CN112417758A - Diesel engine fault prediction method based on combined neural network - Google Patents
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Abstract
The invention discloses a diesel engine fault prediction method based on a combined neural network, which comprises the following steps: determining sample data according to a service application scene; preprocessing sample data to determine training data; establishing a data model by using training data by adopting a combined neural network algorithm; and predicting the running state of the diesel engine according to the data model. The invention applies the combined neural network model, adopts a two-stage forecasting mechanism, applies enough samples for training, and can realize the accurate analysis of the non-stationary vibration signal of the diesel engine. The method can predict the sudden change of the vibration signal of the diesel engine, thereby effectively monitoring the running state of the diesel engine and predicting and managing the fault by using the vibration signal on the surface of the diesel engine.
Description
Technical Field
The invention relates to the technical field of equipment management, in particular to a method and a system for predicting a fault of a diesel engine based on a combined neural network.
Background
The emergency diesel generating set of the nuclear power station belongs to 1E-level safety equipment of the nuclear power station and is a key safety guarantee for the whole nuclear power station. When a normal power supply and a backup power supply of the nuclear island fail, the nuclear island is quickly started to supply power to the safety machine equipment, so that the active safety shutdown of the reactor is ensured, particularly, the occurrence of a reactor core melting accident is prevented, and the nuclear island is of great importance to the safety guarantee of the nuclear power station.
By utilizing a big data analysis technical method, the potential rules and modes of fault development prediction can be found out from massive power system data, and support is provided for decision-making personnel. Compared with the traditional logical reasoning research, the big data research is to carry out statistical search, comparison, classification and other processing on the massive data, so certain characteristics of statistical science are continued, such as data relevance or correlation (the correlation means that certain regularity exists between values of two or more variables); the correlation analysis is mainly performed to find out a potential relationship network in the data set, and is usually reflected by parameters such as reliability, support degree and the like.
Disclosure of Invention
The invention mainly aims to provide a method and a system for predicting the fault of a diesel engine based on a combined neural network.
In order to achieve the purpose, the invention provides a diesel engine fault prediction method based on a combined neural network, which comprises the following steps:
determining sample data according to a service application scene;
preprocessing the sample data to determine training data;
establishing a data model by using the training data by adopting a combined neural network algorithm; and
and predicting the running state of the diesel engine according to the data model.
In the method for predicting the fault of the diesel engine based on the combined neural network, the sample data is a vibration signal of the diesel engine.
In the method for predicting the fault of the diesel engine based on the combined neural network, the step of preprocessing the sample data and determining the training data comprises the following steps:
and reconstructing the vibration signal in a phase space, and extracting characteristic information in the vibration signal.
In the method for predicting the fault of the diesel engine based on the combined neural network, the combined neural network algorithm is adopted, and the step of establishing a data model by using the training data comprises the following steps:
training the characteristic information by adopting two parallel neural networks and applying a fuzzy back propagation algorithm;
and combining the training results and then outputting the combined training results, and training the output results by applying a linear programming algorithm to obtain the data model.
According to another aspect of the present invention, there is provided a system for predicting a failure of a diesel engine based on a combined neural network, including:
the sample data determining unit is used for determining sample data according to the service application scene;
the training data acquisition unit is used for preprocessing the sample data and determining training data;
the model establishing unit is used for establishing a data model by using the training data by adopting a combined neural network algorithm; and
and the prediction unit is used for predicting the running state of the diesel engine according to the data model.
In the diesel engine fault prediction system based on the combined neural network, the sample data is a diesel engine vibration signal.
In the diesel engine fault prediction system based on the combined neural network, the training data acquisition unit reconstructs the vibration signal in a phase space and extracts characteristic information in the vibration signal.
In the diesel engine fault prediction system based on the combined neural network, the model establishing unit comprises a first training unit and a second training unit,
the first training unit adopts two parallel neural networks and trains the characteristic information by applying a fuzzy back propagation algorithm;
and the second training unit combines the training results and then outputs the combined training results, and trains the output results by applying a linear programming algorithm to obtain the data model.
