CN116558833A - Dynamic prediction monitoring system for automobile engine - Google Patents

Dynamic prediction monitoring system for automobile engine Download PDF

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Publication number
CN116558833A
CN116558833A CN202310511563.6A CN202310511563A CN116558833A CN 116558833 A CN116558833 A CN 116558833A CN 202310511563 A CN202310511563 A CN 202310511563A CN 116558833 A CN116558833 A CN 116558833A
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data
module
engine
unit
real
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杨帅虎
杨磊
刘洋
张义禄
成凯
覃伟杰
朱洪南
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Guangxi Academy of Sciences
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Guangxi Academy of Sciences
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M15/00—Testing of engines
    • G01M15/04—Testing internal-combustion engines
    • G01M15/05—Testing internal-combustion engines by combined monitoring of two or more different engine parameters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F18/00—Pattern recognition
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    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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    • G—PHYSICS
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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Abstract

The invention discloses a dynamic prediction monitoring system of an automobile engine, which comprises: the system comprises a data acquisition module, a data preprocessing module, a prediction module and a monitoring module; the data acquisition module is used for acquiring historical data, operation standard data and operation actual measurement data; the data preprocessing module is used for processing the acquired data, dividing the acquired data into different data sets and constructing a corresponding database; the prediction module is used for predicting whether faults are possible to happen according to the convolutional neural network and the acquired data. The monitoring module judges whether the engine operates normally or not; the display module is used for displaying the operation data of each part of the engine and marking the data judged to be abnormal. According to the invention, the acquired operation standard data and the operation measured data are divided according to the working condition of the engine, and a corresponding comparison database is constructed, so that whether faults occur or not and the corresponding fault types can be rapidly judged only by comparing the data under the corresponding working condition.

