CN114858467A - Anti-noise and cross-noise-domain misfire diagnosis method and system for diesel engine - Google Patents
Anti-noise and cross-noise-domain misfire diagnosis method and system for diesel engine Download PDFInfo
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Abstract
The invention provides a method and a system for diagnosing noise-resistant and cross-noise-domain misfire of a diesel engine, which comprise the following steps: collecting vibration signals of a cylinder cover of the diesel engine on line; designing a residual convolution preprocessing module to perform noise reduction preprocessing on an original vibration signal measured by a diesel engine cylinder based on a noise superposition principle, and constructing a loss function by using residual loss for model training; designing a multi-scale convolution module to extract fault features of different time scales from the preprocessed signals; extracting signal dependent features from the signal processed in step S3 by using LSTM; a diesel engine misfire diagnosis model is constructed and trained by utilizing a Keras package under a TensorFlow framework, and the performance of the module is evaluated according to the comparison of a diagnosis result and the actual condition. The method integrates residual convolution preprocessing and a multi-scale convolution long-term and short-term memory network, fully excavates the essential fire characteristics of the diesel engine, and realizes intelligent diagnosis of the fire of the diesel engine under strong noise and different noise domains.
Description
Technical Field
The invention relates to the field of diesel engine fault diagnosis, in particular to a method and a system for diagnosing noise-resistant and cross-noise domain misfire of a diesel engine, and more particularly relates to a method and a system for diagnosing noise-resistant and cross-noise domain misfire of a diesel engine by fusing residual convolution preprocessing and a multi-scale convolution long-short term memory network.
Background
The multi-cylinder diesel engine works stably and can obtain enough power. Compared with a gasoline engine, the diesel engine has the obvious advantages of long service life, economy, durability, low speed, large torque, safety, environmental protection and the like. Therefore, it has been widely used in the fields of construction machinery, automobile industry, ship machinery, electric power industry, agricultural machinery, and the like. Misfire is a common fault in diesel engines, mainly caused by failures of the electrical control system and mechanical components. Electronic control system faults including sensor signal loss or inaccuracy, control unit control signal failure or no signal output, ignition faults due to spark plug or ignition coil damage, injector firing faults, and circuit connection faults. Mechanical failures are mainly due to insufficient cylinder pressure, such as untight valve closure, leakage, etc. Misfire can result in severe vibration, insufficient power, poor acceleration, and high fuel consumption. Therefore, the online monitoring of the running state of the engine and the corresponding measures are of great significance.
Patent document CN103032190B (application number: 2012105725412) discloses a method and apparatus for detecting misfire of a diesel engine based on a rail pressure signal, comprising: acquiring instantaneous rail pressures corresponding to a starting tooth and an ending tooth of a current cylinder respectively; calculating a rail pressure drop value generated by the instantaneous rail pressure of the starting tooth and the ending tooth of each cylinder; calculating a rail pressure reduction standard value according to the rail pressure reduction values of all cylinders; dividing the rail pressure reduction value of each cylinder with the rail pressure reduction standard value to obtain a rail pressure reduction proportion value of each cylinder; respectively judging that the rail pressure drop value proportion value of each cylinder is smaller than a preset relative misfire threshold value, and judging that the cylinder misfire if the rail pressure drop value of the corresponding cylinder is smaller than a preset absolute misfire threshold value; otherwise, it is judged that there is no misfire.
Patent document CN102980777B (application number: 2012105627710) discloses a method and an apparatus for detecting misfire of a diesel engine based on single-cylinder angular acceleration, comprising: acquiring the instantaneous rotating speed of crankshaft teeth corresponding to the starting teeth and the ending teeth of the current cylinder; calculating a single cylinder angular acceleration of each cylinder from the instantaneous rotational speed of the crankshaft teeth; calculating a single-cylinder angular acceleration standard value according to the single-cylinder angular accelerations of all the cylinders; dividing the single-cylinder angular acceleration of each cylinder with the angular acceleration standard value to obtain a single-cylinder angular acceleration proportional value of each cylinder; respectively judging that the single-cylinder angular acceleration proportional value of each cylinder is smaller than a preset relative misfire threshold value, and judging that the cylinder misfire when the single-cylinder angular acceleration of the corresponding cylinder is smaller than a preset absolute misfire threshold value; otherwise there is no misfire.
