A diesel engine crankcase active safety monitoring system and monitoring method

Abstract

The present invention discloses a diesel engine crankcase active safety monitoring system and method, the diesel engine crankcase active safety monitoring system comprising: a blowby monitoring module, which is used to monitor the blowby gas flow rate of the engine crankcase; an oil and gas monitoring module, which is used to monitor the oil mist concentration of the engine crankcase; an engine-side monitoring box, which is used to collect the blowby gas flow rate, the oil mist concentration, the engine speed and the engine torque, and send them; a cloud server, which is used to determine the degree of wear of the engine piston ring and the main bearing according to the blowby gas flow rate, the oil mist concentration, the engine speed and the engine torque sent by the engine-side monitoring box through a trained transfer learning model, thereby realizing accurate monitoring of the engine crankcase blowby condition, and accurately identifying the degree of wear of the engine piston ring and the main bearing through the transfer learning model, thereby realizing real-time monitoring of the healthy online status of the engine.

Description

Technical Field

[0001] The present application relates to the technical field of diesel engine status monitoring, and in particular to a diesel engine crankcase active safety monitoring system and monitoring method. Background Art

[0002] As the power density of automotive diesel engines increases, their in-cylinder pressure and piston linear velocity also increase significantly. This increases the tensile stress on the engine connecting rod, significantly increasing the risk of wear on the engine's piston rings and bearings. Piston ring wear increases crankcase blowby, which can affect the engine's charging efficiency and reduce the compression ratio of the combustion chamber, resulting in reduced engine output and efficiency. Bearing wear increases the oil content of crankcase blowby, causing some oil to be carried away through the crankcase ventilation system, resulting in excessive engine oil consumption and the need for more frequent oil replenishment, which increases vehicle operating costs.

[0003] Due to the tight chassis layout of diesel engine vehicles, existing crankcase active safety systems cannot be installed on current automotive diesel engines, resulting in the inability to identify bearing wear. Therefore, it is necessary to develop new monitoring methods to monitor the engine crankcase status and thus accurately identify bearing wear. Summary of the Invention

[0004] The present application provides a diesel engine crankcase active safety monitoring system and method, which can solve the technical problem in the prior art that diesel engine vehicles cannot identify bearing wear.

[0005] In a first aspect, an embodiment of the present application provides a diesel engine crankcase active safety monitoring system, the diesel engine crankcase active safety monitoring system comprising:

[0006] A blowby gas monitoring module, which is used to monitor the blowby gas flow rate of the engine crankcase;

[0007] An oil and gas monitoring module, which is used to monitor the oil mist concentration in the engine crankcase;

[0008] an engine-side monitoring box, which is used to collect the blowby gas flow rate, the oil mist concentration, the engine speed and the engine torque, and transmit the data;

[0009] The cloud server is used to determine the degree of wear of the engine piston ring and the main bearing based on the blowby gas flow, the oil mist concentration, the engine speed and the engine torque sent by the machine-side monitoring box through a trained transfer learning model.

[0010] In conjunction with the first aspect, in one embodiment, the blowby gas monitoring module includes a blowby gas monitoring pipeline and a flow sensor;

[0011] The blowby gas monitoring pipeline includes a diffuser, a pressure stabilizing chamber and a venturi tube connected in sequence, and the diffuser is connected to the engine crankcase ventilation interface;

[0012] The flow sensor is arranged in the throat of the venturi tube.

[0013] In one embodiment, the oil and gas monitoring module includes an oil mist concentration sensor;

[0014] The oil mist concentration sensor is arranged in the throat of the venturi tube.

[0015] In one embodiment, the machine-side monitoring box includes:

[0016] a collection module, configured to collect the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque;

[0017] a processing module, configured to package the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque to generate packaged data;

[0018] A transmitting module, configured to transmit the packaged data to the cloud server;

[0019] A thermal insulation coating is used to wrap the acquisition module, the processing module and the emission module.