Description

Dynamic prediction monitoring system for automobile engine
Technical Field
The invention belongs to the field of engine state monitoring, and particularly relates to a dynamic prediction monitoring system of an automobile engine.
Background
People often can not learn the real working condition of the driven automobile engine at the first time in the process of driving the automobile, so that when the automobile engine is in the initial stage of failure, a driver does not timely deal with the failure and well eliminates the failure, further the damage of the failure is enlarged, and the automobile parts are easily damaged or traffic accidents are easily caused.
The automobile engine is a very complex mechanical, electric and hydraulic system, and consists of two large mechanisms (crank-link mechanism and valve mechanism) and five large systems (fuel supply system, cooling system, lubricating system, ignition system and starting system), and the faults are various, and the relationship between the fault phenomenon and the reasons is complex and may be one-to-many, many-to-one or many-to-many. Different faults are manifested in different ways. It is necessary to quickly and accurately judge whether the operation is normal or not and ensure the driving to remove risks in time, so that a dynamic prediction and monitoring system for an automobile engine is provided.
Disclosure of Invention
The invention aims to provide a dynamic prediction monitoring system for an automobile engine, which aims to solve the problems in the prior art.
In order to achieve the above object, the present invention provides a dynamic prediction monitoring system for an automobile engine, comprising:
the system comprises a data acquisition module, a data preprocessing module, a prediction module, a monitoring module and a display module;
the data acquisition module is used for acquiring historical data, operation standard data and operation actual measurement data;
the data preprocessing module is used for processing the acquired data, dividing the acquired data into different data sets and constructing a corresponding database;
the prediction module is used for predicting whether faults are likely to occur according to the convolutional neural network and the acquired data; if yes, judging the fault type through the monitoring module.
The monitoring module judges whether the engine operates normally or not according to the operation standard data and the operation measured data and the engine data to be detected obtained in real time;
the display module is used for displaying the operation data of each part of the engine and marking the data judged to be abnormal;
the data acquisition module is connected with the data preprocessing module, the data preprocessing module is respectively connected with the prediction module and the monitoring module, the display module is connected with the monitoring module, and the prediction module is connected with the monitoring module.
Optionally, the data acquisition module comprises a first acquisition unit and a second acquisition unit;
the first acquisition unit is used for acquiring engine operation standard data and operation actual measurement data through a plurality of sensors, wherein the operation actual measurement data are measurement data of the engine in different fault states, and the plurality of sensors comprise a vibration sensor, a temperature sensor, a pressure sensor and a battery data acquisition unit;
the second acquisition unit is used for acquiring engine historical data.
Optionally, the data preprocessing module comprises a data receiving unit, a first processing unit and a second processing unit;
the data receiving unit is connected with the data acquisition module and used for acquiring operation standard data, operation actual measurement data and historical data;
the first processing unit is used for calculating fluctuation variance of data obtained by each sensor after coding reconstruction is carried out on the operation actual measurement data, and eliminating the data with the fluctuation variance exceeding a preset range to obtain effective actual measurement data;
the second processing unit is used for dividing the standard data and the effective measured data into data sets under different working conditions according to the type of the engine operating condition, and respectively forming a comparison database aiming at the standard data and the effective measured data; the historical data is divided into data sets of different phases according to a time sequence.
Optionally, the monitoring module comprises a data acquisition unit, a data processing unit and a state judging unit;
the data acquisition unit is used for acquiring real-time measurement data of a plurality of sensors of the engine and acquiring data in the comparison database;
the data processing unit is used for extracting characteristics of the real-time measurement data and the data in the comparison database respectively;
the state judging unit is used for judging the working state of the transmitter according to the feature extraction result, continuously monitoring if the working state is normal, and sending out a signal to prompt the fault position if the working state is abnormal.
Optionally, the data processing unit performs feature on the data of the comparison database, and is divided into a standard comparison database, an actual measurement comparison database, and feature extraction is performed to obtain standard features and fault features.
Optionally, the state judging unit includes a first judging unit and a second judging unit;
the first judging unit is used for matching the standard characteristic with the real-time measurement data characteristic, judging that the engine works abnormally if the deviation value exceeds a preset range, and storing the real-time measurement data if the deviation value does not exceed the preset range;
the second judging unit is used for comparing the real-time measurement data characteristics with the fault characteristics, judging the fault if the real-time measurement data characteristics are matched with a certain fault characteristic, and storing the real-time measurement data into a corresponding comparison database according to the operation working condition.
Optionally, the prediction module comprises a construction unit, a training unit and a judging unit;
the construction unit is used for constructing a convolutional neural network model;
the training unit adopts the operation data in the history data to divide the history data into a training set and a testing set; inputting a training set into the convolutional neural network model for training until the training is finished after the loss function converges, and obtaining an optimal model;
