A method and system for failure prediction of an IGBT module

Abstract

The application discloses a kind of IGBT module's failure prediction method and system, including IGBT module, the input end of the IGBT module is electrically connected with operating monitoring system bidirectionally, and the output end of the operating monitoring system is electrically connected with control circuit bidirectionally.The application is collected by contact thermocouple, thermistor, infrared thermal imaging camera, diffusion coefficient monitoring module, mobility monitoring module, carrier concentration monitoring module, short-circuit test module and Qg test module to IGBT module internal data and is transferred to control circuit after integration by integrated collection module, terminal processor processes data and transmits it to IGBT module operating prediction system to predict and judge, can improve the monitoring mode of existing IGBT module, can monitor and predict the existing and future state of IGBT module, user can timely master the unexpected dangerous condition of IGBT module in running process, avoid the safety of IGBT module is influenced.

Description

Technical Field

[0001] This invention relates to the field of IGBT module monitoring technology, specifically to a fault prediction method and system for IGBT modules. Background Technology

[0002] IGBT (Insulated Gate Bipolar Transistor) is a composite fully controllable voltage-driven power semiconductor device composed of BJT (Browser Joint Transistor) and MOSFET (Metal-Oxide-Semiconductor). It combines the advantages of MOSFET's high input impedance and GTR's low on-state voltage drop. GTR has a low saturation voltage drop and high current density, but a large drive current. MOSFET has very low drive power and fast switching speed, but a large on-state voltage drop and low current density. IGBT combines the advantages of both devices, with low drive power and low saturation voltage drop. IGBT is very suitable for use in converter systems with DC voltage of 600V and above, such as AC motors, frequency converters, switching power supplies, lighting circuits, traction drives, and other fields.

[0003] To improve the operational safety of IGBT modules, a monitoring system is needed to monitor them. For example, patent number 201610537013.1 published on the China Patent Network, entitled "Online Monitoring Method for IGBT Modules," includes an IGBT module under test and at least one test IGBT module. The IGBT module under test and the test IGBT module form a bridge circuit. The aging state of the IGBT module under test is tested according to the following method: a short-circuit drive voltage is input to the IGBT module under test and it is controlled to operate in a current saturation state. A working drive voltage is input to the test IGBT module to enable it to operate normally. The short-circuit current value of the IGBT module under test in the current saturation state is detected. The aging state of the IGBT module under test is determined based on the short-circuit current value. This method can accurately monitor the aging state of the IGBT module without stopping the machine or disassembling it, thereby accurately determining the aging state of the IGBT module and effectively avoiding economic losses caused by downtime testing and damage caused by disassembling the IGBT module that affects measurement accuracy.

[0004] However, the existing monitoring methods for IGBT modules are relatively simple, only able to monitor the current state of the IGBT module. Users cannot promptly grasp unexpected dangerous situations that occur during the operation of the IGBT module, which affects the safety of the IGBT module.

[0005] Therefore, it is necessary to design and modify the fault prediction method and system for IGBT modules. Summary of the Invention

[0006] To address the problems mentioned in the background section, the present invention aims to provide a fault prediction method and system for IGBT modules. This method and system offer the advantage of predictive monitoring of IGBT modules and solve the problem that existing IGBT module monitoring methods are relatively simple, only able to monitor the current state of the IGBT module. Users cannot promptly grasp unexpected dangerous situations that occur during the operation of the IGBT module, which affects the safety of the IGBT module.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a fault prediction method and system for an IGBT module, comprising an IGBT module;

[0008] The input terminal of the IGBT module is bidirectionally electrically connected to an operation monitoring system, and the output terminal of the operation monitoring system is bidirectionally electrically connected to a control circuit. The input terminal of the IGBT module is bidirectionally electrically connected to a thermal monitoring module, and the output terminal of the thermal monitoring module is bidirectionally electrically connected to an integrated collection module. The output terminal of the integrated collection module is bidirectionally electrically connected to the input terminal of the control circuit. The thermal monitoring module consists of a contact thermocouple, a thermistor, and an infrared thermal imaging camera. The output terminal of the control circuit is bidirectionally electrically connected to a terminal processor, and the output terminal of the terminal processor is bidirectionally electrically connected to an IGBT module operation prediction system. The output terminal of the terminal processor is bidirectionally electrically connected to a classification and storage module.