The method and the system for predicting the fault of the diesel engine based on the combined neural network have the following beneficial effects that: the invention provides a diesel engine fault prediction method based on a combined neural network, which selects sample data (mainly vibration data) by data preprocessing and service scenes; determining training data through feature recognition, selection and analysis, establishing a data model by adopting a combined neural network algorithm, finally issuing a model flow through calibration verification and iterative optimization of test data, and establishing a standardized data modeling method system. Aiming at a typical service scene of the diesel engine, an intelligent data model is established, fault prediction is realized, and the reliability of the diesel engine is improved; the combined neural network model is applied, a secondary prediction mechanism is adopted, sufficient samples are applied for training, and accurate analysis of the unstable vibration signals of the diesel engine can be achieved. The method can predict the sudden change of the vibration signal of the diesel engine, thereby effectively monitoring the running state of the diesel engine and predicting and managing the fault by using the vibration signal on the surface of the diesel engine.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts:
FIG. 1 is a flow chart of a method for predicting a fault of a diesel engine based on a combined neural network according to the present invention;
fig. 2 is a schematic diagram of a diesel engine fault prediction system based on a combined neural network provided by the invention.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Exemplary embodiments of the invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
In order to better understand the technical solutions, the technical solutions will be described in detail below with reference to the drawings and the specific embodiments of the specification, and it should be understood that the embodiments and specific features of the embodiments of the present invention are detailed descriptions of the technical solutions of the present application, and are not limited to the technical solutions of the present application, and the technical features of the embodiments and examples of the present invention may be combined with each other without conflict.
Fig. 1 is a flowchart of a method for predicting a fault of a diesel engine based on a combined neural network according to the present invention. As shown in fig. 1, the method for predicting a fault of a diesel engine based on a combined neural network provided by the present invention includes the following steps:
step S1, determining sample data according to the service application scene;
specifically, in an embodiment of the present invention, the sample data is a diesel engine vibration signal. The vibration signal of the diesel engine contains rich running state information, and the catastrophe point of the vibration signal contains rich fault information, which reflects the impact, oscillation, rotation speed change, structural deformation, clearance increase, fracture and the like caused by faults. Therefore, the diesel engine vibration signal is fitted and predicted, the diesel engine fault is substantially predicted, and the health condition of the diesel engine can be predicted.
Step S2, preprocessing the sample data and determining training data;
specifically, in an embodiment of the present invention, the vibration signal is reconstructed in a phase space, and feature information in the vibration signal is extracted.
Step S3, establishing a data model by using the training data by adopting a combined neural network algorithm;
specifically, in an embodiment of the present invention, it can be known through detailed analysis of the vibration signal of the diesel engine that the vibration characteristics of the diesel engine are complex. The vibration signal measured at the part of the diesel engine contains technical state information of a plurality of parts of the diesel engine, so that the signal is complex, the typical characteristic frequency is difficult to exist, and in addition, the complexity of the corresponding relation between the vibration signal of the diesel engine and the fault is high, and a plurality of classical mode identification used for the fault prediction analysis of the diesel engine can generate large errors. The neural network has the advantages of good adaptivity, fault tolerance, nonlinearity and the like, and the method for extracting the vibration signal by using the neural network is the preferred method. The neural network has a strong mapping function from input information to output information, can store and memorize the processing process in weight and threshold concentration by processing various standard signals and learning standard samples, and realizes signal processing by the associative capability of the network. In the invention, a two-stage forecasting mechanism is applied to forecast the vibration signal: the first stage adopts two parallel neural networks and trains by applying a fuzzy back propagation algorithm; and in the second stage, the results of the first-stage prediction are combined and then output, and a linear programming algorithm of Kamarkar's is applied for training.
