A fault diagnosis system and method for a mechanical and electrical composite transmission system based on a risk order fault tree

Abstract

The application discloses a kind of mechanical and electrical composite transmission system fault diagnosis system and method based on risk order fault tree.The system includes vehicle body, vehicle data unit, fault diagnosis unit, cloud data analysis platform and other same type vehicles.The cloud data analysis platform is connected with vehicle data unit and other same type vehicles by communication network mode data respectively;The fault diagnosis unit and vehicle data unit are connected by CAN bus data transmission;The application is based on fault history big data, and the fault influence severity score of different fault phenomena and the fault occurrence frequency score and detection difficulty score of fault event are determined by statistical analysis, and the fault risk order number is calculated accordingly;Risk order fault tree based on big data is established by fault phenomenon and fault event;The difference between simulation results and vehicle body operating parameters after injecting predicted fault into vehicle physical model is compared with the relationship with preset threshold value to realize fault diagnosis, and the fault diagnosis accuracy is improved.

Description

Technical Field

[0001] This invention belongs to the field of fault diagnosis technology for electromechanical composite transmission systems, and in particular relates to a fault diagnosis system and method for electromechanical composite transmission systems based on risk sequence fault tree. Background Technology

[0002] Faced with increasingly severe environmental pollution problems, countries around the world have responded to the call and introduced relevant environmental protection policies, with a global call to enter the era of a low-carbon economy. In 2020, the China Society of Automotive Engineers released the "Technical Roadmap for Energy-Saving and New Energy Vehicles." Figure 2 The document proposes to actively promote the hybridization of traditional vehicles, including the full adoption of electric drive technology in both new energy vehicles and traditional vehicles, and that by 2035 all traditional energy-powered passenger vehicles will be hybrid.

[0003] A safe and reliable electromechanical hybrid drive system is a core technology for hybrid electric vehicles (HEVs). Currently, major domestic and international research institutes and high-efficiency equipment manufacturers have launched various highly integrated electromechanical hybrid drive systems specifically designed for HEVs. At present, integrating multiple clutches into an electromechanical hybrid drive system based on a planetary gear configuration is an important development direction for HEVs. Failures in any component of the electromechanical hybrid drive system will alter its constrained behavior, further leading to a decrease in vehicle performance. Facing electromechanical hybrid drive systems with a higher degree of electromechanical-hydraulic integration, especially in HEVs equipped with multiple power sources, the actual operating conditions of the electromechanical hybrid drive system are more complex. Traditional on-board self-diagnostic systems are limited by data volume, resulting in limited diagnostic range and self-diagnostic capabilities, and their diagnostic efficiency needs improvement.

[0004] By establishing a risk-sequence fault tree based on big data, historical fault data from vehicles with the same electromechanical hybrid transmission system was effectively utilized. Fault diagnosis of the electromechanical hybrid transmission system based on fault risk sequence can more effectively improve fault diagnosis efficiency and save diagnosis time and manpower costs when faults occur suddenly. Summary of the Invention

[0005] In view of the above-mentioned technological development trends and the shortcomings of existing technologies, the purpose of this invention is to propose a fault diagnosis system and method for electromechanical hybrid transmission systems based on risk sequence fault trees, so as to make full use of fault big data and solve the problem that it is difficult to effectively realize fault diagnosis of electromechanical hybrid transmission systems of hybrid vehicles in existing technologies.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0007] A fault diagnosis system for an electromechanical hybrid transmission system based on a risk-sequence fault tree includes: a vehicle body, an on-board data unit, a fault diagnosis unit, a cloud-based data analysis platform, and other vehicles of the same type. The vehicle body is equipped with a powertrain and an electromechanical hybrid transmission system. The cloud-based data analysis platform is connected to the on-board data unit and other vehicles of the same type via a communication network. The other vehicles of the same type are those manufactured with the same powertrain and electromechanical hybrid transmission system as the vehicle body. The on-board data unit includes a data acquisition module and a data storage module. The data acquisition module includes a speed sensor and a pressure sensor. The fault diagnosis unit transmits data with the on-board data unit via a CAN bus. The fault diagnosis unit includes a vehicle physical model, a fault prediction module, a fault diagnosis module, and a fault display module. The cloud-based data analysis platform includes a fault big data unit, a data analysis unit, and a risk-sequence fault tree based on big data. The fault big data unit includes a fault database and an FMEA module.

