CN114662314A - Working condition switching-oriented marine diesel engine thermal parameter estimation method - Google Patents
Working condition switching-oriented marine diesel engine thermal parameter estimation method Download PDFInfo
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Abstract
A working condition switching-oriented ship diesel engine thermal parameter estimation method belongs to the field of ship diesel engine thermal parameter identification and comprises the following steps: under different working conditions, acquiring thermal parameters such as rotating speed, exhaust temperature, power and the like in the working process of a diesel engine bench experiment table and preprocessing the thermal parameters; respectively analyzing relation curves between three variables of rotating speed, exhaust temperature and power and load working conditions through polynomial fitting, giving a fitting curve effect graph, and evaluating the fitting effect by Mean Absolute Percentage Error (MAPE); and then, the relation between the rotating speed, the exhaust temperature and the power and the propulsion working condition is respectively analyzed through polynomial fitting, a fitting curve graph is given, and the fitting effect is evaluated by MAPE indexes, so that the accurate estimation of the thermal parameters of the marine diesel engine under the condition of various working condition changes is realized. The working condition switching-oriented marine diesel engine thermal parameter estimation method provided by the invention can improve the accuracy of parameter estimation, better conform to the actual working condition of the marine diesel engine, and can be effectively applied to scenes with frequent switching of actual working conditions. Finally, the thermal parameter estimation method under the common working condition can be expanded to the estimation of the thermal parameters under any working condition, and the method has strong expansibility and practicability.
Description
Technical Field
The invention belongs to the field of thermal parameter identification of marine diesel engines, and relates to a working condition switching-oriented method for estimating thermal parameters of a marine diesel engine.
Background
In various power equipment, the diesel engine is widely applied in the ship transportation industry due to the advantages of high thermal efficiency, good economical efficiency, strong adaptability and the like, and is a power source spring and heart of an underway ship. In recent years, marine diesel engines have been developed as the most popular marine power plants in the world, and among the existing large and medium-sized civil ships, more than 90% of the diesel engines are used as main propulsion units, and only in some specific fields, internal combustion engines, steam engines, and the like are used. The diesel engine has complex working process, a plurality of influence factors and a large amount of thermal parameters, and the parameters have the characteristics of a plurality of parameter types, wide distribution range, large data volume and the like. On the other hand, the large quantity and wide distribution of thermal parameters often carry a lot of key information and can reflect the operation state of the diesel engine to a great extent. In the actual working scene of the diesel engine, the running state of the diesel engine is difficult to judge and estimate due to the complex running environment inside and outside the diesel engine, and great hidden danger is brought to the safety and the stability of the diesel engine. The method can deduce the running state of the diesel engine to a greater extent by estimating the state of the typical thermal parameters of the diesel engine, has the advantages of simplicity, directness and strong reliability, has important theoretical value and application prospect for guaranteeing the economy and high-quality running of the marine diesel engine, and is an effective means for improving the intelligent level of a marine power system.
For multivariable complex systems such as marine diesel engines, most of the existing research is to estimate or identify parameters of a certain fixed working condition. However, diesel engines often operate with varying external environmental and energy distribution requirements, which often requires changing operating conditions to achieve optimal efficiency. When the working conditions of the diesel engine are switched, various thermal parameters of the diesel engine change accordingly, and at the moment, a model established aiming at a single working condition cannot accurately give an estimation result of a target parameter under the changed working condition, so that the work of follow-up research (such as fault diagnosis and early warning) is influenced. How to establish an estimation model of typical thermal parameters under the condition of working condition change not only has important theoretical value but also meets the actual requirements of intelligent operation and maintenance of the diesel engine. The patent provides an effective method for estimating thermotechnical parameters under variable working conditions, which effectively solves the problem of parameter estimation of a marine diesel engine after working condition switching according to actual requirements and has certain theoretical reference value and practical application prospect.
