CN112699609A - Diesel engine reliability model construction method based on vibration data - Google Patents
Diesel engine reliability model construction method based on vibration data Download PDFInfo
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Abstract
The invention discloses a method for constructing a reliability model of a diesel engine based on vibration data, and belongs to the technical field of monitoring of running states of the diesel engine. The method is based on diesel engine vibration data, a reliability evaluation index system is determined through combined optimization of a channel and indexes, and then machine learning is carried out based on test working condition data to automatically identify working conditions based on the evaluation index system, so that real-time transverse statistical analysis and trend analysis of the same working condition data are realized, and the reliability level of the diesel engine is evaluated. The model construction comprises preprocessing based on the reliability test data of the diesel engine, construction of an evaluation system, working condition division, data rearrangement, statistical characteristic analysis and trend analysis.
Description
Technical Field
The invention relates to the technical field of diesel engine running state monitoring, in particular to a method for constructing a diesel engine reliability model based on vibration data.
Background
The diesel engine has the characteristics of long service life, high reliability requirement, long time, large investment, high risk, more data and the like, and belongs to a typical multi-working-condition, multi-task and complex reciprocating mechanical system. In the development stage of the novel diesel engine, reliability tests are required to be carried out before performance identification and user delivery. Reliability tests are important tests for checking the design, assembly and process levels of diesel engines, and usually require continuous cycle profile tests for one month or more under laboratory conditions. The reliability test evaluation is based on test data, and objective and accurate evaluation conclusions are given to the normalization, the effectiveness, the reliability index and the like in the test process.
The classical reliability test evaluation method is usually based on test time, counts the number of times of fault data, and adopts a reliability statistical model of timing truncation (specified test time) or fixed number truncation (specified number of times of faults) for evaluation. For the reliability test evaluation of the diesel engine, the method has the following obvious defects: firstly, the reliability test time of the diesel engine is relatively short (compared with the service time), and zero fault condition can occur; secondly, the diesel engine belongs to typical mechanical equipment, the current reliability test evaluation method is derived from electronic equipment, the reliability test evaluation method is different from accidental failure of the electronic equipment, failure of the diesel engine mainly comprises fatigue, fracture and the like, and the failure mechanisms are different, so that the existing evaluation method is not applicable; thirdly, the existing reliability evaluation method is difficult to reflect the self-running characteristics of the diesel engine, and only the failure times are taken as judgment bases. A large amount of operating parameters representing the technical state of the diesel engine are generated in the operating process of the diesel engine, and the reliability level of the diesel engine can be more effectively evaluated by modeling and analyzing the operating parameters.
Disclosure of Invention
In view of the above, the invention provides a method for constructing a diesel engine reliability model based on vibration data, which is based on the diesel engine vibration data, determines a reliability evaluation index system through the joint optimization of a channel and an index, and further establishes a working condition automatic identification method based on the evaluation index system based on the machine learning of test working condition data, thereby realizing the real-time transverse statistical analysis and trend analysis of the same working condition data and evaluating the reliability level.
A diesel engine reliability model construction method based on vibration data comprises the following implementation steps:
the method comprises the following steps: preprocessing vibration data acquired in the reliability test;
step two: performing combined optimization on the vibration test channel and the analysis indexes, and selecting a plurality of optimal vibration test points and corresponding optimal evaluation indexes to form a vibration characteristic evaluation system;
step three: carrying out automatic working condition division on the vibration signals acquired in real time;
step four: classifying and rearranging the data of the same operation working condition according to the time sequence based on the vibration signal working condition division result;
step five: and for each operating condition, analyzing the statistical characteristics (including average value, variance, peak-to-peak value and the like) of the vibration data characteristics in the same operating condition and analyzing the trend based on the vibration characteristic evaluation system established in the step two, so as to judge the vibration stability level of the tested diesel engine under different operating conditions and further analyze the reliability.
Further, the vibration data preprocessing in the first step comprises: counting whether the vibration data have data loss and abnormal values; eliminating the abnormality caused by non-technical factors; and labeling the effective vibration data according to the operation condition.
Further, the joint optimization process in the second step comprises: and respectively calculating a plurality of vibration evaluation indexes for a plurality of vibration measuring point data selected by experience, and preferably selecting a plurality of optimal vibration measuring points and corresponding optimal evaluation indexes in a combined manner according to the principle that the index difference is maximum under different working conditions and the index difference is minimum under the same working condition.
Further, the process of automatic operating condition division in the third step includes: establishing a nonlinear mapping relation between a vibration signal index and an actual operation condition; and based on labeled vibration data, developing one-dimensional signal support vector machine learning, and automatically dividing working conditions of the vibration signals acquired in real time according to the trained support vector machine.