[0008] Furthermore, the power system includes an engine, an electric motor, and a battery.

[0009] Furthermore, the electromechanical composite transmission system includes a power coupling mechanism and a clutch; the power coupling mechanism is a double planetary gear mechanism; the double planetary gear mechanism includes a sun gear, a planet carrier, planet gears, and a ring gear.

[0010] Furthermore, the data acquisition module includes multiple detection sensors for real-time acquisition of dynamic data of the electromechanical hybrid transmission system of the hybrid vehicle; the detection sensors include speed sensors and pressure sensors.

[0011] Furthermore, the data storage module stores the vehicle's factory-preset basic parameter information, including the vehicle's mass parameters, structural parameters, and performance parameters of various power system components; the data storage module stores real-time detection data collected by sensors and classifies and stores the collected data; the data storage module can load risk sequence fault trees based on big data; and the data storage module stores diagnostic data from the fault diagnosis module.

[0012] Furthermore, the fault big data unit includes a fault database and an FMEA module. The fault database stores historical fault data of other in-use mechatronic transmission systems of the same model. This historical fault data includes losses during faults in the mechatronic transmission systems of the same model, fault diagnosis results, vehicle operating data under fault conditions, and specific fault events and sub-events that caused the faults. The FMEA module analyzes the historical fault data, marking the fault phenomena of different faults and their corresponding fault events and sub-events.

[0013] Furthermore, the data analysis unit analyzes the historical big data of faults stored in the fault big data unit, analyzes the loss situation when different faults occur, and assigns a fault impact severity score to the fault phenomenon; it statistically counts the fault events and fault sub-events of different fault phenomena, and calculates the fault occurrence frequency score of a certain fault event and fault sub-event; it analyzes the fault diagnosis results, counts the number of correct diagnoses and misdiagnoses of fault events and fault sub-events of different fault phenomena, and calculates the fault detection difficulty score of fault events and fault sub-events; it calculates the risk order number of fault events or fault sub-events, where the risk order number = fault impact severity score × fault occurrence frequency score × fault detection difficulty score.

[0014] Furthermore, the risk sequence fault tree based on big data is a fault tree of the electromechanical composite transmission system that is expanded according to the risk sequence number of fault events or fault sub-events after statistical analysis of historical fault big data by the data analysis unit.

[0015] Furthermore, the other vehicles of the same type are hybrid electric vehicles of the same model as the vehicle body, equipped with the same power system and electromechanical composite transmission system, and the basic parameter information preset at the factory is consistent with that of the vehicle body. The basic parameters include vehicle weight parameters and performance parameter information of various components of the power system.

[0016] Furthermore, the vehicle physical model is a simulation physical model with consistent parameters across the vehicle itself. These parameters include basic vehicle parameters, powertrain performance parameters, and structural parameters of each component of the electromechanical hybrid transmission system.

[0017] Furthermore, the fault prediction module predicts faults based on a risk sequence fault tree based on big data, and performs fault injection by traversing the vehicle physical model according to the risk sequence number of the fault.

[0018] Furthermore, the fault diagnosis module compares and analyzes the simulation results of the vehicle physical model with the actual vehicle operation data. When the error between the simulation result parameters of each component of the electromechanical composite transmission system and the actual vehicle operation data is less than a preset threshold, the fault diagnosis is completed, and the fault can be determined as the current fault event or a fault sub-event.

[0019] Furthermore, the fault display module displays the fault diagnosis results to remind the driver and facilitate maintenance personnel in carrying out subsequent maintenance work.

[0020] The present invention provides a fault diagnosis method for an electromechanical composite transmission system based on fault risk sequence, comprising the following steps:

[0021] (1) Sensors collect the actual operating data of the vehicle under actual working conditions;

[0022] (2) Establish a physical model of the vehicle based on sensor data and the vehicle’s factory-preset structure and performance parameters;

[0023] (3) The data storage module loads a risk sequence fault tree based on big data from the cloud data analysis platform;

[0024] (4) The fault prediction module makes a preliminary prediction of the vehicle's faults based on the vehicle operation data collected by the sensors of the data acquisition module and the fault risk sequence number, and injects the fault events or fault sub-events of the risk sequence fault tree into the vehicle physical model.