And step S4, predicting the running state of the diesel engine according to the data model.
Specifically, in an embodiment of the present invention, the neural network system has self-organizing, self-learning, and self-adapting features, and in a certain sense, the self-learning of the neural network is to extract the signal features. Therefore, if the signals to be predicted have certain differences, the network can extract the features of the predicted signals through adaptive cluster learning. And then, predicting the fault of the diesel generator according to the relation between the vibration signal characteristics and the fault event obtained on the basis of comprehensively collecting, analyzing, digesting and absorbing the fault event of the nuclear power emergency diesel generator set at home and abroad according to the stock data of the nuclear power emergency diesel generator set.
The invention provides a diesel engine fault prediction method based on a combined neural network, which selects sample data (mainly vibration data) by data preprocessing and service scenes; determining training data through feature recognition, selection and analysis, establishing a data model by adopting a combined neural network algorithm, finally issuing a model flow through calibration verification and iterative optimization of test data, and establishing a standardized data modeling method system. Aiming at the typical service scene of the diesel engine, an intelligent data model is established, fault prediction is realized, and the reliability of the diesel engine is improved.
Fig. 2 is a schematic diagram of a diesel engine fault prediction system based on a combined neural network provided by the invention. As shown in fig. 2, the system for predicting a fault of a diesel engine based on a combined neural network provided by the present invention includes:
a sample data determining unit 210, configured to determine sample data according to a service application scenario;
specifically, in an embodiment of the present invention, the sample data is a diesel engine vibration signal. The vibration signal of the diesel engine contains rich running state information, and the catastrophe point of the vibration signal contains rich fault information, which reflects the impact, oscillation, rotation speed change, structural deformation, clearance increase, fracture and the like caused by faults. Therefore, the diesel engine vibration signal is fitted and predicted, the diesel engine fault is substantially predicted, and the health condition of the diesel engine can be predicted.
A training data obtaining unit 220, configured to pre-process the sample data and determine training data;
specifically, in an embodiment of the present invention, the vibration signal is reconstructed in a phase space, and feature information in the vibration signal is extracted.
A model building unit 230, configured to build a data model using the training data by using a combined neural network algorithm;
specifically, in an embodiment of the present invention, it can be known through detailed analysis of the vibration signal of the diesel engine that the vibration characteristics of the diesel engine are complex. The vibration signal measured at the part of the diesel engine contains technical state information of a plurality of parts of the diesel engine, so that the signal is complex, the typical characteristic frequency is difficult to exist, and in addition, the complexity of the corresponding relation between the vibration signal of the diesel engine and the fault is high, and a plurality of classical mode identification used for the fault prediction analysis of the diesel engine can generate large errors. The neural network has the advantages of good adaptivity, fault tolerance, nonlinearity and the like, and the method for extracting the vibration signal by using the neural network is the preferred method. The neural network has a strong mapping function from input information to output information, can store and memorize the processing process in weight and threshold concentration by processing various standard signals and learning standard samples, and realizes signal processing by the associative capability of the network. In the invention, a two-stage forecasting mechanism is applied to forecast the vibration signal: the model building unit comprises a first training unit and a second training unit, and the first stage adopts two parallel neural networks and trains by applying a fuzzy back propagation algorithm; and in the second stage, the results of the first-stage prediction are combined and then output, and a linear programming algorithm of Kamarkar's is applied for training.
And the prediction unit 240 is used for predicting the running state of the diesel engine according to the data model.
Specifically, in an embodiment of the present invention, the neural network system has self-organizing, self-learning, and self-adapting features, and in a certain sense, the self-learning of the neural network is to extract the signal features. Therefore, if the signals to be predicted have certain difference, the prediction unit can extract the features of the predicted signals through adaptive cluster learning. And then, predicting the fault of the diesel generator according to the relation between the vibration signal characteristics and the fault event obtained on the basis of comprehensively collecting, analyzing, digesting and absorbing the fault event of the nuclear power emergency diesel generator set at home and abroad according to the stock data of the nuclear power emergency diesel generator set.
