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Engine fuel-injection quantity abnormal fault diagnosis method based on information fusion

A fault diagnosis and engine technology, which is applied in the direction of engine testing, measuring devices, testing of machine/structural components, etc., can solve problems such as abnormal fuel injection volume and unclear cause of failure

Inactive Publication Date: 2013-03-27
TIANJIN UNIV OF TECH & EDUCATION TEACHER DEV CENT OF CHINA VOCATIONAL TRAINING & GUIDANCE
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AI Technical Summary

Problems solved by technology

The fault of abnormal fuel injection volume of the engine is a common fault of the engine. There are many factors that affect the fuel injection volume of the injector. Therefore, even if the waveform grounding time of the fuel injection driver is incorrect, that is, the fuel injection volume is abnormal, the cause of the fault cannot be clarified.

Method used

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Embodiment Construction

[0005] Specific embodiments of the present invention will be further described in detail below.

[0006] The method is composed of a data acquisition system, a fuzzy processing system, and a neural network system. The waveform eigenvalues ​​of the fuzzy air flow sensor signal and the waveform eigenvalues ​​of the battery voltage signal are used as the input values ​​of the neural network system. The structural BP neural network completes the fault diagnosis of abnormal fuel injection quantity of the engine. The data acquisition system is responsible for collecting the waveform eigenvalues ​​of the air flow sensor signal and the waveform eigenvalues ​​of the battery voltage signal. The signal features extracted by the air flow sensor are 6, which are the voltage when the throttle is closed, the voltage when the throttle is fully open, whether the waveform changes continuously during the throttle closing-opening process, the lowest voltage of the waveform, the highest voltage of...

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Abstract

An engine fuel-injection quantity abnormal fault diagnosis method based on information fusion mainly requires a data collecting system, a fuzzy processing system and a neural network system. The data collecting system collects and fuses air flow sensor signal waveform characteristics of an engine and storage battery voltage waveform characteristics, and the characteristics are introduced into the fuzzy processing system. The fuzzy processing system fuzzifies the characteristics and enables characteristic values to be distributed in a range of [0,1], and the data is applied to the neural network system. The neural network system is a back propagation (BP) neural network of a multi-output structure, an input layer is a fuzzified waveform characteristic value, and an output layer is diagnosed faults. The engine fuel-injection quantity abnormal fault diagnosis method has the advantages of fusing information of automobile multiple components, improving abnormal fault diagnosis accuracy of the engine fuel injection quantity through correlation among components, and providing technical assistance for automobile maintaining enterprises. The engine fuel-injection quantity abnormal fault diagnosis method can be widely applied to diagnosis of various automobile faults.

Description

Technical field: [0001] The invention is an information fusion-based fault diagnosis method for engine fuel injection abnormalities, which can diagnose engine faults by fusing multiple information of automobiles and applying neural network technology. The technical fields include automotive electronic control technology, neural network technology, fuzzy mathematics, etc. Background technique: [0002] Every sensor in the car has its own standard working waveform. When the tested waveform is different from the standard waveform, it means that the component or its related parts have failed. If the fusion technology is applied to fuse the electronic signals related to a certain fault phenomenon and take into account various signal information, the accuracy of fault diagnosis can be improved. At this stage, the research on engine fault diagnosis based on information fusion technology is relatively extensive, but because the signals fused in each study are different, and the fea...

Claims

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Application Information

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IPC IPC(8): G01M15/00
Inventor 董恩国张蕾关志伟周海松包丕利
Owner TIANJIN UNIV OF TECH & EDUCATION TEACHER DEV CENT OF CHINA VOCATIONAL TRAINING & GUIDANCE
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