[0020] In one embodiment, the cloud server is further configured to:

[0021] Performing chi-square test, information gain ratio and correlation coefficient test on the received packaged data, with the goal of minimizing redundancy of the correlation coefficient and maximizing correlation between the information gain ratio and the chi-square test result, to identify target data in the packaged data;

[0022] Performing a simplification dimensionality reduction and reconstruction on the target data to generate dimensionality-reduced target data;

[0023] The outliers in the dimensionality reduction target data are identified by a spatial clustering algorithm, and the outliers are filtered to obtain the final target data.

[0024] In one embodiment, the cloud server is further configured to:

[0025] The blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque in the final target data are input into the trained transfer learning model to obtain the wear degree of the engine piston ring and the main bearing.

[0026] In one embodiment, the cloud server is further configured to:

[0027] The engine is used as source domain auxiliary data for a plurality of operating condition data of the engine piston ring and main bearing at different wear degrees under the first operating condition, the second operating condition, and the third operating condition, the wear degrees of the engine piston ring and main bearing, the corresponding blowby gas flow rate, and the oil mist concentration, wherein the operating condition data includes the engine load rate and the engine speed, and the wear degrees of the engine piston ring and main bearing include no wear, slight wear, moderate wear, and severe wear;

[0028] The target domain data includes the engine operating condition data of the fourth operating condition, the engine piston ring and main bearing wear degrees, the engine piston ring and main bearing wear degrees, the corresponding blowby gas flow rate and oil mist concentration;

[0029] All the source domain auxiliary data and M data in the target domain data are used as training data, and all the target domain data are used as test data to train the transfer learning model to obtain the trained transfer learning model.

[0030] In a second aspect, an embodiment of the present application provides a diesel engine crankcase active safety monitoring method implemented by any one of the above diesel engine crankcase active safety monitoring systems, the diesel engine crankcase active safety monitoring method comprising:

[0031] Monitor the blowby gas flow rate of the engine crankcase through the blowby gas monitoring module;

[0032] Monitoring the oil mist concentration of the engine crankcase through an oil and gas monitoring module;

[0033] collecting the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque through a machine-side monitoring box and transmitting the collected data;

[0034] The cloud server determines the degree of wear of the engine piston ring and main bearing based on the blowby gas flow, the oil mist concentration, the engine speed and the engine torque sent by the machine-side monitoring box through a trained transfer learning model.

[0035] In conjunction with the second aspect, in one embodiment, the engine-side monitoring box includes a collection module, a processing module, a transmission module, and a thermal insulation coating. The engine-side monitoring box collects the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque and transmits the collected data, including:

[0036] collecting the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque through the collection module;

[0037] Packaging the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque through the processing module to generate packaged data;

[0038] Sending the packaged data to the cloud server through the transmitting module;

[0039] The collection module, the processing module and the emission module are wrapped by the thermal insulation coating.

[0040] In one embodiment, before determining the wear degree of the engine piston ring and main bearing by using a trained transfer learning model based on the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque sent by the on-board monitoring box through a cloud server, the method further includes:

[0041] Performing chi-square test, information gain ratio and correlation coefficient test on the received packaged data by the cloud server, with the goal of minimizing redundancy of correlation coefficient and maximizing correlation between information gain ratio and chi-square test result, to identify target data in the packaged data;

[0042] Performing a simplification dimensionality reduction and reconstruction on the target data to generate dimensionality-reduced target data;

[0043] The outliers in the dimensionality reduction target data are identified by a spatial clustering algorithm, and the outliers are filtered to obtain the final target data.