The above patent needs to set the misfire threshold value when diagnosing the misfire, so it is difficult to avoid the diagnosis error caused by human experience, and because the measurement signal is usually interfered by various noises, the actual misfire diagnosis precision of the diesel engine under strong noise and the generalization performance between different noise levels are still limited.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for diagnosing noise-resistant and cross-noise-domain misfire of a diesel engine.
The invention provides a method for diagnosing noise-resistant and cross-noise-domain misfire of a diesel engine, which comprises the following steps of:
step S1: collecting vibration signals of a cylinder cover of the diesel engine on line;
step S2: designing a residual convolution preprocessing module to perform noise reduction preprocessing on an original vibration signal measured by a diesel engine cylinder based on a noise superposition principle, and constructing a loss function by using residual loss for model training;
step S3: designing a multi-scale convolution module to extract fault features of different time scales from the preprocessed signals;
step S4: extracting signal dependent features from the signal processed in step S3 by using LSTM;
step S5: a diesel engine misfire diagnosis model is constructed and trained by utilizing a Keras package under a TensorFlow framework, and the performance of the module is evaluated according to the comparison of a diagnosis result and the actual condition.
Preferably, the noise-reduced signal of step S2 is the original signal minus the residual convolution pre-processing module output signal.
Preferably, the loss function in step S2 is:
therein, loss β For the constructed loss function, n is the number of samples delivered to the model per iteration, β is the preset noise template, output i The noise signal output beta obtained after the ith sample signal passes through a residual convolution preprocessing module is i Representing the random noise signal carried by the ith sample signal.
Preferably, the multi-scale convolution module comprises a plurality of convolutional layer modules, each convolutional layer module comprising different branches and convolution kernels of different sizes.
Preferably, a plurality of the convolutional layer modules are connected in series to extract fault features of different time scales.
The invention provides a diesel engine anti-noise and cross-noise-domain misfire diagnostic system, which comprises:
module M1: collecting vibration signals of a cylinder cover of the diesel engine on line;
module M2: based on a noise superposition principle, carrying out noise reduction pretreatment on an original vibration signal measured by a diesel engine cylinder through a residual convolution pretreatment module, and constructing a loss function by using residual loss for model training;
module M3: extracting fault features of different time scales from the preprocessed signals by using a multi-scale convolution module;
module M4: extracting signal dependent features from the signal processed by the module M3 by using the LSTM;
a module M5: a diesel engine misfire diagnosis model is constructed and trained by utilizing a Keras package under a TensorFlow framework, and the performance of the module is evaluated according to the comparison of a diagnosis result and the actual condition.
Preferably, the noise-reduced signal of the module M2 is the original signal minus the residual convolution pre-processing module output signal.
Preferably, the loss function in the module M2 is:
among them, loss β For the constructed loss function, n is the number of samples delivered to the model per iteration, β is the preset noise template, output i The noise signal output beta obtained after the ith sample signal passes through a residual convolution preprocessing module is i Representing the random noise signal carried by the ith sample signal.
Preferably, the multi-scale convolution module comprises a plurality of convolutional layer modules, each convolutional layer module comprising different branches and convolution kernels of different sizes.
Preferably, a plurality of the convolutional layer modules are connected in series to extract fault features of different time scales.
