CN104680233A - Wavelet neural network-based engine failure diagnosing method - Google Patents
Wavelet neural network-based engine failure diagnosing method Download PDFInfo
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- CN104680233A CN104680233A CN201410586026.9A CN201410586026A CN104680233A CN 104680233 A CN104680233 A CN 104680233A CN 201410586026 A CN201410586026 A CN 201410586026A CN 104680233 A CN104680233 A CN 104680233A
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Abstract
The invention relates to a wavelet neural network-based engine failure diagnosing method and belongs to the field of engine failure diagnosis. The method comprises the following steps of step 1, acquiring original sample data of vehicle exhaust; step 2, establishing a wavelet neural network diagnosing model, inputting the acquired data sample, and performing sample training; step 3, after sample training is finished, inputting the acquired real-time data, performing failure diagnosing analysis, and outputting failure types. According to the engine failure diagnosing method provided by the invention, compared with the traditional neural network, the adopted wavelet neural network has the advantages that the engine failure diagnosing accuracy of the wavelet neural network is obviously improved, and the convergence rate is high.
Description
Technical field
The invention belongs to engine diagnosis field, be specifically related to a kind of Fault Diagnosis of Engine.
Background technology
Engine is the heart of motor racing, determines the quality of the performance of automobile.Modern Engine has become set electron technology, computer technology, infotech in the intelligent control system of one, and integration degree is more and more higher, structure also becomes increasingly complex; But the intelligent fault diagnosis but making to start of engine and maintenance become the bottleneck of restriction automobile industry development.
Automobile engine system is optimized control by the ratio, discharging waste gas etc. of electronic control means to engine ignition, oil spout, air and fuel oil, makes engine operation in optimum condition.Automobile engine system mainly comprises electric control fuel oil jet system, electronic control ignition system, warning prompt system etc.
Summary of the invention
In order to overcome the deficiencies in the prior art, the invention provides a kind of engine failure detection method based on wavelet neural network, carrying out anticipation by the detection data analyzing all kinds of gas in exhaust emissions, thus hidden danger of fixing a breakdown in advance.
Technical scheme of the present invention is: a kind of Fault Diagnosis of Engine, comprises the steps: step one: the initial sample data gathering vehicle exhaust; Step 2: set up wavelet neural network diagnostic model, the data sample that input gathers, carries out sample training; Step 3: after sample training completes, inputs the real time data collected, carries out Analysis on Fault Diagnosis, exports fault type.In described step one, the initial sample data of vehicle exhaust comprises CO
2, HC, CO
1and O
2content percentage.Described wavelet neural network fault diagnosis model comprises input layer, hidden layer and output layer, and the neuron excitation function that hidden layer is chosen is Morlet small echo:
。The target error function of described input layer is:
In formula: QUOTE
for the desired output of output layer n-th node; QUOTE
for the actual output of network, P is input and output number of samples.The output of described hidden layer is:
in formula:
for input layer input;
hidden layer exports; M is input layer node; K is hidden layer node; w
kmfor the weights between hidden layer node and input layer; H() be Morlet wavelet function.The output of described output layer is:
in formula:
for output layer input; K is hidden layer node; N is output layer node; W
nkfor the weights between hidden layer node and output layer node; Sig() be Sigmod function.
The present invention has following good effect: Fault Diagnosis of Engine of the present invention, and adopt wavelet neural network, compared with traditional neural network, the engine diagnosis accuracy rate of wavelet neural network has obvious lifting, fast convergence rate.
Accompanying drawing explanation
Fig. 1 is specific embodiment of the invention wavelet neural network structural drawing.
Embodiment
Contrast accompanying drawing below, by the description to embodiment, the specific embodiment of the present invention is as the effect of the mutual alignment between the shape of involved each component, structure, each several part and annexation, each several part and principle of work, manufacturing process and operation using method etc., be described in further detail, have more complete, accurate and deep understanding to help those skilled in the art to inventive concept of the present invention, technical scheme.