Disclosure of Invention
The invention aims to solve the problem of estimation of main thermal parameters such as rotating speed, exhaust temperature, power and the like of the marine diesel engine during working condition change. An effective working condition-variable thermal parameter estimation method is provided, so that the accurate estimation of the thermal parameters of the marine diesel engine after the working condition is changed is realized.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for estimating thermal parameters of a marine diesel engine under the condition of variable working conditions comprises the steps of firstly collecting working data under different working conditions generated by a rack experiment table of a MAN company 6S35ME-B9 diesel engine, then respectively analyzing relation curves between three variables of rotating speed, exhaust temperature and power and the variable load working conditions through polynomial fitting, giving a fitting curve effect diagram, estimating the fitting effect by Mean Absolute Percentage Error (MAPE) indexes, then respectively analyzing relation curves between the three variables of rotating speed, exhaust temperature and power and the variable propulsion working conditions through polynomial fitting, giving a fitting curve effect diagram, and similarly estimating the fitting effect by the MAPE indexes, thereby realizing the accurate estimation of the thermal parameters of the marine diesel engine when various main working conditions are changed. The method specifically comprises the following steps:
step S1: under different working conditions, data of rotating speed, exhaust temperature and power variable in the working process of the diesel engine bench experiment table are collected and preprocessed.
In order to ensure the effectiveness of parameter fitting realized by the method provided by the invention, the state parameters of the diesel engine under different working conditions need to be processed. The different working conditions specifically refer to: in order to better fit the relation between the working condition and the rotating speed, the exhaust temperature and the power, the fitting result is closer to the real state, and data with different propulsion and load working conditions are acquired and processed respectively. In addition, since the exhaust temperature of the bench test often has an abnormal point (i.e., the reading number is significantly higher than the normal level) in the acquisition process, the value of the last detection time can be directly used for replacing the abnormal data. And acquiring and processing the state parameters under different working conditions to obtain the monitoring variables.
Step S2: under the condition of changing load working conditions, a relation curve model between the characteristic thermal parameters and the characteristic thermal parameters is established by adopting polynomial fitting, and the fitting effect is evaluated by MAPE indexes:
because the invention adopts the working data generated by the bench test bench of the diesel engine to carry out the test, in order to ensure that other process variables are kept stable in the sampling process, when the working data under the working condition of variable load is collected, the engine is set to work at constant rotating speed to simulate the condition that the ship speed is invariable all the time under the working condition of ship load. Therefore, the rotation speed of the diesel engine is always kept constant even when the operating condition of the diesel engine changes.
When a relation curve of the exhaust temperature changing along with the load working condition is fitted, wherein the process data acquisition time of the 25% load working condition is 2016 year 5 month 14 day 11:30:02 to the current day 11:34:52, the process data acquisition time of the 50% load working condition is 2016 year 5 month 14 day 11:45:02 to the current day 11:49:52, the process data acquisition time of the 75% load working condition is 2016 year 5 month 14 day 12:04:52 to the current day 12:09:42, the process data acquisition time of the 90% load working condition is 2016 year 5 month 14 day 12:14:52 to the current day 12:19:42, and the sampling interval is 10s, namely 30 sample points are taken under each working condition. The final fit curve is shown as follows:
T＝4.1935*10-4t3-0.0811t2+5.9992t+161.4392
wherein T is the exhaust temperature (degC) and T is the load condition. The MAPE of the obtained curve and the actual monitoring data is 0.52 percent through calculation. The MAPE index expression is as follows:
wherein the content of the first and second substances,
estimated value, y, representing fitted curveiRepresenting the actual measurement. The MAPE is a percentage value and represents the average deviation degree of the estimation result from the real result, and the smaller the MAPE value is, the higher the accuracy of parameter estimation is.
When a relation curve of power changing along with the load working condition is fitted, the invention collects power data (25% load working condition, 50% load working condition, 75% load working condition and 90% load working condition) at the same time point as the exhaust temperature data of the load working condition, and the sampling interval is 10s, namely 30 sample points are taken in each working state. The final fitting results are expressed as the following curve:
P＝-0.0073t3+1.2661t2-29.8399t+1014.3385
wherein P is power (kW) and t is load condition. The MAPE of the obtained curve and the actual monitoring data is 3.87% through calculation.