Furthermore, the vibration characteristic evaluation system is obtained by adopting a characteristic selection method based on distance calculation in a clustering analysis idea, and for the signal of each vibration test channel, C working conditions are assumed, and N working conditions are assumedcIf each data segment has Q indexes to be selected, a characteristic index set can be constructed
{In,c,q,n＝1,2,…,Nc；c＝1,2,…,C；q＝1,2,…,Q}
Wherein, In,c,qIs the nth characteristic value of the qth index of the c working condition; n is a radical ofcIs the sample number of the c-th working condition; q is the number of indexes;
a) in-service distance assessment
Firstly, calculating the average distance in the working condition of the same working condition
Then obtaining the average value of the distances in the C working conditions
Defining and calculating a difference factor of distances within a working condition
b) Inter-operating condition distance assessment
Firstly, calculating the average value of each characteristic of the same working condition
Then obtaining the average distance between different working conditions
Defining and calculating a difference factor of the distance between the operating conditions
c) Integrated distance assessment
Firstly, defining and calculating the weighting factor of each index characteristic
Calculating the ratio of inter-and intra-regime distances with weighting factors
Finally, obtaining a comprehensive distance evaluation factor by utilizing a maximum normalization method
Distance evaluation factor calculated according to each index of each vibration channel signal
And (4) combining and preferably selecting the sensitive channels and the corresponding sensitive characteristic indexes from large to small to form an evaluation system of the vibration characteristics.
Has the advantages that:
1. the model construction comprises preprocessing based on the reliability test data of the diesel engine, construction of an evaluation system, working condition division, data rearrangement, statistical characteristic analysis and trend analysis.
2. The traditional reliability evaluation method based on the fault rate does not fully consider the self dynamic characteristics of equipment and the difference of operating conditions, only evaluates the reliability evaluation method from the aspect of fault occurrence probability, and various complex influence factors are easy to be confused and even interfered. The reliability evaluation method of the diesel engine is used for evaluating the reliability of the diesel engine based on the characteristic index statistical characteristics and trend characteristics of the vibration data. The vibration data adopted by the evaluation is subjected to preliminary cleaning, channel and index screening and automatic classification of working conditions, so that the influence of the dynamic characteristics of the equipment on the running state can be fully disclosed, the interference of variable working conditions on characteristic parameters is eliminated, and the reliability evaluation result is more real and credible.
Drawings
FIG. 1 is a flow chart of steps of a method for constructing a reliability model of a diesel engine based on vibration data according to the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
As shown in the attached figure 1, the invention provides a method for constructing a reliability model of a diesel engine based on vibration data, which comprises the following implementation steps:
the method comprises the following steps: performing a multi-working-condition and multi-cycle reliability test on the diesel engine, designing a vibration measuring point according to experience, and collecting vibration data;
step two: data integrity is checked according to a reliability test protocol. The data is segmented by taking every 10 seconds as a segment, and the peak value of each segment of data is calculated. And if the peak value of a certain section of data is more than 10 times of the peak value of a section of data before and after the certain section of data, judging that the section of data has non-technical factor abnormality, rejecting the section of data, and preventing error interference on reliability evaluation. And labeling the effective data segments according to the actual test working conditions, namely giving the working conditions corresponding to each segment of data.
Step three: and calculating 20 commonly used index parameters (including an effective value, a variance, a peak-to-peak value, skewness and kurtosis of an original vibration signal, an amplitude spectrum mean value, an average frequency, a spectrum variance, a spectrum peak value and a spectrum kurtosis of the original signal after Fourier transform, an effective value, a variance, a peak-to-peak value, a skewness and a kurtosis of an amplitude envelope signal of the original signal, and an amplitude spectrum mean value, an average frequency, a spectrum variance, a spectrum peak value and a spectrum kurtosis of the amplitude envelope signal after Fourier transform) for all the acquired vibration channel data by taking the data segment in the step two as a minimum unit. The method for designing the channel and index combined screening standard comprises the following steps:
for each vibration test channel signal, assume C conditions, NcIf each data segment has Q indexes to be selected, a characteristic index set can be constructed
{In,c,q,n＝1,2,…,Nc；c＝1,2,…,C；q＝1,2,…,Q}
Wherein, In,c,qIs the nth characteristic value of the qth index of the c working condition; n is a radical ofcIs the sample number of the c-th working condition; q is the number of indexes. The feature selection method based on distance calculation in the clustering analysis idea can be described as follows:
d) in-service distance assessment
Firstly, calculating the average distance in the working condition of the same working condition
Then obtaining the average value of the distances in the C working conditions
Defining and calculating a difference factor of distances within a working condition
e) Inter-operating condition distance assessment
Firstly, calculating the average value of each characteristic of the same working condition
Then obtaining the average distance between different working conditions
Defining and calculating a difference factor of the distance between the operating conditions
f) Integrated distance assessment
Firstly, defining and calculating the weighting factor of each index characteristic
Calculating the ratio of inter-and intra-regime distances with weighting factors
Finally, obtaining a comprehensive distance evaluation factor by utilizing a maximum normalization method
Is seen to be larger
The corresponding characteristic indexes are easier to separate C working conditions, and the robustness and the stability are stronger. Thus, the distance estimation factor is calculated from the respective indices of the respective vibration channel signals
And (4) from large to small, a sensitive channel and a corresponding sensitive characteristic index can be combined and optimized to form an evaluation system of the vibration data.