Known according to automobile failure diagnosis, containing the information in engine cylinder combustion process in vehicle exhaust, HC, CO in engine fire fault and vehicle emission component
2, CO and O
2volume fraction Deng gas has corresponding relation, therefore judges the duty residing for engine by volume concentration of gas phase each in vehicle exhaust, and completes Misfire Fault Diagnosis according to the corresponding relation of gas and fault.Theoretical according to this, machine learning method can be utilized to carry out learning training to priori data sample, the machinery diagnosis model trained is used for the analyzing and diagnosing of engine failure.
The present invention adopts wavelet neural network algorithm to the process of input data analysis.Make full use of wavelet transformation and there is the advantage that good Time-Frequency Localization character and neural network have self-learning function, for fault diagnosis.Algorithm of the present invention, before search wavelet neural network hidden layer link weights, first uses genetic algorithm to calculate, is optimized wavelet neural network structure.
The wavelet-neural network model that the present invention adopts comprises input layer, hidden layer and output layer, and output layer adopts linear convergent rate, and input layer has M(m=1, and 2 ..., N) and individual neuron, hidden layer has K(k=1, and 2 ... K) individual neuron, as shown in Figure 1.
The neuron excitation function that hidden layer is chosen is Morlet small echo
(1)
Vibrating in order to avoid causing in weights and threshold correction when sample training one by one, adopting groups training method.To the output also not weighted sum simply of network, but first to the output weighted sum of network hidden layer small echo node, then after Sigmoid functional transformation, obtain final network and export, be conducive to treatment classification problem like this, reduce the possibility of dispersing in training process simultaneously.
Given P(p=1,2 ..., P) and organize input and output sample, learning rate is η (η >0), and factor of momentum is λ (0< λ <1), and target error function is
（2）
In formula: QUOTE
for the desired output of output layer n-th node; QUOTE
for the actual output of network.
The target of algorithm is constantly adjustment network parameters, makes error function reach minimum value.
Hidden layer exports
（3）
In formula:
for input layer input;
hidden layer exports; M is input layer node; K is hidden layer node; w
kmfor the weights between hidden layer node and input layer; H() be Morlet wavelet function.
Output layer exports
(4)
In formula:
for output layer input; K is hidden layer node; N is output layer node; W
nkfor the weights between hidden layer node and output layer node; Sig() be Sigmod function.
By each weight w of neural network
km, w
nkweave into the solution of a character string as problem in order, adopt real coding as follows
w
01w
02……w
1mw
o1……w
kmw
nk
Evaluation function is
f=1/（1+E）
In formula: the expression formula of E is shown in formula (2).
Concrete operations are as follows:
(1) initialization colony: in order to produce as much as possible may solution, the individuality in colony can be divided into groups;
(2) calculate the fitness of each individuality and sort, genetic operator being acted on circulation of future generation and perform, until satisfy condition.
Under abnomal condition, the content of each gas in tail gas is:
1) reading of HC is high, illustrates that fuel oil does not have Thorough combustion.
2) too high levels of CO, shows that fuel delivery is too much, air supply is very few; The content of CO is too low, then show that mixed gas is excessively rare.
3) CO
2be the product of combustion mixture burning, its height reflects the quality of mixture combustion, i.e. burning efficiency.
4) O
2content be the best index of reflection gasoline air mixture ratio, be one of the most useful diagnostic data.By tails assay, the fault of following main aspect can be detected: combination gas overrich or excessively rare, secondary air injection system is malfunctioning, the leakage of injector failures, air-distributor vacuum, the damage of pneumatic pump fault, cylinder head gasket, EGR valve fault, exhaust system are leaked, firing system angle of advance is excessive etc.
Theoretical model after wavelet neural network increases momentum term inherits the advantage of BP neural network and wavelet neural network, has outstanding approximation of function and pattern recurrence performance simultaneously, avoids local minimum, have better practicality.In order to avoid when the complicated network structure, wavelet neural network is difficult to the problem finding optimum solution, and algorithm is before search wavelet neural network hidden layer link weights herein, first uses genetic algorithm to calculate, is optimized wavelet neural network structure.