Step S3: under the variable propulsion working condition, a relation curve model between the variable propulsion working condition and typical thermal parameters is established by adopting polynomial fitting, and the fitting effect is evaluated by MAPE indexes:
when a relation curve of the change of the rotating speed along with the propulsion working condition is fitted, the process data acquisition time of 25% of the propulsion working condition is 2016 year 5 month 14 day 12:59:52 to the current day 13:06:02, the process data acquisition time of 50% of the propulsion working condition is 2016 year 5 month 14 day 12:44:52 to the current day 12:51:02, the process data acquisition time of 75% of the propulsion working condition is 2016 year 5 month 14 day 12:29:52 to the current day 12:36:02, the process data acquisition time of 90% of the propulsion working condition is 2016 year 5 month 14 day 12:14:52 to the current day 12:21:02, and the sampling intervals are all 10 s. It is noted that 38 sample points were taken for all operating conditions except for 20 sample points at 25% propulsion (the value of the subsequent 18 sample points is directly truncated to 0). The final fit curve is shown as follows:
n＝-0.0067t2+1.6392t+47.2291
wherein n is the rotating speed (r/min) and t is the propelling working condition. The MAPE of the obtained curve and the actual monitoring data is 14.6% through calculation.
When a relation curve of exhaust temperature changing along with the propulsion working condition is fitted, the exhaust temperature data (25% propulsion working condition, 50% propulsion working condition, 75% propulsion working condition and 90% propulsion working condition) at the same time point as the propulsion working condition rotating speed data are collected, the sampling interval is 10s, and 38 sample points are taken in each working state. The final fit curve is shown as follows:
T＝5.1058*10-4t3-0.124t2+10.6154t+25.7013
wherein T is the exhaust temperature (degC) and T is the propulsion condition. The MAPE of the obtained curve and the actual monitoring data is 7.69 percent through calculation.
When a relation curve of power changing along with the propulsion working condition is fitted, the power data (25% propulsion working condition, 50% propulsion working condition, 75% propulsion working condition and 90% propulsion working condition) of the same time point as the above-mentioned propulsion working condition rotating speed data are collected, the sampling interval is also 10s, and except that the number of sample points under the 25% propulsion working condition is 19 (the value of the subsequent 19 sample points is directly omitted), 38 sample points are taken under other working conditions. The final fit is shown as follows:
P＝-0.0165t3+2.8921t2-112.8566t+1960.5187
wherein, P is power (kW), and t is a propulsion working condition. The result curve was calculated to be 2287% MAPE of the actual monitored data.
The curve fitting result of the method between the power variable, the rotating speed variable and the changed propelling working condition is not ideal, and a person thinks that the reason of the method is probably the situation that direct shutdown exists in the later stage of the process of acquiring the variable under the propelling working condition, so that part of the variables are subjected to step change, but the method still can meet the actual application requirement, and can obtain a better parameter estimation theoretical result for the rest conditions.
From the foregoing, it is readily apparent that the exhaust temperature and power parameters (speed at load, not considered) generally increase with increasing diesel engine load. In fact, the larger the load in the working process of the diesel engine is, the larger the acting force and the torque required by the engine when the engine does work are, and the higher the pressure and the temperature generated by the explosion of the fuel in the cylinder are, which is shown in the thermal parameters of the exhaust temperature and the power that the whole curve shows the rising trend along with the increase of the load working condition. On the other hand, in the conclusion of the patent, the rotating speed, the exhaust temperature and the power also increase along with the increase of the propulsion working condition, which is shown in the practical situation, namely the increase of the propulsion working condition directly leads to the increase of the oil supply amount of the diesel engine and the opening of the air door, the pressure in the combustion chamber is increased, further the torque and the rotating speed of the engine are increased, and higher exhaust temperature and power are generated along with the increase of the oil supply amount of the diesel engine, which shows that the estimation of typical thermal parameters of the patent at the time of changing the working condition is consistent with the actual situation, and the application value is very high.
