CN113537524A - Preventive maintenance decision method for engine cylinder block of engineering vehicle - Google Patents
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Abstract
The invention discloses a preventive maintenance decision method for an engine cylinder block of an engineering vehicle, which is carried out according to the following steps: s1, measuring the cylinder block abrasion amount of the engine cylinder block in different use time or driving mileage, and predicting the engine cylinder block abrasion based on the cylinder block abrasion amount; s2, carrying out service life distribution and parameter calculation of the engine cylinder; s3, firstly, calculating the expected cost of the engine cylinder block in unit time under the condition of long-term use; next, the variation of the expected cost per unit time of the engine block with the age replacement cycle is calculated. The method utilizes the RCM-based preventive maintenance monitoring model method to make the decision of the preventive maintenance period of the cylinder body, the decision obtained by the method has high reliability, and guiding opinions can be provided for the preventive maintenance of the engineering vehicle. The invention is suitable for the technical field of engineering vehicle maintenance and is used for providing maintenance decisions of the engine cylinder block of the engineering vehicle.
Description
Technical Field
The invention belongs to the technical field of maintenance of engineering vehicles, relates to a maintenance decision of an engine cylinder block of an engineering vehicle, and particularly relates to a preventive maintenance decision method of the engine cylinder block of the engineering vehicle.
Background
The engine is a key component of the engineering vehicle, and the running state of the engine directly influences the performance of the engine and the quality and efficiency of the completion of engineering tasks. In the construction process, once the engine is damaged, construction interruption can be caused, the progress of the project is greatly delayed, and the labor cost is increased. Therefore, it is necessary to perform preventive maintenance on the engine of the construction vehicle.
Among many faults of the engine, the cylinder wear has a serious influence on the engine performance, the environmental protection of the vehicle and the economy. Therefore, it is necessary to make a reasonable maintenance plan of the engine block of the vehicle and implement preventive maintenance of the engine block, which not only can effectively prolong the service life of the engine, but also can effectively avoid the influence of the engine fault on the construction.
Disclosure of Invention
The invention aims to provide a preventive maintenance decision method for an engine cylinder block of an engineering vehicle, which is used for deciding a preventive maintenance period of the cylinder block by using a preventive maintenance monitoring model method based on RCM.
In order to achieve the purpose, the invention adopts the following technical scheme:
a preventive maintenance decision method for an engine cylinder block of an engineering vehicle is carried out according to the following steps:
s1, predicting the abrasion loss of the engine cylinder block
Measuring cylinder block abrasion loss of the engine cylinder block in different use time or driving mileage, and predicting the engine cylinder block abrasion based on the cylinder block abrasion loss;
s2, carrying out service life distribution and parameter calculation of the engine cylinder;
s3 calculation of expected cost
S31, firstly, calculating expected cost EC (T) of the engine cylinder block in unit time under the condition of long-term use;
s32, secondly, calculating the change condition of the expected cost EC (T) of the engine cylinder block in unit time along with the working age replacement period T;
s4, determining the optimal replacement period
S41, obtaining a derivative of the unit time expected cost to T;
and S42, when the derivative is zero, the corresponding T is the optimal preventive maintenance period.
As a limitation: the step S1 includes the following procedures,
s11, firstly, dividing the degradation process of the engine cylinder block into n processes according to the Gamma process obeyed by the abrasion process of the engine cylinder block, wherein n is more than or equal to 1; let Xi(t), i is 1,2, L, n, which represents the amount of degradation of the i-th degradation process at time t, and Xi(0)=0;
S12, according to the fact that in the Gamma degradation process, the ith performance degradation process is in the time interval [ t, t + delta t ]]Increment of degradation Xi(t+Δt)-Xi(t) obtaining X following a Gamma distributioni(t+Δt)-Xi(t) distribution function and probability density function
Wherein, Delta Lambdai(t;γi)=Λi(t+Δt;γi)-Λi(t;γi),Λi(t;γi) Is a time scale transformation function and has
As a further limitation: the step S2 includes the following procedures,
s21, defining the system life as T on the basis of the random parameter Gamma processi={t:Xi(t)≥LiFor a particular individual, i.e. when λiWhen the number of the distribution function is constant, the cumulative distribution function of the life condition and the corresponding probability density function are respectively obtained as
S22, estimating Gamma process parameter by adopting a moment estimation method
First, the mean and variance of the Gamma process areWherein,means M representing the mean value of the amount of wear of the engine block at time t2(t) is the second-order central moment of the wear loss sample at time t;
As a further limitation: in step S31, the expected cost per unit time of the engine cylinder under the condition of long-term use can be expressed as,
where Cp and Cf are the cost of each preventive maintenance and troubleshooting of the engine block, T is the preventive maintenance cycle, RL(T)=GT(L),fL(t)=-dGt(L)/dt, GT (L) follows a Gamma distribution.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the technical progress that:
(1) the decision-making method disclosed by the invention is used for making the decision of the preventive maintenance period of the cylinder body by using the preventive maintenance monitoring model method based on RCM, the decision-making reliability obtained by the method is high, and guiding opinions can be provided for the preventive maintenance of the engineering vehicle;
(2) the method adopts a GAMMA process and a moment estimation method to deduce an engine cylinder block abrasion fault distribution model, and has higher precision compared with the prior method;
(3) the invention also provides a method for predicting the abrasion loss of the cylinder block, which provides quantitative basis for predicting the health state of the engine.