[0044] An embodiment of the present application provides a diesel engine crankcase active safety monitoring system and method, the diesel engine crankcase active safety monitoring system comprising: a blowby monitoring module, which is used to monitor the blowby gas flow rate of the engine crankcase; an oil and gas monitoring module, which is used to monitor the oil mist concentration of the engine crankcase; an engine-side monitoring box, which is used to collect the blowby gas flow rate, the oil mist concentration, the engine speed and the engine torque, and send them; a cloud server, which is used to determine the degree of wear of the engine piston ring and the main bearing based on the blowby gas flow rate, the oil mist concentration, the engine speed and the engine torque sent by the engine-side monitoring box through a trained transfer learning model, thereby realizing accurate monitoring of the engine crankcase blowby condition, and accurately identifying the degree of wear of the engine piston ring and the main bearing through the transfer learning model, thereby realizing real-time monitoring of the healthy online status of the engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the architecture of the diesel engine crankcase active safety monitoring system of this application;

[0046] Figure 2 This is a schematic diagram of the installation positions of the flow sensor and oil mist concentration sensor;

[0047] Figure 3 This is a functional module diagram of the machine-side monitoring box for this application;

[0048] Figure 4Schematic diagram of the transfer learning model training process;

[0049] Figure 5 This is a flow chart of an embodiment of a method for active safety monitoring of a diesel engine crankcase according to the present application. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0052] In a first aspect, an embodiment of the present application provides a diesel engine crankcase active safety monitoring system, which can be used in various types of engine vehicles such as diesel engines or gasoline engines.

[0053] In one embodiment, referring to Figure 1 , Figure 1 This is a schematic diagram of the architecture of the diesel engine crankcase active safety monitoring system in this application. Figure 1 As shown in the figure, the diesel engine crankcase active safety monitoring system includes:

[0054] A blowby gas monitoring module, which is used to monitor the blowby gas flow rate of the engine crankcase;

[0055] An oil and gas monitoring module is used to monitor the oil mist concentration in the engine crankcase.

[0056] an engine-side monitoring box, which is used to collect the blowby gas flow rate, the oil mist concentration, the engine speed and the engine torque, and transmit the data;

[0057] The cloud server is used to determine the degree of wear of the engine main bearing according to the blowby gas flow, the oil mist concentration, the engine speed and the engine torque sent by the machine-side monitoring box and through a trained transfer learning model.

[0058] In this embodiment, the blowby gas monitoring module and the oil-gas monitoring module accurately monitor the blowby gas flow rate and oil mist concentration in the engine crankcase. The monitored blowby gas flow rate and oil mist concentration, along with the engine speed and engine torque, are used as inputs to a trained transfer learning model. This trained transfer learning model accurately and efficiently identifies the degree of wear on the engine piston rings and main bearings. The calculation process for determining the degree of wear on the engine piston rings and main bearings using the trained transfer learning model is performed in a cloud server, improving computational efficiency and effectively reducing data processing pressure on the vehicle side. Furthermore, the diesel engine crankcase active safety monitoring system provided in this application utilizes a modular design, enhancing the system's interchangeability and versatility.

[0059] In one embodiment, if Figure 2 As shown, the blowby monitoring module includes a blowby monitoring pipeline and a flow sensor; the blowby monitoring pipeline includes a diffuser, a pressure stabilizing chamber and a venturi tube connected in sequence, and the diffuser is connected to the engine crankcase ventilation interface; the flow sensor is arranged in the throat of the venturi tube.

[0060] The oil and gas monitoring module includes an oil mist concentration sensor, which is arranged in the throat of the venturi tube.

[0061] It is worth noting that the diffuser can increase the airflow pressure and stabilize the airflow through the pressure stabilizing chamber. The flow sensor and oil mist concentration sensor are set in the throat of the Venturi tube. When the airflow passes through the throat part of the Venturi tube, the airflow velocity increases and the pressure decreases, thereby realizing accurate monitoring of the blowby airflow and mist concentration in the engine crankcase.