The invention applies the combined neural network model, adopts a two-stage forecasting mechanism, applies enough samples for training, and can realize the accurate analysis of the non-stationary vibration signal of the diesel engine. The method can predict the sudden change of the vibration signal of the diesel engine, thereby effectively monitoring the running state of the diesel engine and predicting and managing the fault by using the vibration signal on the surface of the diesel engine.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (8)
1. A diesel engine fault prediction method based on a combined neural network is characterized by comprising the following steps:
determining sample data according to a service application scene;
preprocessing the sample data to determine training data;
establishing a data model by using the training data by adopting a combined neural network algorithm; and
and predicting the running state of the diesel engine according to the data model.
2. The combined neural network-based diesel engine fault prediction method of claim 1, wherein the sample data is a diesel engine vibration signal.
3. The method of claim 2, wherein the step of preprocessing the sample data and determining training data comprises:
and reconstructing the vibration signal in a phase space, and extracting characteristic information in the vibration signal.
4. The method of claim 3, wherein the step of using the neural network algorithm to build a data model using the training data comprises:
training the characteristic information by adopting two parallel neural networks and applying a fuzzy back propagation algorithm;
and combining the training results and then outputting the combined training results, and training the output results by applying a linear programming algorithm to obtain the data model.
5. A diesel engine fault prediction system based on a combined neural network is characterized by comprising the following components:
the sample data determining unit is used for determining sample data according to the service application scene;
the training data acquisition unit is used for preprocessing the sample data and determining training data;
the model establishing unit is used for establishing a data model by using the training data by adopting a combined neural network algorithm; and
and the prediction unit is used for predicting the running state of the diesel engine according to the data model.
6. The combined neural network-based diesel engine fault prediction system of claim 5, wherein the sample data is a diesel engine vibration signal.
7. The system of claim 6, wherein the training data acquisition unit reconstructs the vibration signal in phase space to extract the characteristic information in the vibration signal.
8. The combined neural network-based diesel engine fault prediction system of claim 7, wherein the model building unit comprises a first training unit and a second training unit,
the first training unit adopts two parallel neural networks and trains the characteristic information by applying a fuzzy back propagation algorithm;
and the second training unit combines the training results and then outputs the combined training results, and trains the output results by applying a linear programming algorithm to obtain the data model.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| GB2602871A (en) * | 2020-11-19 | 2022-07-20 | Caterpillar Inc | Monitoring system for engine performance and failure prediction |
| CN117574114A (en) * | 2024-01-15 | 2024-02-20 | 安徽农业大学 | Remote reconstruction and jump disturbance detection method for running data of rotary machine |
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| CN110702418A (en) * | 2019-10-10 | 2020-01-17 | 山东超越数控电子股份有限公司 | Aircraft engine fault prediction method |
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| CN110702418A (en) * | 2019-10-10 | 2020-01-17 | 山东超越数控电子股份有限公司 | Aircraft engine fault prediction method |
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| 吴明赞 等: "基于组合神经网络的柴油机振动信号预测", 《机械工程学报》, vol. 38, no. 04, pages 144 - 147 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2602871A (en) * | 2020-11-19 | 2022-07-20 | Caterpillar Inc | Monitoring system for engine performance and failure prediction |
| US11650580B2 (en) | 2020-11-19 | 2023-05-16 | Caterpillar Inc. | Monitoring system for engine performance and failure prediction |
| CN117574114A (en) * | 2024-01-15 | 2024-02-20 | 安徽农业大学 | Remote reconstruction and jump disturbance detection method for running data of rotary machine |
| CN117574114B (en) * | 2024-01-15 | 2024-04-19 | 安徽农业大学 | Remote reconstruction and jump disturbance detection method for running data of rotary machine |
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