[0025] (5) The vehicle physical model simulates the actual vehicle operating environment conditions and operates under the condition of predicted fault events or fault sub-events;

[0026] (6) The fault diagnosis module will compare and analyze the simulation results of the vehicle physical model with the actual vehicle operation data. When the error between the simulation result parameters of each component of the electromechanical composite transmission system and the actual vehicle data is less than the preset threshold, the fault diagnosis is completed. The fault can be determined as the current fault event or fault sub-event, and the fault is displayed and the fault diagnosis data is stored.

[0027] Compared with the prior art, the beneficial effects of this invention, due to the adoption of the above technical solutions, are as follows:

[0028] (1) It makes full use of the redundant historical big data information of the electromechanical composite transmission system of the same type of hybrid electric vehicle in the cloud, providing reliable data support for fault diagnosis. The FMEA analysis based on this has dynamic real-time performance, and the fault events and fault sub-events corresponding to the fault phenomena of different faults can be updated in real time.

[0029] (2) By assigning a fault evaluation method based on risk sequence number to fault events and fault sub-events under different fault phenomena, a risk sequence fault tree based on big data can be established, which can provide an effective predictive basis for fault diagnosis.

[0030] (3) The proposed fault diagnosis method can predict and inject faults in a targeted manner, and can realize accurate and efficient diagnosis of faults in electromechanical composite transmission systems. Its real-time performance is better than that of existing diagnostic technologies. Attached Figure Description

[0031] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. These drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0032] Figure 1 This is an architecture diagram of the electromechanical composite transmission system fault diagnosis system described in an embodiment of the present invention;

[0033] Figure 2This is a flowchart illustrating the establishment of a risk sequence fault tree based on big data, as described in an embodiment of the present invention.

[0034] Figure 3 This is a schematic diagram of a risk sequence fault tree based on big data as described in an embodiment of the present invention;

[0035] Figure 4 This is a flowchart of the main steps of the fault diagnosis method described in the embodiments of the present invention;

[0036] Figure 5 This is a flowchart of the fault prediction injection and diagnosis process described in an embodiment of the present invention. Detailed Implementation

[0037] To further illustrate the technical solution and specific working process of the present invention, the present invention will be further described below with reference to the accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0038] In this document, suffixes such as "module" or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module" or "unit" may be used interchangeably.

[0039] Reference Figure 1 As shown, the electromechanical composite transmission system fault diagnosis system based on risk sequence fault tree of the present invention includes a vehicle body, an on-board data unit, a fault diagnosis unit, a cloud data analysis platform, and other vehicles of the same type. The cloud data analysis platform is connected to the on-board data unit and other vehicles of the same type via a communication network; the fault diagnosis unit transmits data with the on-board data unit via a CAN bus.

[0040] The vehicle body includes a power system and an electromechanical composite transmission system;

[0041] Preferably, the power system of the vehicle body includes, but is not limited to, an engine, an electric motor, and a battery;

[0042] Preferably, the electromechanical composite transmission system includes a power coupling mechanism and a clutch; the power coupling mechanism is a double planetary gear mechanism; the double planetary gear mechanism includes a sun gear, a planet carrier, planet gears and a ring gear.

[0043] The other vehicles of the same type are hybrid electric vehicles of the same model as the vehicle body, equipped with the same power system and electromechanical composite transmission system, and the basic parameter information of the vehicles set at the factory is consistent with that of the vehicle body. The basic parameters include vehicle weight parameters and performance parameter information of each component of the power system.

[0044] The cloud-based data analysis platform includes a fault big data unit, a data analysis unit, and a risk sequence fault tree based on big data; the fault big data unit includes a fault database and an FMEA module.

[0045] Preferably, the fault database stores historical fault data of other in-use mechatronic transmission systems of the same model. This historical fault data includes losses from faults in the mechatronic transmission systems of the same model, fault diagnosis results, vehicle operating data under fault conditions, and specific fault events and sub-events that caused the fault. The FMEA module marks the fault events and sub-events corresponding to different fault phenomena in the historical fault data.

[0046] Reference Figure 2 As shown, the data analysis unit analyzes the historical big data of faults stored in the fault database, assigns a fault severity score to the fault phenomenon based on the loss situation at different fault occurrences, statistically counts fault events and fault sub-events for different fault phenomena, calculates the fault occurrence frequency score of a certain fault event and fault sub-event, counts the number of correct diagnoses and misdiagnoses of fault events and fault sub-events for different fault phenomena, calculates the fault detection difficulty score of fault events and fault sub-events, and calculates the risk order number of fault events or fault sub-events under the fault phenomenon, where the risk order number = fault severity score × fault occurrence frequency score × fault detection difficulty score.