[0009] As a preferred embodiment of the present invention, the IGBT module operation prediction system includes a combined prediction model, the input of which is bidirectionally electrically connected to the output of the terminal processor, the output of which is bidirectionally electrically connected to a comparison module, the input of which is bidirectionally electrically connected to a long short-term memory network, and the input of which is bidirectionally electrically connected to the output of a classification storage module.

[0010] As a preferred embodiment of the present invention, the output terminal of the comparison module is bidirectionally electrically connected to a prediction and evaluation module, and the output terminal of the prediction and evaluation module is bidirectionally electrically connected to a feedback module.

[0011] In a preferred embodiment of the present invention, the output terminal of the feedback module is electrically connected to the input terminal of the terminal processor, the output terminal of the feedback module is bidirectionally electrically connected to a wireless transceiver antenna, and the output terminal of the wireless transceiver antenna is remotely bidirectionally electrically connected to a remote monitoring platform.

[0012] As a preferred embodiment of the present invention, the combined prediction model consists of a linear regression model, a grey prediction model, and a BP neural network prediction model.

[0013] As a preferred embodiment of the present invention, the output terminal of the control circuit is electrically connected to a protective device, the output terminal of which is electrically connected to the input terminal of the IGBT module, and the protective device consists of an intelligent fuse and a remote control circuit breaker.

[0014] As a preferred embodiment of the present invention, the operation monitoring system comprises a diffusion coefficient monitoring module, a mobility monitoring module, a carrier concentration monitoring module, a short-circuit test module, and a Qg test module.

[0015] A method and system for fault prediction of an IGBT module, comprising the following steps:

[0016] S1: During the operation of the IGBT module, contact thermocouples, thermistors, infrared thermal imaging cameras, diffusion coefficient monitoring modules, mobility monitoring modules, carrier concentration monitoring modules, short-circuit test modules, and Qg test modules can collect internal data of the IGBT module and transmit it to the control circuit after integration by the integration collection module.

[0017] S2: The terminal processor processes the data and transmits it to the IGBT module to run the prediction system. The linear regression model can use regression analysis in mathematical statistics to determine the statistical data of the quantitative relationship between two or more variables. The grey prediction model can build a mathematical model to make predictions based on the small amount and incomplete information collected by the IGBT module. Based on the past and present development laws of objective things, it describes and analyzes the future development trend and status of the IGBT module with the help of data stored in the long short-term memory network and scientific methods, and forms scientific hypotheses and judgments.

[0018] S3: The comparison module compares the predicted data. When an error occurs in the data, the feedback module sends an alarm signal to the remote monitoring platform through the wireless transceiver antenna. At the same time, the feedback module can use the control circuit to control the protection device to turn on, preventing damage to the IGBT module.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0020] 1. This invention collects internal data of the IGBT module using contact thermocouples, thermistors, infrared thermal imaging cameras, diffusion coefficient monitoring modules, mobility monitoring modules, carrier concentration monitoring modules, short-circuit test modules, and Qg test modules. This data is then integrated using an integrated collection module and transmitted to the control circuit. The terminal processor processes the data and transmits it to the IGBT module operation prediction system for prediction and judgment. This improves existing IGBT module monitoring methods, enabling the monitoring and prediction of the current and future states of the IGBT module. Users can promptly grasp unexpected dangerous situations that may occur during IGBT module operation, preventing the safety of the IGBT module from being compromised.

[0021] 2. By setting up a combined prediction model and a comparison module, this invention can predict the data status, enabling users to monitor the operation of IGBT modules and detect module failures early.

[0022] 3. By setting up a prediction evaluation module and a feedback module, this invention can judge the prediction results, avoid prediction errors, and improve detection accuracy.