When the fitting model is established by using the polynomial aiming at the situation of variable working conditions, the method only collects data under the working conditions of 25%, 50%, 75% and 90%, and actually collects data covering more and more full working conditions for research if the conditions allow, so that the reliability of the experimental result is further improved. On the other hand, in practical application, the model can be used for obtaining the estimation of the target parameters under the working conditions of any state, so that the method has strong expansibility.
Compared with the prior art, the invention has the following beneficial effects: the accuracy of parameter estimation can be improved, the actual working condition of the marine diesel engine is better met, and the method can be effectively applied to scenes with frequent switching of actual working conditions; the final model parameters can be reduced by adopting a polynomial fitting mode, the complexity of the model is reduced, and the calculation efficiency is improved; the single thermal parameter estimation method can be expanded to the estimation of any parameter, and has strong expansibility and practicability.
Drawings
FIG. 1 is a flow chart of a working condition switching-oriented diesel engine thermal parameter estimation method;
FIG. 2 is a graph of exhaust temperature as a function of load conditions;
FIG. 3 is a graph of power as a function of load conditions;
FIG. 4 is a graph of rotational speed as a function of propulsion conditions;
FIG. 5 is a graph of exhaust temperature as a function of propulsion conditions;
FIG. 6 is a graph of power as a function of propulsion conditions.
Detailed Description
The present invention will be further described below with reference to specific examples of fitting rotational speed, exhaust temperature, and power when switching between load and propulsion conditions, respectively, but the scope of the present invention is not limited to the following examples. The complete implementation process comprises the following steps:
step S1: the method comprises the steps of collecting data of rotating speed, exhaust temperature and power of a marine diesel engine under different working conditions and preprocessing the data. Specifically, the patent takes a diesel engine of MAN company 6S35ME-B9 as an example, and for the load condition: collecting exhaust temperature and power data when the load working condition is 25% in Beijing at 2016, 5, 14 days 11:30:02 to 2016, 5, 14 days 11:34:52, collecting exhaust temperature and power data when the load working condition is 50% in 2016, 5, 14 days 11:45:02 to 2016, 5, 14 days 11:49:52, 5, 14 days 12:04:52 to 2016, 5, 14 days 12:09:42, collecting exhaust temperature and power data when the load working condition is 75%, 14 days 12:14:52 to 2016, 5, 14 days 12:19:42, collecting exhaust temperature and power data when the load working condition is 90%, wherein the sampling interval is 10s, namely 30 sample points are taken under each load working condition; for the propulsion condition: the method comprises the steps of collecting rotation speed, exhaust temperature and power data when a propulsion working condition is 25% in Beijing at 2016 (12: 59: 52) -2016 (5-2016) (14: 13: 02), collecting rotation speed, exhaust temperature and power data when the propulsion working condition is 50% in 2016 (12: 44: 52) -2016 (5-2016) (14: 51: 02), collecting rotation speed, exhaust temperature and power data when the propulsion working condition is 75% in 2016 (12: 29: 52) -2016 (5-2016) (14: 36: 02), collecting rotation speed, exhaust temperature and power data when the propulsion working condition is 90% in 2016 (12: 14: 52) -2016 (5-2016) (14: 21: 02), and collecting the rotation speed, exhaust temperature and power data when the propulsion working condition is 90%, wherein sampling intervals are 10s, namely 38 sample points are obtained in each propulsion working condition. In addition, the abnormal point (i.e., the reading number is significantly higher than the normal level) existing in the collection process of the exhaust temperature for the bench test is directly replaced with the value at the last detection time.