And 4, step 4: and (3) according to the vibration data labeled by the working conditions, combining the preferable vibration data evaluation indexes in the step (3), learning a support vector machine, constructing a corresponding relation between the vibration data evaluation indexes and the actual operation working conditions, and accurately identifying the actual operation working conditions of all the sections of data again.
And 5: rearranging all data segments in each cycle according to the time sequence for the same operation working condition so as to realize time positive sequence signal arrangement;
step 6: and (3) performing statistical characteristic analysis (including average value, variance, peak-to-peak value and the like) and trend analysis on the vibration data characteristics of the time positive sequence signals of each cycle and each working condition based on an evaluation system of the vibration data.
Preferably, the data segment division length, the number and type of the common index parameters, and the statistical characteristic analysis method in the above process are finely adjusted, so that the reliability model construction object of the present invention can be also achieved.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A diesel engine reliability model construction method based on vibration data is characterized by comprising the following implementation steps:
the method comprises the following steps: preprocessing vibration data acquired in the reliability test;
step two: performing combined optimization on the vibration test channel and the analysis indexes, and selecting a plurality of optimal vibration test points and corresponding optimal evaluation indexes to form a vibration characteristic evaluation system;
step three: carrying out automatic working condition division on the vibration signals acquired in real time;
step four: classifying and rearranging the data of the same operation working condition according to the time sequence based on the vibration signal working condition division result;
step five: and for each operating condition, analyzing the statistical characteristics of the vibration data characteristics in the same operating condition and analyzing the trend based on the vibration characteristic evaluation system established in the step two, so as to judge the vibration stability level of the tested diesel engine under different operating conditions and further analyze the reliability.
2. The method for constructing a reliability model of a diesel engine based on vibration data as claimed in claim 1, wherein the preprocessing of the vibration data in the first step comprises: counting whether the vibration data have data loss and abnormal values; eliminating the abnormality caused by non-technical factors; and labeling the effective vibration data according to the operation condition.
3. The method for constructing a reliability model of a diesel engine based on vibration data as claimed in claim 2, wherein the joint optimization process in the second step comprises: and respectively calculating a plurality of vibration evaluation indexes for a plurality of vibration measuring point data selected by experience, and preferably selecting a plurality of optimal vibration measuring points and corresponding optimal evaluation indexes in a combined manner according to the principle that the index difference is maximum under different working conditions and the index difference is minimum under the same working condition.
4. The method for constructing the reliability model of the diesel engine based on the vibration data as claimed in claim 2, wherein the process of automatic condition division in the third step comprises: establishing a nonlinear mapping relation between a vibration signal index and an actual operation condition; and based on labeled vibration data, developing one-dimensional signal support vector machine learning, and automatically dividing working conditions of the vibration signals acquired in real time according to the trained support vector machine.
5. The method for constructing the reliability model of the diesel engine based on the vibration data as claimed in claim 4, wherein the vibration characteristic evaluation system is obtained by a characteristic selection method based on distance calculation in a clustering analysis idea, and for a signal of each vibration test channel, C working conditions are assumed, and N is assumed to existcIf each data segment has Q indexes to be selected, a characteristic index set can be constructed
{In,c,q,n＝1,2,…,Nc；c＝1,2,…,C；q＝1,2,…,Q}
Wherein, In,c,qIs the nth characteristic value of the qth index of the c working condition; n is a radical ofcIs the sample number of the c-th working condition; q is the number of indexes;
a) in-service distance assessment
Firstly, calculating the average distance in the working condition of the same working condition
Then obtaining the average value of the distances in the C working conditions