Compared with the prior art, the invention has the following beneficial effects:
1. the method designs a residual convolution preprocessing module to process original vibration signals measured by a diesel engine cylinder, constructs a new loss function for model training through residual loss, and provides a basis for extracting essential failure of diesel engine fire failure subsequently;
2. the invention designs a multi-scale convolution module to realize multi-scale feature extraction so as to enhance the robustness of the model to the time scale when learning the fire fault feature, and simultaneously utilizes LSTM to extract the correlation feature so as to further improve the anti-noise and noise domain adaptability;
3. the method integrates residual convolution preprocessing and a multi-scale convolution long-term and short-term memory network, fully excavates the essential fire characteristics of the diesel engine, realizes intelligent diagnosis of the fire of the diesel engine under strong noise and different noise domains, is beneficial to follow-up adoption of corresponding operation and maintenance decisions, and improves the automation and intelligence level of the diesel engine.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a diesel anti-noise and cross-noise domain misfire diagnostic method implementation proposed by the present invention;
FIG. 2 is a network structure diagram of a noise-resistant and cross-noise domain misfire diagnostic model of a diesel engine according to the present invention;
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Aiming at the problems that the misfire threshold needs to be set in the current diesel engine misfire diagnosis, the misfire diagnosis precision under strong noise and the generalization performance between different noise levels is limited, the invention provides the diesel engine anti-noise and cross-noise domain misfire diagnosis method and system integrating residual convolution preprocessing and the multi-scale convolution long-short term memory network. The method is characterized in that a residual convolution preprocessing module is designed to process original vibration signals measured by a diesel engine cylinder, a multi-scale convolution module is designed to realize multi-scale feature extraction, robustness of a model to a time scale when learning fire fault features is enhanced, and correlation features are extracted by using LSTM to further improve anti-noise and noise domain adaptability. The method integrates residual convolution preprocessing and a multi-scale convolution long-term and short-term memory network, fully excavates the essential fire characteristics of the diesel engine, and can be used for intelligent diagnosis of the fire of the diesel engine under strong noise and different noise domains through correlation analysis.
Example 1:
the invention discloses a method for diagnosing noise-resistant and cross-noise-domain misfire of a diesel engine, which comprises the following steps of:
step S1: collecting vibration signals of a diesel engine cylinder cover on line, and constructing a data section at every 1024 points for input model processing;
step S2: based on a noise superposition principle, a seven-layer residual convolution preprocessing module is designed to carry out noise reduction preprocessing on an original vibration signal measured by a diesel engine cylinder, the original signal is subtracted by the output of a convolution block to obtain a signal after noise removal, a residual loss construction loss function is used for model training, and the loss function is as follows:
therein, loss β For the constructed loss function, n is the number of samples delivered to the model per iteration, β is the preset noise template, output i The noise signal output beta obtained after the ith sample signal passes through a residual convolution preprocessing module is i Representing the random noise signal carried by the ith sample signal.
Step S3: and designing a multi-scale convolution module to extract fault features of different time scales from the preprocessed signals, wherein the multi-scale convolution module comprises a plurality of convolution layer modules, and each convolution layer module comprises convolution kernels of different branches and different sizes. In the embodiment, the number of the convolutional layer modules is preferably four, and the four convolutional layer modules are mutually connected in series and used for extracting different time scale characteristics in signals so as to enhance the robustness of the model for learning the misfire fault characteristics to the time scale;
step S4: extracting signal dependence characteristics from the signal processed in the step S3 by using the LSTM, and further improving the anti-noise and cross-noise domain adaptability performance;
step S5: a diesel engine misfire diagnosis model is built and trained by utilizing a Keras package under a TensorFlow frame, and the performance of the module is evaluated according to the diagnosis result and the actual comparison.
The diesel engine fire diagnosis model integrates residual convolution preprocessing and a multi-scale convolution long-term and short-term memory network, fully excavates the fire intrinsic characteristics of the diesel engine, realizes intelligent diagnosis of the diesel engine fire under strong noise and different noise domains, is beneficial to follow-up adoption of corresponding operation and maintenance decisions, and improves the automation and intelligence level of the diesel engine.