[0047] Reference Figure 3 As shown, the risk sequence fault tree based on big data is a fault tree that expands based on the risk sequence number of fault events or fault sub-events.

[0048] The vehicle-mounted data unit includes a data acquisition module and a data storage module, as shown in the reference... Figure 4 As shown, the data acquisition module includes multiple sensors such as speed sensors and pressure sensors, which are used to collect dynamic data of the electromechanical hybrid transmission system of the hybrid vehicle in real time; the data storage module stores the basic parameter information of the vehicle set at the factory, including the basic parameters of the vehicle body and the performance parameter information of each component of the power system, and also classifies and stores the real-time detection data collected by the data acquisition module.

[0049] Preferably, the data storage module can load a risk sequence fault tree based on big data to provide a basis for fault injection for the fault prediction module;

[0050] Preferably, the data storage module can also store the diagnostic results data of the fault diagnosis module.

[0051] The fault diagnosis unit includes a vehicle physical model, a fault prediction module, a fault diagnosis module, and a fault display module.

[0052] Preferably, the vehicle physical model is a simulation physical model with consistent parameters across the vehicle itself. This parameter consistency means that the vehicle's basic parameters, powertrain performance parameters, and structural parameters of each component of the electromechanical composite transmission system are all consistent with the vehicle itself.

[0053] The fault prediction module injects faults into the vehicle physical model based on a risk sequence fault tree based on big data.

[0054] Preferably, the risk sequence number of the injected fault events or fault sub-events decreases sequentially;

[0055] The fault diagnosis module compares and analyzes the simulation results of the vehicle physical model with the actual vehicle operation data. When the error between the simulation result parameters of each component of the electromechanical composite transmission system and the actual vehicle data is less than a preset threshold, the fault diagnosis is completed and the fault can be determined as the current fault event or fault sub-event. Otherwise, the model simulation results of the next injected fault event or fault sub-event are analyzed.

[0056] The fault display module displays the fault diagnosis results to remind the driver and facilitate maintenance personnel in carrying out subsequent maintenance work.

[0057] Reference Figure 4 and Figure 5 As shown, the present invention provides a fault diagnosis method for an electromechanical composite transmission system based on a fault risk sequence. Based on the aforementioned system, the method includes the following steps:

[0058] (1) Sensors collect the actual operating data of the vehicle under actual working conditions;

[0059] (2) Establish a physical model of the vehicle based on sensor data and the vehicle’s factory-preset structure and performance parameters;

[0060] (3) The data storage module loads a risk sequence fault tree based on big data from the cloud data analysis platform;

[0061] (4) The fault prediction module makes a preliminary prediction of the faults of the vehicle and injects the fault events or fault sub-events of the risk sequence fault tree into the vehicle physical model in descending order of risk sequence value.

[0062] (5) The vehicle physical model simulates the actual vehicle operating environment conditions and operates under the condition of predicted fault events or fault sub-events;