Defining and calculating a difference factor of distances within a working condition
b) Inter-operating condition distance assessment
Firstly, calculating the average value of each characteristic of the same working condition
Then obtaining the average distance between different working conditions
Defining and calculating a difference factor of the distance between the operating conditions
c) Integrated distance assessment
Firstly, defining and calculating the weighting factor of each index characteristic
Calculating the ratio of inter-and intra-regime distances with weighting factors
Finally, obtaining a comprehensive distance evaluation factor by utilizing a maximum normalization method
According to the individual fingers of the individual vibration channel signalsDistance evaluation factor obtained by standard calculation
And (4) combining and preferably selecting the sensitive channels and the corresponding sensitive characteristic indexes from large to small to form an evaluation system of the vibration characteristics.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114674363A (en) * | 2022-03-01 | 2022-06-28 | 扬州华辉水泵有限公司 | Slurry pump flow velocity reliability prediction system based on industrial Internet of things |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10197404A (en) * | 1997-01-08 | 1998-07-31 | Power Reactor & Nuclear Fuel Dev Corp | Apparatus for monitoring abnormality of diesel generator |
| CN105319071A (en) * | 2015-09-21 | 2016-02-10 | 天津大学 | Diesel engine fuel oil system fault diagnosis method based on least square support vector machine |
| CN106094570A (en) * | 2016-07-13 | 2016-11-09 | 北京航空航天大学 | A kind of aero-engine complete machine health evaluating method under variable working condition based on operating mode's switch and this distance of paddy |
| CN107941537A (en) * | 2017-10-25 | 2018-04-20 | 南京航空航天大学 | A kind of mechanical equipment health state evaluation method |
| CN108375476A (en) * | 2018-03-09 | 2018-08-07 | 中国水利水电科学研究院 | A kind of Hydropower Unit health evaluating method |
| CN109974782A (en) * | 2019-04-10 | 2019-07-05 | 郑州轻工业学院 | Equipment fault early-warning method and system based on big data sensitive features optimum option |
| CN110044631A (en) * | 2019-03-13 | 2019-07-23 | 中交广州航道局有限公司 | Trend prediction method, device and the computer equipment of ship machine diesel engine |
| US20190383705A1 (en) * | 2016-11-29 | 2019-12-19 | Sts Defence Limited | Engine health diagnostic apparatus and method |
| CN111947929A (en) * | 2020-08-14 | 2020-11-17 | 华东交通大学 | Method for analyzing working condition sensitivity of vibration characteristic evaluation index of rotary machine |
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10197404A (en) * | 1997-01-08 | 1998-07-31 | Power Reactor & Nuclear Fuel Dev Corp | Apparatus for monitoring abnormality of diesel generator |
| CN105319071A (en) * | 2015-09-21 | 2016-02-10 | 天津大学 | Diesel engine fuel oil system fault diagnosis method based on least square support vector machine |
| CN106094570A (en) * | 2016-07-13 | 2016-11-09 | 北京航空航天大学 | A kind of aero-engine complete machine health evaluating method under variable working condition based on operating mode's switch and this distance of paddy |
| US20190383705A1 (en) * | 2016-11-29 | 2019-12-19 | Sts Defence Limited | Engine health diagnostic apparatus and method |
| CN107941537A (en) * | 2017-10-25 | 2018-04-20 | 南京航空航天大学 | A kind of mechanical equipment health state evaluation method |
| CN108375476A (en) * | 2018-03-09 | 2018-08-07 | 中国水利水电科学研究院 | A kind of Hydropower Unit health evaluating method |
| CN110044631A (en) * | 2019-03-13 | 2019-07-23 | 中交广州航道局有限公司 | Trend prediction method, device and the computer equipment of ship machine diesel engine |
| CN109974782A (en) * | 2019-04-10 | 2019-07-05 | 郑州轻工业学院 | Equipment fault early-warning method and system based on big data sensitive features optimum option |
| CN111947929A (en) * | 2020-08-14 | 2020-11-17 | 华东交通大学 | Method for analyzing working condition sensitivity of vibration characteristic evaluation index of rotary machine |
Non-Patent Citations (5)
| Title |
|---|
| MIN WANG ET AL.: "Design of Vibration Monitoring and Fault Diagnosis System for Marine Diesel Engine", 2020 11TH INTERNATIONAL CONFERENCE ON PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT, pages 1 - 4 * |
| 姚良;李艾华;张振仁;: "用于内燃机振动诊断的一种特征参数选取方法", 内燃机工程, no. 04, pages 80 - 83 * |
| 张小明;刘建敏;乔新勇;刘艳斌;: "柴油机健康状况评估及其方法", 装甲兵工程学院学报, no. 02, pages 34 - 38 * |
| 张猛: "船用柴油机健康状态监测评估系统开发与验证", 中国优秀硕士学位论文全文数据库 工程科技II辑, no. 7, pages 036 - 58 * |
| 范海鹏;吴敏;曹卫华;赖旭芝;陈略峰;陆承达;: "基于钻进状态监测的智能工况识别", 探矿工程(岩土钻掘工程), no. 04, pages 110 - 117 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114674363A (en) * | 2022-03-01 | 2022-06-28 | 扬州华辉水泵有限公司 | Slurry pump flow velocity reliability prediction system based on industrial Internet of things |
| CN114674363B (en) * | 2022-03-01 | 2022-11-29 | 扬州华辉水泵有限公司 | Slurry pump flow velocity reliability prediction system based on industrial Internet of things |
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