The invention also discloses a system for diagnosing noise resistance and cross-noise domain misfire of the diesel engine, which comprises the following components:
module M1: collecting vibration signals of a diesel engine cylinder cover on line, and constructing a data section at every 1024 points for input model processing;
module M2: based on a noise superposition principle, carrying out noise reduction pretreatment on an original vibration signal measured by a diesel engine cylinder through a residual convolution pretreatment module, and constructing a loss function by using residual loss for model training, wherein the loss function is as follows:
therein, loss β For the constructed loss function, n is the number of samples delivered to the model per iteration, β is the preset noise template, output i The noise signal output beta obtained after the ith sample signal passes through a residual convolution preprocessing module is i Representing the random noise signal carried by the ith sample signal.
Module M3: extracting fault features of different time scales from the preprocessed signals by using a multi-scale convolution module; the multi-scale convolution module comprises a plurality of convolution layer modules, each convolution layer module comprises convolution kernels with different branches and different sizes, the convolution layer modules are connected in series, and fault features of different time scales are extracted
Module M4: extracting signal dependent features from the signal processed by the module M3 by using the LSTM;
a module M5: a diesel engine misfire diagnosis model is constructed and trained by utilizing a Keras package under a TensorFlow framework, and the performance of the module is evaluated according to the comparison of a diagnosis result and the actual condition.
Example 2:
example 2 is a modification of example 1.
Referring to fig. 1 to 2, the present invention provides a method for diagnosing anti-noise and cross-noise domain misfire of a diesel engine, comprising the steps of:
step S1: collecting vibration signals of a diesel engine cylinder cover on line, and constructing a data section at every 1024 points for input model processing;
step S2: based on a noise superposition principle, designing a residual convolution preprocessing module to perform noise reduction preprocessing on an original vibration signal measured by a diesel engine cylinder, and constructing a new loss function by using residual loss for model training:
therein, loss β For the constructed loss function, n is the batch size, i.e., the number of samples transferred to the model per iteration, and β is a predetermined noise template, output i And outputting the noise signal obtained after the ith sample signal passes through the residual convolution preprocessing module, wherein beta represents the random noise signal carried by the ith sample signal.
Step S3: four multi-scale convolution modules are designed to realize multi-scale feature extraction so as to enhance the robustness of the model to the time scale when learning the fire fault features;
step S4: extracting correlation characteristics by using the single-layer LSTM to further improve the anti-noise and noise domain adaptability;
step S5: a diesel engine misfire diagnosis model is constructed and trained by utilizing a Keras package under a TensorFlow framework, and the model performance is evaluated according to the comparison of a diagnosis result and the actual condition.
The model is tested and evaluated, and the average precision of the model is up to 97% under the strong noise condition (-10dB signal to noise ratio) of four data sets under different working conditions. Meanwhile, training is carried out on a data set with a signal to noise ratio of-10 dB under the same working condition, cross-noise-domain diagnosis is carried out on test sets with signal to noise ratios of-8 dB, -6dB, -4dB, -2dB,0dB,2dB,4dB,6dB,8dB and 10dB respectively, and the accuracy rates of the models are 97.851%, 97.851%, 97.460%, 97.851%, 97.070%, 97.460%, 97.460% and 97.460% respectively. The results show that the proposed intelligent diagnosis model for the diesel engine fire combines residual convolution preprocessing and a multi-scale convolution long-term and short-term memory network, fully excavates the essential characteristics of the diesel engine fire, can be used for intelligent diagnosis of the diesel engine fire under strong noise and different noise domains, is beneficial to follow-up adoption of corresponding operation and maintenance decisions, and improves the automation and intelligence levels of the diesel engine.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A method for diagnosing noise-resistant and cross-noise-domain misfire of a diesel engine is characterized by comprising the following steps:
step S1: collecting vibration signals of a cylinder cover of the diesel engine on line;
step S2: designing a residual convolution preprocessing module to perform noise reduction preprocessing on an original vibration signal measured by a diesel engine cylinder based on a noise superposition principle, and constructing a loss function by using residual loss for model training;
step S3: designing a multi-scale convolution module to extract fault features of different time scales from the preprocessed signals;
step S4: extracting signal dependent features from the signal processed in step S3 by using LSTM;
step S5: a diesel engine misfire diagnosis model is constructed and trained by utilizing a Keras package under a TensorFlow framework, and the performance of the module is evaluated according to the comparison of a diagnosis result and the actual condition.