[0062] The oil mist concentration sensor operates primarily based on the principle of light scattering. It detects the intensity of light at a specific wavelength to identify the type and density of reflective materials, thereby measuring oil mist concentration. The oil mist concentration sensor utilizes the phenomenon of light scattering, which occurs when light passes through the sample gas and is reflected in all directions by impurity particles suspended in the gas. By detecting the intensity of this scattered light, the sensor can identify the type and density of the reflective material, thereby accurately determining the oil mist concentration.

[0063] In some optional embodiments, such as Figure 3As shown, the on-board monitoring box includes: a collection module for collecting the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque; a processing module for packaging the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque to generate packaged data; a transmission module for transmitting the packaged data to the cloud server; and a thermal insulation coating for covering the collection module, the processing module, and the transmission module. The on-board monitoring box also includes a radiator for dissipating heat from each module in the on-board monitoring box.

[0064] It's worth noting that the thermal insulation coating is used to isolate engine heat radiation. By wrapping the acquisition module, processing module, and transmitter module, it effectively prevents high-temperature engine gases from damaging the electronic components in the on-board monitoring box. The acquisition module includes an acquisition board that collects blowby flow signals from the flow sensor, oil mist concentration signals from the oil mist concentration sensor, and engine speed and torque signals from the vehicle, and transmits them to the processing module via TCP / IP. The processing module in this embodiment can utilize an NI 9033 chassis, with an underlying FPGA-based algorithm for real-time signal processing. The signals are then packaged and transmitted to a cloud server via the transmitter module.

[0065] It is worth noting that before determining the degree of wear of the engine piston rings and main bearings through the trained transfer learning model, during the engine development stage, fault simulation tests can be conducted to test different piston ring and bearing wear conditions, and the engine piston ring and bearing wear data, as well as the corresponding blowby gas flow rate and oil mist concentration, can be recorded. This data can be used as the training data for the transfer learning model to obtain a trained transfer learning model.

[0066] Specifically, when training the transfer learning model, the cloud server is also used to: use multiple operating condition data of the engine piston ring and main bearing with different degrees of wear under the first operating condition, the second operating condition and the third operating condition, the degree of wear of the engine piston ring and the main bearing, the corresponding blowby gas flow rate and the oil mist concentration as source domain auxiliary data, wherein the operating condition data include the engine load rate and the engine speed, and the degree of wear of the engine piston ring and the main bearing includes no wear, slight wear, moderate wear and severe wear; use multiple operating condition data of the engine piston ring and main bearing with different degrees of wear under the fourth operating condition, the degree of wear of the engine piston ring and the main bearing, the corresponding blowby gas flow rate and the oil mist concentration as target domain data; use all the source domain auxiliary data and M data of the target domain data as training data, and use all the target domain data as test data to train the transfer learning model to obtain the trained transfer learning model.

[0067] For example, in this embodiment, the first engine operating condition includes a 20% engine load and a speed of 1500 rpm, the second engine operating condition includes a 40% engine load and a speed of 1500 rpm, the third engine operating condition includes an 80% engine load and a speed of 1500 rpm, and the fourth engine operating condition includes a 60% engine load and a speed of 1500 rpm. The wear level of the engine piston rings and main bearings includes four levels: no wear, slight wear, moderate wear, and severe wear. The wear level can be set based on the percentage of wear on the engine piston rings and main bearings. M can be set as needed; in this embodiment, M is 100.

[0068] In this example, the source domain auxiliary data is used for the first, second, and third engine operating conditions, along with the wear levels of the piston rings and main bearings in the fourth gear engine, as well as the corresponding blowby gas flow rate and oil mist concentration. The target domain data is used for the fourth engine operating condition, along with the wear levels of the piston rings and main bearings in the fourth gear engine, as well as the corresponding blowby gas flow rate and oil mist concentration. The data divisions for the source domain auxiliary data and target domain data are shown in Table 1.