the judging unit inputs the test set into the optimal model to obtain the prediction probability, then inputs real-time measurement data into the optimal model to predict, judges whether faults possibly occur, if yes, feeds back the faults to the monitoring module, judges the fault type through the monitoring module, and adjusts the data acquisition frequency.
The invention has the technical effects that:
according to the invention, the acquired operation standard data and the operation measured data are divided according to the working condition of the engine, a corresponding comparison database is constructed, whether faults occur or not and the corresponding fault types can be rapidly judged only by comparing the operation standard data with the data under the corresponding working condition, the possibility of faults is predicted by training the convolutional neural network through the historical data, the state judgment is carried out on the data of the possible fault area through the monitoring module, the possibility of fault missing detection is reduced, and the loss caused by faults in the data acquisition interval is avoided.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
fig. 1 is a schematic diagram of a system structure according to an embodiment of the present invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
As shown in fig. 1, the present embodiment provides a dynamic prediction monitoring system for an automobile engine, including:
the system comprises a data acquisition module, a data preprocessing module, a prediction module, a monitoring module and a display module;
the data acquisition module is used for acquiring historical data, operation standard data and operation actual measurement data;
the data preprocessing module is used for processing the acquired data, dividing the acquired data into different data sets and constructing a corresponding database;
the prediction module is used for predicting whether faults are likely to occur according to the convolutional neural network and the acquired data; if yes, judging the fault type through the monitoring module.
The monitoring module is used for judging whether the engine operates normally or not based on the operation standard data and the operation actual measurement data by combining the engine data to be detected obtained in real time;
the display module is used for displaying the operation data of each part of the engine and marking the data judged to be abnormal;
the data acquisition module is connected with the data preprocessing module, the data preprocessing module is respectively connected with the prediction module and the monitoring module, the display module is connected with the monitoring module, and the prediction module is connected with the monitoring module.
In some embodiments, the data acquisition module includes a first acquisition unit, a second acquisition unit;
the first acquisition unit is used for acquiring engine operation standard data and operation actual measurement data through a plurality of sensors, wherein the operation actual measurement data are measurement data of the engine in different fault states, and the plurality of sensors comprise a vibration sensor, a temperature sensor, a pressure sensor and a battery data acquisition unit;
the second acquisition unit is used for acquiring engine history data.
In some embodiments, the data preprocessing module includes a data receiving unit, a first processing unit, a second processing unit;
the data receiving unit is connected with the data acquisition module and used for acquiring operation standard data, operation actual measurement data and historical data;
the first processing unit is used for calculating fluctuation variance of data obtained by each sensor after coding reconstruction is carried out on the operation actual measurement data, and eliminating the data with the fluctuation variance exceeding a preset range to obtain effective actual measurement data;
the second processing unit is used for dividing the standard data and the effective measured data into data sets under different working conditions according to the type of the engine operation working conditions, and respectively forming a comparison database aiming at the standard data and the effective measured data; the historical data is divided into data sets of different phases according to a time sequence.
In some embodiments, the monitoring module includes a data acquisition unit, a data processing unit, a status determination unit;
the data acquisition unit is used for acquiring real-time measurement data of a plurality of sensors of the engine and acquiring data in the comparison database;
the data processing unit is used for extracting characteristics of the real-time measurement data and the data in the comparison database respectively;
the state judging unit is used for judging the working state of the transmitter according to the feature extraction result, continuously monitoring if the working state is normal, and sending out a signal to prompt the fault position if the working state is abnormal.
In some embodiments, the data processing unit performs features on the data of the comparison database, and the features are divided into a standard comparison database, an actual measurement comparison database, and the feature extraction obtains standard features and fault features.
In some embodiments, the state determination unit includes a first determination unit, a second determination unit;
the first judging unit is used for matching the standard characteristic with the real-time measurement data characteristic, judging that the engine works abnormally if the deviation value exceeds a preset range, and storing the real-time measurement data if the deviation value does not exceed the preset range;
the second judging unit is used for comparing the real-time measurement data characteristics with the fault characteristics, judging the fault if the real-time measurement data characteristics are matched with a certain fault characteristic, and storing the real-time measurement data into a corresponding comparison database according to the operation working condition.
In some embodiments, the prediction module includes a construction unit, a training unit, a determination unit;
the construction unit is used for constructing a convolutional neural network model;
the training unit adopts operation data in the historical data to divide the historical data into a training set and a testing set; inputting the training set into a convolutional neural network model for training until the loss function converges and the training is finished, and obtaining an optimal model;
the judging unit inputs the test set into the optimal model to obtain the prediction probability, then inputs the real-time measurement data into the optimal model to predict, judges whether the fault is possible to happen, if yes, feeds back the fault to the monitoring module, judges the fault type through the monitoring module, and adjusts the data acquisition frequency.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (7)