The present invention learns the tail gas content data gathered, and com-parison and analysis is carried out to data, compared with traditional neural network, the Fault Diagnosis of Gearbox Detection accuracy of wavelet neural network has obvious lifting, convergence of algorithm of the present invention is fastest, the speed of convergence of traditional neural network is the slowest, and the speed of convergence of genetic algorithm and wavelet neural network is suitable.Compared with algorithm of the present invention, genetic algorithm needs a large amount of training sample, and the accuracy rate of diagnosis is lower when training sample is less, and genetic algorithm has randomness, there is larger difference in the result of evolving for each time, the therefore poor reliability of result, can not stably be separated.In addition, engine failure be random, the signal collected is non-linear stochastic signal mostly, and genetic algorithm process non-linear constrain problem time need add penalty factor, this will make computing velocity significantly slow down; When processing engine failure problem, if the feature quantity extracted is more, the dimension of proper vector is just larger, and this will make genetic algorithm be difficult to process and optimize.
Above by reference to the accompanying drawings to invention has been exemplary description; obvious specific implementation of the present invention is not subject to the restrictions described above; as long as have employed the improvement of the various unsubstantialities that method of the present invention is conceived and technical scheme is carried out; or design of the present invention and technical scheme directly applied to other occasion, all within protection scope of the present invention without to improve.
Claims (6)
1. based on a Fault Diagnosis of Engine for wavelet neural network, it is characterized in that: comprise the steps:
Step one: the initial sample data gathering vehicle exhaust;
Step 2: set up wavelet neural network diagnostic model, the data sample that input gathers, carries out sample training;
Step 3: after sample training completes, inputs the real time data collected, carries out Analysis on Fault Diagnosis, exports fault type.
2. the Fault Diagnosis of Engine based on wavelet neural network according to claim 1, is characterized in that: in described step one, the initial sample data of vehicle exhaust comprises CO
2, HC, CO
1and O
2content percentage.
3. the Fault Diagnosis of Engine based on wavelet neural network according to claim 1, it is characterized in that: described wavelet neural network fault diagnosis model comprises input layer, hidden layer and output layer, the neuron excitation function that hidden layer is chosen is Morlet small echo:
。
4. the Fault Diagnosis of Engine based on wavelet neural network according to claim 1, is characterized in that: the target error function of described input layer is:
In formula: QUOTE
for the desired output of output layer n-th node; QUOTE
for the actual output of network, P is input and output number of samples.
5. the Fault Diagnosis of Engine based on wavelet neural network according to claim 1, is characterized in that: the output of described hidden layer is:
In formula:
for input layer input;
hidden layer exports; M is input layer node; K is hidden layer node; w
kmfor the weights between hidden layer node and input layer; H() be Morlet wavelet function.