[0069] Table 1. Division of source domain auxiliary data and target domain data

[0070]

[0071] Among them, the number of source domain auxiliary data is 6,000, including 12 situations in 3 engine operating conditions and 4 wear gears, with 500 data for each situation. The number of target domain data is 400, including 4 situations in 1 engine operating condition and 4 wear gears, with 100 data for each situation. In this embodiment, the training set of the transfer learning model includes 6,000 source domain auxiliary data and 100 target domain data randomly selected from the 400 target domain data, totaling 6,100 data. The training set of the transfer learning model includes 400 target domain data. The transfer learning model is trained using a training set and a test set. The data of the test set are the crankcase signal feature data of the engine at 60% load, and the data of the training set are the data of the engine at 20% load, 40% load and 80% load plus a very small amount of 60% load data. If the diagnostic success rate of the transfer learning model reaches the preset success rate threshold, it means that the diagnostic model can realize the transfer of knowledge between different loads, and the training of the transfer learning model is completed.

[0072] The transfer learning model in this embodiment is the TrAdaBoost model. When the TrAdaBoost model is used in diesel engine crankcase condition monitoring and diagnosis, the source domain auxiliary data and a small amount of target domain data are used as training sets, and then the target domain data is used as a training set. It is imported into the final classifier to obtain the diagnosis result, solving the problem of low diagnostic accuracy caused by different data distribution characteristics due to variable working conditions. The training process is as follows: Figure 4Its impact on the training process, the specific steps are as follows:

[0073] In the first step, the input of the model includes the training set T and the test set S. The training set T consists of two parts: the auxiliary training set Ta and the source domain training set Tb. The auxiliary training set Ta consists of 100 target domain data randomly selected from the 400 target domain data, and the source domain training set Tb consists of 6000 source domain auxiliary data.

[0074] The second step is to calculate the initial weight :

[0075]

[0076]

[0077] Among them, β is the learning machine weight, N is the maximum number of iterations, n is the number of samples in the auxiliary training set Ta, and m is the number of samples in the source domain training set Tb.

[0078] The third step is to set the time step t=1,2,…,N and set the weight , the output prediction value ht can be calculated, and the error is:

[0079]

[0080] in, is the true value, and x is the sample in the test set S.

[0081] The fourth step is to set ;

[0082]

[0083] in, is the learning machine weight for each iteration.

[0084] Step 5: Calculate new training weights:

[0085]

[0086] Step 6: Calculate the final classifier:

[0087]

[0088] The transfer learning is trained in sequence to obtain a trained transfer learning model, and the trained transfer learning model is placed in the cloud server for subsequent identification of the wear degree of the engine piston ring and main bearing.

[0089] As a preferred embodiment, the cloud server is used to determine the degree of wear of the engine main bearing based on the blowby flow rate, the oil mist concentration, the engine speed and the engine torque sent by the machine-side monitoring box and through a trained transfer learning model. It is also used to: perform a chi-square test, an information gain ratio and a correlation coefficient test on the received packaged data, and identify the target data in the packaged data with the goal of minimum redundancy of the correlation coefficient and maximum correlation between the information gain ratio and the chi-square test result; perform a simplification dimensionality reduction reconstruction on the target data to generate reduced-dimensionality target data; identify outliers in the reduced-dimensionality target data through a spatial clustering algorithm, and filter the outliers to obtain the final target data.

[0090] Exemplarily, in this embodiment, the algorithms used for simplification and dimensionality reduction reconstruction of target data include linear discriminant analysis LDA (Linear Discriminant Analysis) dimensionality reduction, principal component analysis PAC (Principal Component Analysis) dimensionality reduction, or autoencoder dimensionality reduction. In this embodiment, the spatial clustering algorithm includes the K-Means algorithm or the density-based noise application spatial clustering DBSCAN algorithm. Since the data collected in real time and the data in the laboratory environment are very different, in order to extract useful data, the chi-square test, information gain ratio and correlation coefficient test are used to identify useful target data; then the target data is simplified through the reduction algorithm, and the singular values ​​are identified and filtered through DBSCAN to obtain the final target data, thereby ensuring the accuracy of the output of the engine piston ring and main bearing wear degree by inputting the data into the transfer learning model.