2. The diesel anti-noise and cross-noise domain misfire diagnostic method of claim 1, characterized by: the noise-reduced signal in the step S2 is the original signal minus the output signal of the residual convolution preprocessing module.
3. The diesel anti-noise and cross-noise domain misfire diagnostic method of claim 1, characterized by: the loss function in step S2 is:
therein, loss β For the constructed loss function, n is the number of samples delivered to the model per iteration, β is the preset noise template, output i The noise signal output beta obtained after the ith sample signal passes through a residual convolution preprocessing module is i Representing the random noise signal carried by the ith sample signal.
4. The diesel anti-noise and cross-noise domain misfire diagnostic method of claim 1, characterized by: the multi-scale convolution module includes a plurality of convolutional layer modules, each convolutional layer module including different branches and convolutional kernels of different sizes.
5. The diesel anti-noise and cross-noise domain misfire diagnostic method of claim 4, characterized by: and the plurality of convolutional layer modules are connected in series to extract fault characteristics of different time scales.
6. A diesel anti-noise and cross-noise domain misfire diagnostic system comprising:
module M1: collecting vibration signals of a cylinder cover of the diesel engine on line;
module M2: based on a noise superposition principle, carrying out noise reduction pretreatment on an original vibration signal measured by a diesel engine cylinder through a residual convolution pretreatment module, and constructing a loss function by using residual loss for model training;
module M3: extracting fault features of different time scales from the preprocessed signals by using a multi-scale convolution module;
module M4: extracting signal dependent features from the signal processed by the module M3 by using the LSTM;
module M5: a diesel engine misfire diagnosis model is built and trained by utilizing a Keras package under a TensorFlow frame, and the performance of the module is evaluated according to the diagnosis result and the actual comparison.
7. The diesel anti-noise and cross-noise domain misfire diagnostic system of claim 6, characterized in that: the noise-reduced signal of the module M2 is the original signal minus the output signal of the residual convolution preprocessing module.
8. The diesel anti-noise and cross-noise domain misfire diagnostic system of claim 6, characterized in that: the loss function in the module M2 is:
therein, loss β For the constructed loss function, n is the number of samples delivered to the model per iteration, β is the preset noise template, output i The noise signal output beta obtained after the ith sample signal passes through a residual convolution preprocessing module is i Representing the random noise signal carried by the ith sample signal.
9. The diesel anti-noise and cross-noise domain misfire diagnostic system of claim 6, characterized in that: the multi-scale convolution module includes a plurality of convolutional layer modules, each convolutional layer module including different branches and convolutional kernels of different sizes.
10. The diesel anti-noise and cross-noise domain misfire diagnostic system of claim 9, characterized in that: and the plurality of convolutional layer modules are connected in series to extract fault characteristics of different time scales.
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| 叶壮;余建波;: "基于多通道一维卷积神经网络特征学习的齿轮箱故障诊断方法", 振动与冲击 * |
| 张康等: "随机丢弃和批标准化的深度卷积神经网络柴油机失火故障诊断", 《西安交通大学学报》 * |
| 池福临等: "基于深度收缩残差网络的轴承变工况故障诊断", 《计算机集成制造***》 * |
| 贾继德;贾翔宇;韩佳佳;任刚;: "基于单振动传感器BP神经网络的柴油机失火诊断", 军事交通学院学报 * |
| 赵海鹏: "柴油机典型故障特征提取与智能诊断方法研究", 《中国博士学位论文全文数据库-工程科技II辑》 * |
| 高文志等: "基于卷积神经网络的柴油机失火故障实时诊断", 《吉林大学学报（工学版）》 * |
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