Step S2: under the condition of changing load working condition, a relation curve model between the characteristic thermal parameters and the characteristic thermal parameters is established by adopting a polynomial fitting method, and the fitting effect is evaluated by MAPE indexes. The rotating speed of the bench test bed is constant under the load working condition, so that the rotating speed of the engine does not change under the load working condition;
when the relation curve of the exhaust temperature changing along with the load condition is fitted, the relation curve of the exhaust temperature changing along with the load condition is fitted by adopting the exhaust temperature data under the load condition changing in the step S1 (the exhaust temperature data under all the load conditions take 30 sample points), and the relation expression of the exhaust temperature changing along with the load condition is obtained:
T＝4.1935*10-4t3-0.0811t2+5.9992t+161.4392
wherein T is the exhaust temperature (degC) and T is the load condition. MAPE was calculated to be 0.52%, indicating good accuracy of the fit.
When fitting the relation curve of the power changing with the load condition, fitting the relation curve of the power changing with the load condition by adopting the power data under the changing load condition in the step S1 (the power data under all the load conditions take 30 sample points), and obtaining a relation expression of the power changing with the load condition:
P＝-0.0073t3+1.2661t2-29.8399t+1014.3385
wherein P is power (kW), and t is load working condition. Through calculation, the MAPE of the obtained result curve and the actual monitoring data is 3.87%, and the fitting effect is good.
Step S3: under the variable propulsion working condition, a relation curve model between the variable propulsion working condition and typical thermal parameters is established by adopting polynomial fitting, and the fitting effect is evaluated by MAPE indexes.
When fitting the relation curve of the rotation speed changing along with the propulsion working condition, fitting the relation curve of the rotation speed changing along with the propulsion working condition by adopting the rotation speed data under the propulsion working condition (wherein the rotation speed data of the first 20 sample points are taken under 25% of the propulsion working condition, and the rotation speed data of the 38 sample points are taken under other propulsion working conditions) in the step S1 to obtain a relation expression of the rotation speed changing along with the propulsion working condition:
n＝-0.0067t2+1.6392t+47.2291
wherein n is the rotating speed (r/min) and t is the propelling working condition. Through calculation, the MAPE of the obtained result curve and the actual monitoring data is 14.6%, and the fitting effect is poor.
When fitting the relation curve of the exhaust temperature changing along with the propulsion condition, fitting the relation curve of the exhaust temperature changing along with the propulsion condition by adopting the exhaust temperature data under the propulsion condition in the step S1 (the exhaust temperature data under all the propulsion conditions take 38 sample points), and obtaining the relation expression of the exhaust temperature changing along with the propulsion condition:
T＝5.1058*10-4t3-0.124t2+10.6154t+25.7013
wherein T is the exhaust temperature (degC) and T is the propulsion condition. Through calculation, the MAPE of the obtained curve and the actual monitoring data is 7.69%, and the fitting effect is general.
When fitting the relation curve of the power changing along with the propulsion condition, fitting the relation curve of the power changing along with the propulsion condition by adopting the power data under the propulsion condition in the step S1 (wherein, the power data of the first 19 sample points are taken under the 25% propulsion condition, and the power data of the 38 sample points are taken under other propulsion conditions), and obtaining the relation expression of the power changing along with the propulsion condition:
P＝-0.0165t3+2.8921t2-112.8566t+1960.5187
wherein, P is power (kW), and t is propulsion condition. Through calculation, the obtained curve has poor fitting effect with MAPE (MAPE) 2287% of actual monitoring data.
The above-mentioned embodiments only express the embodiments of the present invention, but not should be understood as the limitation of the scope of the invention patent, it should be noted that, for those skilled in the art, many variations and modifications can be made without departing from the concept of the present invention, and these all fall into the protection scope of the present invention.