[0091] The cloud server is also used to input the blowby flow, the oil mist concentration, the engine speed and the engine torque in the final target data into the trained transfer learning model to obtain the engine main bearing fault degree output by the trained transfer learning model.

[0092] In some optional embodiments, the cloud service is also used to send the obtained engine piston ring and main bearing wear levels to a preset terminal and vehicle end via notification information, so that the user can know the engine status in time and perform engine maintenance.

[0093] The diesel engine crankcase active safety monitoring system provided in this embodiment adopts a modular design, dividing the system into a blowby monitoring module, an oil and gas monitoring module, an on-board monitoring box, and a cloud server. The blowby monitoring module includes a variable pressure reduction port, a pressure stabilizing chamber, a venturi tube, and a flow sensor located at the throat of the venturi tube, enabling accurate monitoring of the engine crankcase's blowby flow. The oil and gas content detection module includes an oil mist concentration sensor installed at the throat of the venturi tube, which uses a photoelectric sensor to identify the crankcase's oil and gas status. The on-board test box is used to transmit information and is equipped with a thermal insulation coating to prevent damage to electronic components caused by high-temperature gases. The cloud service can be developed based on the Nandou Cloud. A transfer learning model based on a supervised learning algorithm analyzes and diagnoses the signals transmitted by the on-board test box and sends fault information on the wear level of the engine piston rings and main bearings to the user. The modular system design improves the system's interchangeability and versatility. The wear level of the engine main bearings is calculated in the cloud server, improving computing efficiency and reducing data processing pressure on the vehicle side.

[0094] In a second aspect, an embodiment of the present application also provides a method for active safety monitoring of a diesel engine crankcase.

[0095] In one embodiment, referring to Figure 5 , Figure 5 This is the process intention of an embodiment of the diesel engine crankcase active safety monitoring method of this application. Figure 5 As shown, the diesel engine crankcase active safety monitoring method includes:

[0096] Monitor the blowby gas flow rate of the engine crankcase through the blowby gas monitoring module;

[0097] Monitoring the oil mist concentration of the engine crankcase through an oil and gas monitoring module;

[0098] collecting the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque through a machine-side monitoring box and transmitting the collected data;

[0099] The cloud server determines the degree of wear of the engine piston ring and main bearing based on the blowby gas flow, the oil mist concentration, the engine speed and the engine torque sent by the machine-side monitoring box through a trained transfer learning model.

[0100] Furthermore, in one embodiment, the blowby gas monitoring module includes a blowby gas monitoring pipeline and a flow sensor;

[0101] The blowby gas monitoring pipeline includes a diffuser, a pressure stabilizing chamber and a venturi tube connected in sequence, and the diffuser is connected to the engine crankcase ventilation interface;

[0102] The flow sensor is arranged in the throat of the venturi tube.

[0103] Furthermore, in one embodiment, the oil and gas monitoring module includes an oil mist concentration sensor;

[0104] The oil mist concentration sensor is arranged in the throat of the venturi tube.

[0105] Furthermore, in one embodiment, the engine-side monitoring box includes a collection module, a processing module, a transmission module, and a thermal insulation coating. The engine-side monitoring box collects the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque and transmits the collected data, including:

[0106] collecting the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque through the collection module;

[0107] Packaging the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque through the processing module to generate packaged data;

[0108] Sending the packaged data to the cloud server through the transmitting module;

[0109] The collection module, the processing module and the emission module are wrapped by the thermal insulation coating.