[0023] 4. By setting up a wireless transceiver antenna and a remote monitoring platform, this invention can provide prompts to remote monitoring users, making it easier for users to centrally monitor the operating status of multiple IGBT modules.

[0024] 5. This invention, by setting up a linear regression model, a grey prediction model, and a BP neural network prediction model, can use regression analysis in mathematical statistics to determine the statistical data of the quantitative relationship between two or more variables. The grey prediction model can build a mathematical model to make predictions based on the limited and incomplete information collected from the IGBT module. Based on the past and present development laws of objective things, and with the help of the data stored in the long short-term memory network and scientific methods, it describes and analyzes the future development trend and status of the IGBT module, and forms scientific hypotheses and judgments.

[0025] 6. By incorporating protective electrical components, this invention can protect the IGBT module and prevent it from being damaged due to accidents.

[0026] 7. This invention, by setting up a diffusion coefficient monitoring module, a mobility monitoring module, a carrier concentration monitoring module, a short-circuit test module, and a Qg test module, can monitor the IGBT module, improve the data acquisition range, determine whether the FWD chip is normal, and determine the short-circuit status of CE, GE, and GC. At the same time, the carrier concentration, mobility, and diffusion coefficient of the IGBT power module are all affected by temperature. Contact thermocouples, thermistors, and infrared thermal imaging cameras can collect and judge the static parameters in the on or off state and the dynamic parameters at the moment of on or off. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] like Figure 1 As shown, the present invention provides a fault prediction method and system for an IGBT module, including an IGBT module;

[0030] The input of the IGBT module is bidirectionally electrically connected to an operation monitoring system, and the output of the operation monitoring system is bidirectionally electrically connected to a control circuit. The input of the IGBT module is bidirectionally electrically connected to a thermal monitoring module, and the output of the thermal monitoring module is bidirectionally electrically connected to an integrated collection module. The output of the integrated collection module is bidirectionally electrically connected to the input of the control circuit. The thermal monitoring module consists of a contact thermocouple, a thermistor, and an infrared thermal imaging camera. The output of the control circuit is bidirectionally electrically connected to a terminal processor, and the output of the terminal processor is bidirectionally electrically connected to an IGBT module operation prediction system. The output of the terminal processor is bidirectionally electrically connected to a classification and storage module.

[0031] refer to Figure 1 The IGBT module operation prediction system includes a combined prediction model. The input of the combined prediction model is bidirectionally electrically connected to the output of the terminal processor. The output of the combined prediction model is bidirectionally electrically connected to a comparison module. The input of the comparison module is bidirectionally electrically connected to a long short-term memory network. The input of the long short-term memory network is bidirectionally electrically connected to the output of the classification storage module.

[0032] As a technical optimization of the present invention, by setting up a combined prediction model and a comparison module, the data status can be predicted, enabling users to monitor the operating status of the IGBT module and detect module failures early.

[0033] refer to Figure 1 The output of the comparison module is bidirectionally electrically connected to the prediction and evaluation module, and the output of the prediction and evaluation module is bidirectionally electrically connected to the feedback module.

[0034] As a technical optimization of the present invention, by setting up a prediction evaluation module and a feedback module, the prediction results can be judged, prediction errors can be avoided, and detection accuracy can be improved.

[0035] refer to Figure 1 The output of the feedback module is electrically connected to the input of the terminal processor. The output of the feedback module is bidirectionally electrically connected to a wireless transceiver antenna. The output of the wireless transceiver antenna is remotely bidirectionally electrically connected to a remote monitoring platform.

[0036] As a technical optimization of the present invention, by setting up a wireless transceiver antenna and a remote monitoring platform, prompts can be given to remote monitoring users, making it easier for users to centrally grasp the operating status of multiple IGBT modules.

[0037] refer to Figure 1 The combined prediction model consists of a linear regression model, a grey prediction model, and a BP neural network prediction model.