1. A dynamic predictive monitoring system for an automotive engine, comprising:
the system comprises a data acquisition module, a data preprocessing module, a prediction module, a monitoring module and a display module;
the data acquisition module is used for acquiring historical data, operation standard data and operation actual measurement data;
the data preprocessing module is used for processing the acquired data, dividing the acquired data into different data sets and constructing a corresponding database;
the prediction module is used for predicting whether faults are likely to occur according to the convolutional neural network and the acquired data; if yes, judging the fault type through a monitoring module;
the monitoring module judges whether the engine operates normally or not according to the operation standard data and the operation measured data and the engine data to be detected obtained in real time;
the display module is used for displaying the operation data of each part of the engine and marking the data judged to be abnormal;
the data acquisition module is connected with the data preprocessing module, the data preprocessing module is respectively connected with the prediction module and the monitoring module, the display module is connected with the monitoring module, and the prediction module is connected with the monitoring module.
2. The dynamic predictive monitoring system of an automobile engine of claim 1, wherein,
the data acquisition module comprises a first acquisition unit and a second acquisition unit;
the first acquisition unit is used for acquiring engine operation standard data and operation actual measurement data through a plurality of sensors, wherein the operation actual measurement data are measurement data of the engine in different fault states, and the plurality of sensors comprise a vibration sensor, a temperature sensor, a pressure sensor and a battery data acquisition unit;
the second acquisition unit is used for acquiring engine historical data.
3. The dynamic predictive monitoring system of an automobile engine of claim 1, wherein,
the data preprocessing module comprises a data receiving unit, a first processing unit and a second processing unit;
the data receiving unit is connected with the data acquisition module and used for acquiring operation standard data, operation actual measurement data and historical data;
the first processing unit is used for calculating fluctuation variance of data obtained by each sensor after coding reconstruction is carried out on the operation actual measurement data, and eliminating the data with the fluctuation variance exceeding a preset range to obtain effective actual measurement data;
the second processing unit is used for dividing the standard data and the effective measured data into data sets under different working conditions according to the type of the engine operating condition, and respectively forming a comparison database aiming at the standard data and the effective measured data; the historical data is divided into data sets of different phases according to a time sequence.
4. The dynamic predictive monitoring system of an automobile engine of claim 1, wherein,
the monitoring module comprises a data acquisition unit, a data processing unit and a state judging unit;
the data acquisition unit is used for acquiring real-time measurement data of a plurality of sensors of the engine and acquiring data in the comparison database;
the data processing unit is used for extracting characteristics of the real-time measurement data and the data in the comparison database respectively;
the state judging unit is used for judging the working state of the transmitter according to the feature extraction result, continuously monitoring if the working state is normal, and sending out a signal to prompt the fault position if the working state is abnormal.
5. The dynamic predictive monitoring system of an automobile engine of claim 4, wherein,
and the data processing unit is used for carrying out characteristic on the data of the comparison database, and the data processing unit is divided into a standard comparison database, an actual measurement comparison database and characteristic extraction to obtain standard characteristics and fault characteristics.
6. The dynamic predictive monitoring system of an automobile engine of claim 5, wherein,
the state judging unit comprises a first judging unit and a second judging unit;
the first judging unit is used for matching the standard characteristic with the real-time measurement data characteristic, judging that the engine works abnormally if the deviation value exceeds a preset range, and storing the real-time measurement data if the deviation value does not exceed the preset range;
the second judging unit is used for comparing the real-time measurement data characteristics with the fault characteristics, judging the fault if the real-time measurement data characteristics are matched with a certain fault characteristic, and storing the real-time measurement data into a corresponding comparison database according to the operation working condition.
7. The dynamic predictive monitoring system of an automobile engine of claim 3, wherein,
the prediction module comprises a construction unit, a training unit and a judging unit;
the construction unit is used for constructing a convolutional neural network model;
the training unit adopts the operation data in the history data to divide the history data into a training set and a testing set; inputting a training set into the convolutional neural network model for training until the training is finished after the loss function converges, and obtaining an optimal model;
the judging unit inputs the test set into the optimal model to obtain the prediction probability, then inputs real-time measurement data into the optimal model to predict, judges whether faults possibly occur, if yes, feeds back the faults to the monitoring module, judges the fault type through the monitoring module, and adjusts the data acquisition frequency.
CN202310511563.6A 2023-05-08 2023-05-08 Dynamic prediction monitoring system for automobile engine Pending CN116558833A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310511563.6A CN116558833A (en) 2023-05-08 2023-05-08 Dynamic prediction monitoring system for automobile engine

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310511563.6A CN116558833A (en) 2023-05-08 2023-05-08 Dynamic prediction monitoring system for automobile engine

Publications (1)

Publication Number Publication Date
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