[0110] Furthermore, in one embodiment, before determining the degree of wear of the engine piston ring and main bearing by using a trained transfer learning model based on the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque sent by the on-board monitoring box via the cloud server, the method further includes:

[0111] Performing chi-square test, information gain ratio and correlation coefficient test on the received packaged data by the cloud server, with the goal of minimizing redundancy of correlation coefficient and maximizing correlation between information gain ratio and chi-square test result, to identify target data in the packaged data;

[0112] Performing a simplification dimensionality reduction and reconstruction on the target data to generate dimensionality-reduced target data;

[0113] The outliers in the dimensionality reduction target data are identified by a spatial clustering algorithm, and the outliers are filtered to obtain the final target data.

[0114] Furthermore, in one embodiment, the cloud server determines the degree of wear of the engine piston ring and main bearing based on the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque sent by the engine-side monitoring box using a trained transfer learning model, further comprising:

[0115] The blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque in the final target data are input into the trained transfer learning model to obtain the wear degree of the engine piston ring and the main bearing.

[0116] Furthermore, in one embodiment, the method further includes:

[0117] The cloud server is used to store multiple operating condition data of the engine under the first operating condition, the second operating condition, and the third operating condition, including the engine piston ring and main bearing wear degrees, the corresponding blowby gas flow rate, and the oil mist concentration, as source domain auxiliary data. The operating condition data includes the engine load rate and the engine speed. The engine piston ring and main bearing wear degrees include no wear, slight wear, moderate wear, and severe wear.

[0118] The target domain data includes the engine operating condition data of the fourth operating condition, the engine piston ring and main bearing wear degrees, the engine piston ring and main bearing wear degrees, the corresponding blowby gas flow rate and oil mist concentration;

[0119] All the source domain auxiliary data and M data in the target domain data are used as training data, and all the target domain data are used as test data to train the transfer learning model to obtain the trained transfer learning model.

[0120] Among them, the functional implementation of each module in the above-mentioned diesel engine crankcase active safety monitoring method corresponds to the various steps in the above-mentioned diesel engine crankcase active safety monitoring method embodiment, and their functions and implementation processes will not be repeated here one by one.

[0121] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0122] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0123] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0124] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0125] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0126] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of this application.

[0127] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A diesel engine crankcase active safety monitoring system, characterized in that: The diesel engine crankcase active safety monitoring system includes: A blowby gas monitoring module, which is used to monitor the blowby gas flow rate of the engine crankcase; An oil and gas monitoring module, which is used to monitor the oil mist concentration in the engine crankcase; an engine-side monitoring box, which is used to collect the blowby gas flow rate, the oil mist concentration, the engine speed and the engine torque, and transmit the data; a cloud server configured to determine the degree of wear of the engine piston ring and main bearing based on the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque sent by the on-board monitoring box and using a trained transfer learning model; The cloud server is also used for: The engine is used as source domain auxiliary data for a plurality of operating condition data of the engine piston ring and main bearing at different wear degrees under the first operating condition, the second operating condition, and the third operating condition, the wear degrees of the engine piston ring and main bearing, the corresponding blowby gas flow rate, and the oil mist concentration, wherein the operating condition data includes the engine load rate and the engine speed, and the wear degrees of the engine piston ring and main bearing include no wear, slight wear, moderate wear, and severe wear; The target domain data includes the engine operating condition data of the fourth operating condition, the engine piston ring and main bearing wear degrees, the engine piston ring and main bearing wear degrees, the corresponding blowby gas flow rate and oil mist concentration; All the source domain auxiliary data and M data in the target domain data are used as training data, and all the target domain data are used as test data to train the transfer learning model to obtain the trained transfer learning model.

2. The diesel engine crankcase active safety monitoring system according to claim 1, characterized in that: The blowby gas monitoring module includes a blowby gas monitoring pipeline and a flow sensor; The blowby gas monitoring pipeline includes a diffuser, a pressure stabilizing chamber and a venturi tube connected in sequence, and the diffuser is connected to the engine crankcase ventilation interface; The flow sensor is arranged in the throat of the venturi tube.

