host is put into use to the time the first failure occurs, T i The calculation method of (2≤i≤n) is to count the cumulative running time of the host from the time the host recovers from the i-1st fault to the time the i-th fault occurs. The average fault interval time of the host Standard deviation of the host failure interval In this embodiment, a certain ship's main engine has experienced three cumulative failures since it was put into use. The calculated intervals between the failures are [525, 518, 520] (in hours), the average failure interval μ=521 (hours), and the standard deviation of the failure interval σ=2.94 (hours).

[0043] Step S2: Calculate the cumulative running time t of the ship's main engine after the last fault recovery. In this embodiment, the cumulative running time of a ship's main engine after the last (ie, the third) fault recovery is 47 hours.

[0044] Step S3: Count the frequency N1, N2, N3 of the first, second, and third level alarms of the main engine of the ship according to the sliding time window T; the length T of the sliding time window can be defined according to actual needs. In this embodiment, the sliding time window length T is set to 24 hours.

[0045] Step S4: Calculate the mission execution capability R of the ship main engine based on the log-normal distribution model and the fault impact coefficient. The calculation formula is:

[0046]

[0047] Among them, f(N1, N2, N3) is the fault influence coefficient, and its value range is (0, 1). The mission execution capability R of the ship main engine ranges from [0, 1]. Among them, the calculation formula of the fault influence coefficient f(N1, N2, N3) is:

[0048] f(N1,N2,N3)=α1×k(N1)+α2×k(N2)+α3×k(N3)

[0049] Among them, α1, α2, and α3 are weight coefficients, which respectively represent the degree of influence of the first-level, second-level, and third-level alarms on the mission execution capability of the ship's main engine. They are obtained through expert assignment and need to satisfy α1<α2<α3 and α1+α2+α3=1.

[0050] k(N i ) is the risk index of the first, second and third level alarms (1≤i≤3), and the calculation formula is:

[0051]

[0052] Among them, N i is the number of level i alarms that occur during the 24-hour sliding window of the ship’s main engine, β i To calculate the risk index parameters for level i alarm, we need to calculate the β i Conduct parameter adjustment training.

[0053] In this embodiment, the values ​​α1=0.2, α2=0.3, and α3=0.5 are assigned. The parameters β1=0.8, β2=0.4, and β3=0.1 are adjusted. This can meet the requirement that the more alarm times and the higher alarm levels, the smaller the value of the fault influence coefficient f(N1, N2, N3).

[0054] In this embodiment, the ship's main engine has one level 1 alarm and one level 2 alarm, but no level 3 alarm, within the 24-hour sliding time window. Therefore, N1=1, N2=1, and N3=0.

[0055] Finally, the mission execution capability of the ship's main engine in this case is calculated to be R=0.78.

[0056] The method of the above embodiment of the present invention is based on probability statistics theory, mines the fault occurrence law of the ship main engine from historical data, and comprehensively considers the historical fault law of the ship main engine and the characteristics of the recent alarm signal, completes the evaluation of the task execution capability of the ship main engine, and provides auxiliary decision-making for the maintenance and predictive maintenance of the ship main engine. The research of this method has important scientific value and engineering significance.

[0057] The technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0058] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for evaluating the mission execution capability of a ship main engine based on historical statistics, characterized in that: The method comprises: Obtain the operating time history data of the ship's main engine; Counting the time intervals between all failures of the ship's main engine according to the historical data; Determine the statistic of the interval time between main engine failures of the ship according to the interval time between all the failures; Determine the cumulative running time of the ship's main engine after the last fault recovery; According to a certain length sliding time window, the frequency of the first, second and third level alarms of the ship's main engine is counted; The mission execution capability of the ship's main engine is determined based on the log-normal distribution model and the fault influence coefficient.

2. The method for evaluating the mission execution capability of a ship main engine based on historical statistics according to claim 1 is characterized in that: The historical data includes: the time point of each host startup, shutdown and failure.

3. The method for evaluating the mission execution capability of a ship main engine based on historical statistics according to claim 1 is characterized in that: The statistics of the ship main engine failure interval time include a mean value μ and a standard deviation σ.

4. The method for evaluating the mission execution capability of a ship main engine based on historical statistics according to claim 3 is characterized in that: The determining of the mission execution capability of the ship main engine based on the log-normal distribution model and the fault influence coefficient includes: The mission execution capability R of the ship's main engine is determined according to the following formula: Among them, R ranges from [0,1], f(N1,N2,N3) is the fault influence coefficient, and its value range is (0,1], t is the cumulative running time of the ship main engine after the last fault recovery, N1, N2, N3 are the frequencies of the first, second, and third level alarms of the main engine according to the sliding time window T.

5. The method for evaluating the mission execution capability of a ship main engine based on historical statistics according to claim 4 is characterized in that: The counting of the intervals between all failures of the ship's main engine according to the historical data comprises: For the time interval between the ship's main engine from being put into use to the occurrence of n failures [T1, T2, ..., T n The calculation method of T1 is as follows: The calculation method of T1 is to count the cumulative running time of the host from the time the host is put into use to the time when the first failure occurs, T i The calculation method of (2≤i≤n) is to count the cumulative running time of the host from the time the host recovered from the i-1th fault to the time the i-th fault occurred; Average time between failures of the host Standard deviation of the host failure interval 6. The method for evaluating the mission execution capability of a ship main engine based on historical statistics according to claim 4 is characterized in that: The length T of the sliding time window for statistical failure frequency is defined according to actual needs.

7. The method for evaluating the mission execution capability of a ship main engine based on historical statistics according to claim 6 is characterized in that: The length of the sliding time window T for statistical failure frequency is less than μ.

8. The method for evaluating the mission execution capability of a ship main engine based on historical statistics according to claim 4 is characterized in that: The calculation formula of the fault influence coefficient f(N1, N2, N3) is: f(N1, N2, N3)=α1×k(N1)+α2×k(N2)+α3×k(N3) Among them, α1, α2, and α3 are weight coefficients, which respectively represent the influence of the first-level, second-level, and third-level alarms on the mission execution capability of the ship's main engine, α1<α2<α3 and α1+α2+α3=1; k(N i ) is the risk index of the first, second and third level alarms (1≤i≤3), and the calculation formula is: Among them, N i is the number of level i alarms that occur during the sliding time window T of the ship’s main engine, β i Calculate the risk index parameters for level i alarm.

9. The method for evaluating the mission execution capability of a ship main engine based on historical statistics according to claim 8 is characterized in that: The method further comprises: i Conduct parameter adjustment training.

10. The method for evaluating the mission execution capability of a ship main engine based on historical statistics according to claim 8, characterized in that: The basic criterion for assigning the values ​​of α1, α2, α3, β1, β2, and β3 parameters is: the more alarm times and the higher the alarm level, the smaller the value of the fault influence coefficient f(N1, N2, N3).

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