[0038] As a technical optimization scheme of the present invention, by setting up a linear regression model, a grey prediction model and a BP neural network prediction model, it is possible to use regression analysis in mathematical statistics to determine the statistical data of the quantitative relationship between two or more variables. The grey prediction model can establish a mathematical model to make predictions based on the small amount and incomplete information collected from the IGBT module. Based on the past and present development laws of objective things, it describes and analyzes the future development trend and status of the IGBT module with the help of data stored in the long short-term memory network and scientific methods, and forms scientific hypotheses and judgments.

[0039] refer to Figure 1 The output of the control circuit is electrically connected to a protective device, and the output of the protective device is electrically connected to the input of the IGBT module. The protective device consists of an intelligent fuse and a remote control circuit breaker.

[0040] As a technical optimization of the present invention, by setting up protective electrical devices, the IGBT module can be protected to prevent the IGBT module from being damaged due to accidents.

[0041] refer to Figure 1 The operation monitoring system consists of a diffusion coefficient monitoring module, a mobility monitoring module, a carrier concentration monitoring module, a short-circuit test module, and a Qg test module.

[0042] As a technical optimization of the present invention, by setting up a diffusion coefficient monitoring module, a mobility monitoring module, a carrier concentration monitoring module, a short-circuit test module, and a Qg test module, the IGBT module can be monitored, which can improve the data acquisition range, determine whether the FWD chip is normal, and determine the short-circuit status of CE, GE, and GC. At the same time, the carrier concentration, mobility, and diffusion coefficient of the IGBT power module are all affected by temperature. Contact thermocouples, thermistors, and infrared thermal imaging cameras can collect and judge the static parameters in the on or off state and the dynamic parameters at the moment of on or off.

[0043] refer to Figure 1 A method and system for predicting faults in an IGBT module, comprising the following steps:

[0044] S1: During the operation of the IGBT module, contact thermocouples, thermistors, infrared thermal imaging cameras, diffusion coefficient monitoring modules, mobility monitoring modules, carrier concentration monitoring modules, short-circuit test modules, and Qg test modules can collect internal data of the IGBT module and transmit it to the control circuit after integration by the integration collection module.

[0045] S2: The terminal processor processes the data and transmits it to the IGBT module to run the prediction system. The linear regression model can use regression analysis in mathematical statistics to determine the statistical data of the quantitative relationship between two or more variables. The grey prediction model can build a mathematical model to make predictions based on the small amount and incomplete information collected by the IGBT module. Based on the past and present development laws of objective things, it describes and analyzes the future development trend and status of the IGBT module with the help of data stored in the long short-term memory network and scientific methods, and forms scientific hypotheses and judgments.

[0046] S3: The comparison module compares the predicted data. When an error occurs in the data, the feedback module sends an alarm signal to the remote monitoring platform through the wireless transceiver antenna. At the same time, the feedback module can use the control circuit to control the protection device to turn on, preventing damage to the IGBT module.

[0047] The working principle and usage process of this invention: During the operation of the IGBT module, contact thermocouples, thermistors, infrared thermal imaging cameras, diffusion coefficient monitoring modules, mobility monitoring modules, carrier concentration monitoring modules, short-circuit test modules, and Qg test modules can collect internal data of the IGBT module. After integration by the collection module, the data is transmitted to the control circuit. The terminal processor processes the data and transmits it to the IGBT module operation prediction system. The linear regression model can use regression analysis in mathematical statistics to determine the statistical data of the quantitative relationship between two or more variables. The grey prediction model can build a mathematical model to make predictions based on the small amount and incomplete information collected by the IGBT module. Based on the past and present development laws of objective things, and with the help of data stored in the long short-term memory network and scientific methods, the future development trend and status of the IGBT module are described and analyzed, and scientific hypotheses and judgments are formed. The comparison module compares the predicted data. When the data has errors, the feedback module sends an alarm signal to the remote monitoring platform through the wireless transceiver antenna. At the same time, the feedback module can use the control circuit to control the protection electrical appliances to turn on, preventing damage to the IGBT module.