3. The diesel engine crankcase active safety monitoring system according to claim 2, characterized in that: The oil and gas monitoring module includes an oil mist concentration sensor; The oil mist concentration sensor is arranged in the throat of the venturi tube.

4. The diesel engine crankcase active safety monitoring system according to claim 1, characterized in that: The machine-side monitoring box includes: a collection module, configured to collect the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque; a processing module, configured to package the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque to generate packaged data; A transmitting module, configured to transmit the packaged data to the cloud server; A thermal insulation coating is used to wrap the acquisition module, the processing module and the emission module.

5. The diesel engine crankcase active safety monitoring system according to claim 4, characterized in that: The cloud server is also used for: Performing chi-square test, information gain ratio and correlation coefficient test on the received packaged data, with the goal of minimizing redundancy of the correlation coefficient and maximizing correlation between the information gain ratio and the chi-square test result, to identify target data in the packaged data; Performing a simplification dimensionality reduction and reconstruction on the target data to generate dimensionality-reduced target data; The outliers in the dimensionality reduction target data are identified by a spatial clustering algorithm, and the outliers are filtered to obtain the final target data.

6. The diesel engine crankcase active safety monitoring system according to claim 5, characterized in that: The cloud server is also used for: The blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque in the final target data are input into the trained transfer learning model to obtain the wear degree of the engine piston ring and the main bearing.

7. A diesel engine crankcase active safety monitoring method implemented based on the diesel engine crankcase active safety monitoring system according to any one of claims 1 to 6, characterized in that: The diesel engine crankcase active safety monitoring method comprises: Monitor the blowby gas flow rate of the engine crankcase through the blowby gas monitoring module; Monitoring the oil mist concentration of the engine crankcase through an oil and gas monitoring module; collecting the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque through a machine-side monitoring box and transmitting the collected data; Determining the degree of wear of the engine piston ring and main bearing by using a trained transfer learning model based on the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque sent by the on-board monitoring box via a cloud server; The method further includes: The cloud server is used to store multiple operating condition data of the engine under the first operating condition, the second operating condition, and the third operating condition, including the engine piston ring and main bearing wear degrees, the corresponding blowby gas flow rate, and the oil mist concentration, as source domain auxiliary data. The operating condition data includes the engine load rate and the engine speed. The engine piston ring and main bearing wear degrees include no wear, slight wear, moderate wear, and severe wear. The target domain data includes the engine operating condition data of the fourth operating condition, the engine piston ring and main bearing wear degrees, the engine piston ring and main bearing wear degrees, the corresponding blowby gas flow rate and oil mist concentration; All the source domain auxiliary data and M data in the target domain data are used as training data, and all the target domain data are used as test data to train the transfer learning model to obtain the trained transfer learning model.

8. The diesel engine crankcase active safety monitoring method according to claim 7, characterized in that: The machine-side monitoring box includes a collection module, a processing module, a transmission module, and a thermal insulation coating. The machine-side monitoring box collects the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque, and transmits them, including: collecting the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque through the collection module; Packaging the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque through the processing module to generate packaged data; Sending the packaged data to the cloud server through the transmitting module; The collection module, the processing module and the emission module are wrapped by the thermal insulation coating.

9. The diesel engine crankcase active safety monitoring method according to claim 8, characterized in that: Before determining the wear degree of the engine piston ring and the main bearing by using the trained transfer learning model based on the blowby gas flow rate, the oil mist concentration, the engine speed, and the engine torque sent by the engine side monitoring box through the cloud server, the method further includes: Performing chi-square test, information gain ratio and correlation coefficient test on the received packaged data by the cloud server, with the goal of minimizing redundancy of correlation coefficient and maximizing correlation between information gain ratio and chi-square test result, to identify target data in the packaged data; Performing a simplification dimensionality reduction and reconstruction on the target data to generate dimensionality-reduced target data; The outliers in the dimensionality reduction target data are identified by a spatial clustering algorithm, and the outliers are filtered to obtain the final target data.

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