The invention is suitable for the technical field of engineering vehicle maintenance and is used for providing maintenance decisions of the engine cylinder block of the engineering vehicle.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
In the drawings:
FIG. 1 is a graph illustrating a cost analysis of a cylinder replacement cycle according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for purposes of illustration and explanation only and are not intended to limit the present invention.
Embodiment of the invention provides a preventive maintenance decision method for an engine cylinder block of an engineering vehicle
This example was carried out in the following sequence of steps:
s1, predicting the abrasion loss of the engine cylinder block
Measuring cylinder block abrasion loss of the engine cylinder block in different use time or driving mileage, and predicting the engine cylinder block abrasion based on the cylinder block abrasion loss; data statistics show that the abrasion of an engine cylinder body follows a Gamma process, in the abrasion process, the performance of the engine can be continuously degraded along with the increase of working time, and when the degradation exceeds a threshold value, a system can be failed;
the present step includes the following processes,
s11, firstly, according to the abrasion process of the engine cylinder block, the Gamma process is obeyed, and n performance degradation processes of the engine are assumed, wherein n is more than or equal to 1; let Xi(t), i is 1,2, L, n, which represents the amount of degradation of the i-th degradation process at time t, and Xi(0)=0;
S12, according to the i-th performance degradation in the Gamma degradation processThe process is carried out in the time interval [ t, t + delta t]Increment of degradation Xi(t+Δt)-Xi(t) obeying the Gamma distribution, taking into account the non-linearities of the system degradation process, in combination with a time-scale transformation function, Xi(t+Δt)-XiThe distribution function and probability density function of (t) can be expressed as
Wherein, Delta Lambdai(t;γi)=Λi(t+Δt;γi)-Λi(t;γi),Λi(t;γi) The time scale transformation function can convert the nonlinear degradation process into a linear degradation process, thereby facilitating the reliability analysis and the residual life prediction of a subsequent system. In engineering practice, it is common to use an exponential form, i.e.
S2, calculating the service life distribution and parameters of the engine cylinder
The present step includes the following processes,
s21, defining the system life as T on the basis of the random parameter Gamma processi={t:Xi(t)≥LiFor a particular individual, i.e. when λiWhen the number of the distribution function is constant, the cumulative distribution function of the life condition and the corresponding probability density function are respectively obtained as
S22, estimating Gamma process parameter by adopting a moment estimation method
First, the mean and variance of the Gamma process areWherein,hair with indicationMean value of abrasion loss of engine cylinder body at t moment, M2(t) is the second-order central moment of the wear loss sample at time t;
S3 calculation of expected cost
S31, first, calculating the expected cost EC (T) of the engine cylinder block in unit time under the condition of long-term use
Let Cp and Cf be the costs per preventive maintenance and troubleshooting of the engine block, respectively, T is the preventive maintenance cycle, and the expected cost per unit time of the engine block under long-term use can be expressed as,
where Cp and Cf are the cost of each preventive maintenance and troubleshooting of the engine block, T is the preventive maintenance cycle, RL(T)=GT(L),fL(t)=-dGt(L)/dt,GT(L) obeys a Gamma distribution;
s32, on the basis of the above, calculating by MATLAB to obtain the expected cost per unit time ec (T) of the engine block as a function of the age replacement period T, as shown in fig. 1, which is the expected cost per unit time ec (T) of the engine block as a function of the age replacement period T in the present embodiment, it can be seen from the figure that the expected cost per unit time gradually decreases as the preventive maintenance period changes, and gradually increases after reaching the lowest point. The optimal maintenance period corresponds to the location of the lowest point of the expected cost per unit time;
s4, determining the optimal replacement period
S41, obtaining a derivative of the unit time expected cost to T;
at S42, T when the derivative is zero is the optimal preventive maintenance period, i.e. the position of the lowest point of the expected cost per unit time in fig. 1.