Claims (2)
1. A working condition switching-oriented marine diesel engine thermal parameter estimation method. The method is characterized in that key thermal parameters are respectively collected according to common different working conditions, then relation curves between three typical thermal parameters of rotating speed, exhaust temperature and power and the changed load working conditions are respectively analyzed through polynomial fitting, a fitting curve effect graph is given, relation curves between the rotating speed, the exhaust temperature and the changed propulsion working conditions are respectively analyzed through polynomial fitting, a fitting curve effect graph is given, and thermal parameter estimation under the working condition switching condition is achieved. The method specifically comprises the following steps:
step S1: under different working conditions, acquiring data of rotating speed, exhaust temperature and power variable in the working process of a diesel engine bench experiment table and preprocessing the data;
and collecting and processing the state parameters of the diesel engine under different working conditions. The different working conditions specifically refer to: in order to better fit the relation between the working condition and the rotating speed, the exhaust temperature and the power, the fitting result is closer to the real state, and data with different propulsion and load working conditions are acquired and processed respectively. In addition, since the exhaust temperature of the bench test often has an abnormal point (i.e., the reading number is significantly higher than the normal level) in the acquisition process, the value of the last detection time can be directly used for replacing the abnormal data.
Step S2: under the condition of changing load, a relation curve model between the characteristic thermal parameters and the characteristic thermal parameters is established by adopting polynomial fitting, and the fitting effect is evaluated by Mean Absolute Percentage Error (MAPE);
in order to ensure that other residual thermotechnical parameters in the acquisition process are kept stable, the method keeps the engine working at a constant rotating speed when acquiring process data under a load working condition so as to simulate the situation that the ship speed is not changed when the ship load works;
the relationship curve between the variable load condition and the exhaust temperature is fitted, and the result is as follows:
T＝4.1935*10-4t3-0.0811t2+5.9992t+161.4392
wherein T is exhaust temperature (degC) and T is load working condition; calculating to obtain MAPE between the result curve and the actual monitoring data which is 0.52%;
the relationship curve between the variable load condition and the power is fitted according to the variable load condition, and the result is as follows:
P＝-0.0073t3+1.2661t2-29.8399t+1014.3385
wherein P is power (kW) and t is working condition; calculating to obtain a result curve, wherein MAPE of the result curve and actual monitoring data is 3.87%;
step S3: under the variable propulsion working condition, a relation curve model between the propulsion working condition and typical thermal parameters is established by adopting polynomial fitting, and the fitting effect is evaluated by MAPE indexes;
the relationship curve between the variable propulsion conditions and the rotating speed is fitted, and the result is as follows:
n＝-0.0067t2+1.6392t+47.2291
wherein n is the rotating speed (r/min) and t is the propelling working condition; calculating to obtain a result curve, wherein MAPE of the result curve and actual monitoring data is 14.6%;
the relationship between the temperature of the exhaust gas and the variation of the propulsion condition is fitted, and the result is as follows:
T＝5.1058*10-4t3-0.124t2+10.6154t+25.7013
wherein T is exhaust temperature (degC) and T is propulsion condition; calculating to obtain a result curve and the MAPE of the actual monitoring data which is 7.69%; in addition, the final model parameters can be reduced by adopting a polynomial fitting mode, the complexity of the model is reduced, and the calculation efficiency is improved.
The relationship curve between the variable propulsion conditions and the power is fitted, and the result is as follows:
P＝-0.0165t3+2.8921t2-112.8566t+1960.5187
wherein P is power (kW), and t is a propulsion working condition; the result curve is calculated to be 2287% of MAPE of the actual monitoring data.
2. The method for estimating the thermal parameters of the marine diesel engine under the condition of variable working conditions according to claim 1, wherein the steps S2 and S3 are as follows:
the polynomial fitting is a linear combination of high-order terms (cubic terms of input variables are adopted at the highest level in the patent) of the input variables (x), and the expression is as follows:
wherein the content of the first and second substances,
representing an estimated value, w, based on an input variable x and a parameter wiRepresenting the target parameter. The obtained model can be operated efficiently like a linear model, and meanwhile, the model can be suitable for the situation of all working conditions. Using the sum of squared errors as a loss function, the expression is as follows:
wherein E (w) represents a result loss, N represents the number of test samples, and xnDenotes the nth test sample data, tnAnd representing the actual label of the nth test sample, and obtaining an optimal estimation model when the loss function takes the minimum value.
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