[0063] (6) The fault diagnosis module will compare and analyze the simulation results of the vehicle physical model with the actual vehicle operation data. When the error between the simulation result parameters of each component of the electromechanical composite transmission system and the actual vehicle operation data is less than the preset threshold, the fault diagnosis is completed. The fault can be determined as the current fault event or fault sub-event, and the fault is displayed and the fault diagnosis data is stored.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fault diagnosis system for an electromechanical composite transmission system based on a risk sequence fault tree, characterized in that, include: Vehicle body, on-board data unit, fault diagnosis unit, cloud data analysis platform and other vehicles of the same type; The vehicle body is equipped with a powertrain system and an electromechanical composite transmission system; the cloud-based data analysis platform is connected to the on-board data unit and other vehicles of the same type via a communication network; the other vehicles of the same type are those equipped with the same powertrain system and electromechanical composite transmission system as the vehicle body at the factory; the on-board data unit includes a data acquisition module and a data storage module; the data acquisition module includes a speed sensor and a pressure sensor; the fault diagnosis unit transmits data with the on-board data unit via a CAN bus; the fault diagnosis unit includes a connected vehicle physical model, a fault prediction module, a fault diagnosis module, and a fault display module; the cloud-based data analysis platform includes a fault big data unit, a data analysis unit, and a risk sequence fault tree based on big data. The fault big data unit includes a fault database and an FMEA module; The fault big data unit includes a fault database and an FMEA module; The fault database stores historical big data on faults in the mechatronic transmission systems of other vehicles of the same model. The historical big data on faults includes the loss situation, fault diagnosis results, vehicle operation data under fault conditions, and specific fault events or sub-events that caused the fault when the mechatronic transmission system of the same model fails. The FMEA module analyzes the historical big data on faults and marks the fault phenomena of different faults and their corresponding fault events and sub-events. The data analysis unit analyzes the historical big data of faults stored in the fault database, analyzes the loss situation when different faults occur, assigns a fault impact severity score to the fault phenomenon, and counts the fault events and fault sub-events of different fault phenomena, and calculates the fault occurrence frequency score of a certain fault event and fault sub-event. Analyze the fault diagnosis results, count the number of correct and false diagnoses of fault events and fault sub-events for different fault phenomena, and calculate the fault detection difficulty score of fault events and fault sub-events. Calculate the risk order number of the fault event or fault sub-event under the fault phenomenon, wherein the risk order number = fault impact severity score × fault occurrence frequency score × fault detection difficulty score; The risk sequence fault tree based on big data is a fault tree of an electromechanical composite transmission system that is built according to the risk sequence number of fault events or fault sub-events after statistical analysis of historical fault big data by the data analysis unit. The vehicle physical model is a simulation physical model with consistent parameters of the vehicle body; the parameters include basic vehicle parameters, power system performance parameters, and structural parameters of each component of the electromechanical composite transmission system; The fault prediction module predicts faults based on a risk sequence fault tree of big data and injects faults into the vehicle physical model according to the risk sequence number of the fault. The fault diagnosis module compares and analyzes the difference between the vehicle physical model simulation results and the actual vehicle operation data with a preset threshold to achieve fault diagnosis; the fault display module displays the fault diagnosis results to remind the driver and facilitate maintenance personnel to carry out subsequent maintenance work.

2. The system according to claim 1, characterized in that, The data acquisition module includes multiple detection sensors for real-time acquisition of dynamic data of the electromechanical hybrid transmission system of the hybrid vehicle; the detection sensors include speed sensors and pressure sensors.

3. The system according to claim 1, characterized in that, The data storage module stores the vehicle's factory-preset basic parameter information, including the vehicle's mass parameters, structural parameters, and performance parameters of various power system components; the data storage module stores real-time detection data collected by the data acquisition module and classifies and stores the collected data; the data storage module can load risk sequence fault trees based on big data. The data storage module can also store diagnostic data from the fault diagnosis module.

4. The system according to claim 1, characterized in that, The other vehicles of the same type are hybrid electric vehicles of the same model as the vehicle body, equipped with the same power system and electromechanical composite transmission system, and the basic parameter information of the vehicles set at the factory is consistent with that of the vehicle body. The basic parameters include vehicle weight parameters and performance parameter information of each component of the power system.

5. A diagnostic method for a fault diagnosis system of an electromechanical composite transmission system based on a risk sequence fault tree according to any one of claims 1-4, characterized in that, Includes the following steps: (1) Sensors collect the actual operating data of the vehicle under actual working conditions; (2) Establish a physical model of the vehicle based on the sensor data and the vehicle's factory-preset structure and performance parameters; (3) The data storage module loads a risk sequence fault tree based on big data from the cloud data analysis platform; (4) The fault prediction module makes a preliminary prediction of the vehicle's faults based on the vehicle operation data collected by the sensors of the data acquisition module and the fault risk sequence number, and injects the fault events or fault sub-events of the risk sequence fault tree into the vehicle physical model. (5) The vehicle physical model simulates the actual vehicle operating environment conditions and operates under the condition of predicted fault events or fault sub-events; (6) The fault diagnosis module will compare and analyze the simulation results of the vehicle physical model with the actual vehicle operation data. When the error between the simulation result parameters of each component of the electromechanical composite transmission system and the actual vehicle data is less than the preset threshold, the fault diagnosis is completed. The fault can be determined as the current fault event or fault sub-event, and the fault is displayed and the fault diagnosis data is stored.

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