Claims (4)
1. A preventive maintenance decision method for an engine cylinder block of an engineering vehicle is characterized by comprising the following steps:
s1, predicting the abrasion loss of the engine cylinder block
Measuring cylinder block abrasion loss of the engine cylinder block in different use time or driving mileage, and predicting the engine cylinder block abrasion based on the cylinder block abrasion loss;
s2, carrying out service life distribution and parameter calculation of the engine cylinder;
s3 calculation of expected cost
S31, firstly, calculating expected cost EC (T) of the engine cylinder block in unit time under the condition of long-term use;
s32, secondly, calculating the change condition of the expected cost EC (T) of the engine cylinder block in unit time along with the working age replacement period T;
s4, determining the optimal replacement period
S41, obtaining a derivative of the unit time expected cost to T;
and S42, when the derivative is zero, the corresponding T is the optimal preventive maintenance period.
2. The preventive maintenance decision method for the engine block of the engineering vehicle as claimed in claim 1, characterized in that: the step S1 includes the following procedures,
s11, firstly, dividing the degradation process of the engine cylinder block into n processes according to the Gamma process obeyed by the abrasion process of the engine cylinder block, wherein n is more than or equal to 1; let Xi(t), i is 1,2, L, n, which represents the amount of degradation of the i-th degradation process at time t, and Xi(0)=0;
S12, according to the fact that in the Gamma degradation process, the ith performance degradation process is in the time interval [ t, t + delta t ]]Increment of degradation Xi(t+Δt)-Xi(t) obtaining X following a Gamma distributioni(t+Δt)-Xi(t) distribution function and probability density function
3. The preventive maintenance decision method for the engine block of the engineering vehicle as claimed in claim 2, characterized in that: the step S2 includes the following procedures,
s21, defining the system life as T on the basis of the random parameter Gamma processi={t:Xi(t)≥LiFor a particular individual, i.e. when λiWhen the number of the distribution function is constant, the cumulative distribution function of the life condition and the corresponding probability density function are respectively obtained as
S22, estimating Gamma process parameter by adopting a moment estimation method
First, the mean and variance of the Gamma process areWherein,means M representing the mean value of the amount of wear of the engine block at time t2(t) is the second-order central moment of the wear loss sample at time t;
4. A preventive maintenance decision method for an engine block of an engineering vehicle according to claim 3, characterized in that: in step S31, the expected cost per unit time of the engine cylinder under the condition of long-term use can be expressed as,
where Cp and Cf are the cost of each preventive maintenance and troubleshooting of the engine block, T is the preventive maintenance cycle, RL(T)=GT(L),fL(t)=-dGt(L)/dt,GT(L) obeys a Gamma distribution.
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115420501A (en) * | 2022-11-04 | 2022-12-02 | 山东驰勤机械有限公司 | Gearbox running management and control system based on artificial intelligence |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102519733A (en) * | 2011-12-02 | 2012-06-27 | 南京航空航天大学 | Method for assessing flying reliability of aircraft engine on basis of monitoring information fusion |
US20140365178A1 (en) * | 2010-10-28 | 2014-12-11 | Eads Deutschland Gmbh | Maintenance information device, condition sensor for use therein and method which can be carried out therewith for arriving at a decision whether or not to perform servicing or maintenance |
CN104463421A (en) * | 2014-11-06 | 2015-03-25 | 朱秋实 | Big data modeling equipment dynamic optimization maintenance method based on real-time status |
CN106647273A (en) * | 2016-12-26 | 2017-05-10 | 北京天源科创风电技术有限责任公司 | Method and device for preventability replacing time of prediction part |
-
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- 2021-07-19 CN CN202110814068.3A patent/CN113537524A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140365178A1 (en) * | 2010-10-28 | 2014-12-11 | Eads Deutschland Gmbh | Maintenance information device, condition sensor for use therein and method which can be carried out therewith for arriving at a decision whether or not to perform servicing or maintenance |
CN102519733A (en) * | 2011-12-02 | 2012-06-27 | 南京航空航天大学 | Method for assessing flying reliability of aircraft engine on basis of monitoring information fusion |
CN104463421A (en) * | 2014-11-06 | 2015-03-25 | 朱秋实 | Big data modeling equipment dynamic optimization maintenance method based on real-time status |
CN106647273A (en) * | 2016-12-26 | 2017-05-10 | 北京天源科创风电技术有限责任公司 | Method and device for preventability replacing time of prediction part |
Non-Patent Citations (2)
Title |
---|
王宇: "轴向柱塞泵性能可靠性建模与维修策略优化", 《工程科技Ⅱ辑》, no. 09, 15 September 2019 (2019-09-15), pages 29 - 33 * |
袁亚辉: "基于独立增量过程的列控车载设备视情维修方法研究", 《工程科技Ⅱ辑》, no. 03, 15 March 2021 (2021-03-15), pages 29 - 38 * |
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115420501A (en) * | 2022-11-04 | 2022-12-02 | 山东驰勤机械有限公司 | Gearbox running management and control system based on artificial intelligence |
CN115420501B (en) * | 2022-11-04 | 2023-01-24 | 山东驰勤机械有限公司 | Gearbox running management and control system based on artificial intelligence |
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