[0048] In summary, the fault prediction method and system for this IGBT module collects internal data from the IGBT module using contact thermocouples, thermistors, infrared thermal imaging cameras, diffusion coefficient monitoring modules, mobility monitoring modules, carrier concentration monitoring modules, short-circuit test modules, and Qg test modules. This data is then integrated by an integration module and transmitted to the control circuit. The terminal processor processes the data and transmits it to the IGBT module operation prediction system for prediction and judgment. This method improves existing IGBT module monitoring methods, enabling the monitoring and prediction of the current and future states of the IGBT module. Users can promptly grasp unexpected dangerous situations that may occur during IGBT module operation, preventing compromises to the IGBT module's safety.

[0049] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A fault prediction system for an IGBT module, comprising an IGBT module; Its features are: The input terminal of the IGBT module is bidirectionally electrically connected to an operation monitoring system. The output terminal of the operation monitoring system is bidirectionally electrically connected to a control circuit. The input terminal of the IGBT module is bidirectionally electrically connected to a thermal monitoring module. The output terminal of the thermal monitoring module is bidirectionally electrically connected to an integrated collection module. The output terminal of the integrated collection module is bidirectionally electrically connected to the input terminal of the control circuit. The thermal monitoring module consists of a contact thermocouple, a thermistor, and an infrared thermal imaging camera. The output terminal of the control circuit is bidirectionally electrically connected to a terminal processor. The output terminal of the terminal processor is bidirectionally electrically connected to an IGBT module operation prediction system. The output terminal of the terminal processor is bidirectionally electrically connected to a classification and storage module. The IGBT module operation... The prediction system includes a combined prediction model. The input of the combined prediction model is bidirectionally electrically connected to the output of a terminal processor. The output of the combined prediction model is bidirectionally electrically connected to a comparison module. The input of the comparison module is bidirectionally electrically connected to a long short-term memory network. The input of the long short-term memory network is bidirectionally electrically connected to the output of a classification storage module. The output of the comparison module is bidirectionally electrically connected to a prediction evaluation module. The output of the prediction evaluation module is bidirectionally electrically connected to a feedback module. The output of the feedback module is electrically connected to the input of the terminal processor. The output of the feedback module is bidirectionally electrically connected to a wireless transceiver antenna. The output of the wireless transceiver antenna is remotely bidirectionally electrically connected to a remote monitoring platform.

2. The system for failure prediction of an IGBT module according to claim 1, characterized in that: The combined prediction model consists of a linear regression model, a grey prediction model, and a BP neural network prediction model.

3. The system for failure prediction of an IGBT module according to claim 1, characterized in that: The output terminal of the control circuit is electrically connected to a protective device, the output terminal of which is electrically connected to the input terminal of the IGBT module. The protective device consists of an intelligent fuse and a remote control circuit breaker.

4. The system for failure prediction of an IGBT module according to claim 1, characterized in that: The operation monitoring system consists of a diffusion coefficient monitoring module, a mobility monitoring module, a carrier concentration monitoring module, a short-circuit test module, and a Qg test module.

5. A method of fault prediction for an IGBT module as claimed in any one of the preceding claims, characterized in that: Includes the following steps: S1: During the operation of the IGBT module, contact thermocouples, thermistors, infrared thermal imaging cameras, diffusion coefficient monitoring modules, mobility monitoring modules, carrier concentration monitoring modules, short-circuit test modules, and Qg test modules can collect internal data of the IGBT module and transmit it to the control circuit after integration by the integration collection module. S2: The terminal processor processes the data and transmits it to the IGBT module to run the prediction system. The linear regression model can use regression analysis in mathematical statistics to determine the statistical data of the quantitative relationship between two or more variables. The grey prediction model can build a mathematical model to make predictions based on the small amount and incomplete information collected by the IGBT module. Based on the past and present development laws of objective things, it describes and analyzes the future development trend and status of the IGBT module with the help of data stored in the long short-term memory network and scientific methods, and forms scientific hypotheses and judgments. S3: The comparison module compares the predicted data. When an error occurs in the data, the feedback module sends an alarm signal to the remote monitoring platform through the wireless transceiver antenna. At the same time, the feedback module can use the control circuit to control the protection device to turn on, preventing damage to the IGBT module.

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