6. the Fault Diagnosis of Engine based on wavelet neural network according to claim 1, is characterized in that: the output of described output layer is:
In formula:
for output layer input; K is hidden layer node; N is output layer node; W
nkfor the weights between hidden layer node and output layer node; Sig() be Sigmod function.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106408687A (en) * | 2016-11-24 | 2017-02-15 | 沈阳航空航天大学 | Automobile engine fault early warning method based on machine learning method |
| CN106840406A (en) * | 2016-11-29 | 2017-06-13 | 浙江中新电力发展集团有限公司 | Isolation switch method for diagnosing faults based on matrix neutral net |
| CN107024331A (en) * | 2017-03-31 | 2017-08-08 | 中车工业研究院有限公司 | A kind of neutral net is to train motor oscillating online test method |
| CN107122802A (en) * | 2017-05-02 | 2017-09-01 | 哈尔滨理工大学 | A kind of fault detection method based on the rolling bearing for improving wavelet neural network |
| CN107818665A (en) * | 2017-07-06 | 2018-03-20 | 浙江海洋大学 | A kind of construction method of safety pre-warning system |
| CN108416429A (en) * | 2018-02-24 | 2018-08-17 | 华北水利水电大学 | Hydrogen-fuel engine fault diagnosis system and its method based on SOM neural networks |
| CN109855878A (en) * | 2018-12-29 | 2019-06-07 | 青岛海洋科学与技术国家实验室发展中心 | Computer-readable medium, engine failure detection device and ship |
| CN110007235A (en) * | 2019-03-24 | 2019-07-12 | 天津大学青岛海洋技术研究院 | A kind of accumulator of electric car SOC on-line prediction method |
| CN110749447A (en) * | 2019-11-27 | 2020-02-04 | 淮安信息职业技术学院 | Loader engine fault diagnosis method using big data |
| CN112348656A (en) * | 2020-09-29 | 2021-02-09 | 百维金科(上海)信息科技有限公司 | BA-WNN-based personal loan credit scoring method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020082132A1 (en) * | 2000-12-27 | 2002-06-27 | Byeong-Wook Jeon | Malfunction diagnostic method and system for output shaft rpm sensor of a transmission |
| CN103135206A (en) * | 2012-09-07 | 2013-06-05 | 玉晶光电(厦门)有限公司 | Portable electronic device and optical imaging lens thereof |
-
2014
- 2014-10-28 CN CN201410586026.9A patent/CN104680233A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020082132A1 (en) * | 2000-12-27 | 2002-06-27 | Byeong-Wook Jeon | Malfunction diagnostic method and system for output shaft rpm sensor of a transmission |
| CN103135206A (en) * | 2012-09-07 | 2013-06-05 | 玉晶光电(厦门)有限公司 | Portable electronic device and optical imaging lens thereof |
Non-Patent Citations (1)
| Title |
|---|
| 郑令: "基于小波神经网络的机械故障诊断方法的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106408687A (en) * | 2016-11-24 | 2017-02-15 | 沈阳航空航天大学 | Automobile engine fault early warning method based on machine learning method |
| CN106840406A (en) * | 2016-11-29 | 2017-06-13 | 浙江中新电力发展集团有限公司 | Isolation switch method for diagnosing faults based on matrix neutral net |
| CN106840406B (en) * | 2016-11-29 | 2019-08-16 | 浙江中新电力工程建设有限公司自动化分公司 | Isolation switch method for diagnosing faults based on matrix neural network |
| CN107024331B (en) * | 2017-03-31 | 2019-07-12 | 中车工业研究院有限公司 | A kind of neural network is to train motor oscillating online test method |
| CN107024331A (en) * | 2017-03-31 | 2017-08-08 | 中车工业研究院有限公司 | A kind of neutral net is to train motor oscillating online test method |
| CN107122802A (en) * | 2017-05-02 | 2017-09-01 | 哈尔滨理工大学 | A kind of fault detection method based on the rolling bearing for improving wavelet neural network |
| CN107818665A (en) * | 2017-07-06 | 2018-03-20 | 浙江海洋大学 | A kind of construction method of safety pre-warning system |
| CN108416429A (en) * | 2018-02-24 | 2018-08-17 | 华北水利水电大学 | Hydrogen-fuel engine fault diagnosis system and its method based on SOM neural networks |
| CN109855878A (en) * | 2018-12-29 | 2019-06-07 | 青岛海洋科学与技术国家实验室发展中心 | Computer-readable medium, engine failure detection device and ship |
| CN110007235A (en) * | 2019-03-24 | 2019-07-12 | 天津大学青岛海洋技术研究院 | A kind of accumulator of electric car SOC on-line prediction method |
| CN110749447A (en) * | 2019-11-27 | 2020-02-04 | 淮安信息职业技术学院 | Loader engine fault diagnosis method using big data |
| CN110749447B (en) * | 2019-11-27 | 2021-04-16 | 淮安信息职业技术学院 | Loader engine fault diagnosis method using big data |
| CN112348656A (en) * | 2020-09-29 | 2021-02-09 | 百维金科(上海)信息科技有限公司 | BA-WNN-based personal loan credit scoring method |
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