CN114002517A

CN114002517A - Device diagnosis method, platform, system and readable storage medium

Info

Publication number: CN114002517A
Application number: CN202010736161.2A
Authority: CN
Inventors: 陈世静; 胡宗权
Original assignee: BYD Co Ltd
Current assignee: BYD Co Ltd
Priority date: 2020-07-28
Filing date: 2020-07-28
Publication date: 2022-02-01

Abstract

The invention discloses a device diagnosis method, a device, a platform and a readable storage medium, which aim to solve the problem that the device diagnosis of equipment is not timely, so that the equipment has safety risks. The method comprises the following steps: acquiring health state evaluation parameters of devices of the same type of equipment in real time, wherein the health state evaluation parameters are associated with the current state of the devices; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices, wherein the suspicious parameters are health state evaluation parameters with preset errors with other health state evaluation parameters; if the suspicious parameters exist, the device corresponding to the suspicious parameters is diagnosed to be in an unhealthy state.

Description

Device diagnosis method, platform, system and readable storage medium

Technical Field

The present invention relates to the field of device diagnosis technologies of devices, and in particular, to a device diagnosis method, apparatus, platform, and readable storage medium.

Background

In electronic equipment or systems (hereinafter referred to as equipment), the health status of devices is extremely important, and the effectiveness, stability and reliability of the functions of each device in the equipment are closely related to the performance of the equipment. In order to ensure the safety and reliability of the equipment, the performance of the device of the equipment needs to be monitored, if the performance is unqualified, the device needs to be replaced in time so as to ensure the reliable operation of the equipment, wherein the service life index of the device is an important parameter index for weighing the performance of the device. For example, the device may refer to a contactor of a power battery high voltage circuit on an electric vehicle. It can be known that the power battery high-voltage circuit is normally powered on and powered off through contactors, and the contactors of the high-voltage circuit generally include contactors such as a positive contactor and a negative contactor, and thus, the performance of the contactors is closely related to the performance of the whole vehicle.

In the prior art, methods for diagnosing the service life of a device mainly comprise: at present, parameters such as resistance at a device end, voltage difference of the device, temperature and the like are generally obtained when equipment is maintained and checked regularly, so that the health state of the device, such as an automobile contactor, is diagnosed, the parameters of the contactor need to be obtained when a vehicle is maintained and checked regularly, the service life of the contactor cannot be monitored in real time, the probability of single detection data inaccuracy is high, and therefore the device diagnosis method in the prior art is not real-time enough, and therefore certain safety risks exist in the equipment.

Disclosure of Invention

The invention provides a device diagnosis method, a platform, a system and a readable storage medium, which aim to solve the problem that in the prior art, the device diagnosis method is not real-time enough, so that equipment has certain safety risk.

In view of this, a first aspect of the present invention provides a device diagnostic method, including:

acquiring health state evaluation parameters of devices of the same type of equipment in real time, wherein the health state evaluation parameters are associated with the current state of the devices;

determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices, wherein the suspicious parameters are health state evaluation parameters with preset errors with other health state evaluation parameters;

if the suspicious parameters exist, the device corresponding to the suspicious parameters is diagnosed to be in an unhealthy state.

Further, determining whether there is a suspect parameter in all the health assessment parameters for the same class of devices includes:

determining a first mean value and a first standard deviation corresponding to all the health state evaluation parameters;

and determining whether the health state evaluation parameters of all the devices in the same class are suspicious parameters according to the first mean value and the first standard deviation corresponding to all the health state evaluation parameters.

Further, determining whether the health state evaluation parameters of each device in the same type of device are suspicious parameters according to the first mean value and the first standard deviation corresponding to all the health state evaluation parameters, including:

determining the absolute value of the difference between the health state evaluation parameter of each device in the same class of devices and the first mean value to obtain a first absolute error corresponding to each device;

determining the ratio of the first absolute error corresponding to each device to the first standard deviation;

determining a comparison coefficient according to the quantity of all the health state evaluation parameters, wherein the comparison coefficient and the quantity are in positive correlation;

and if the ratio of the first absolute error corresponding to the target device to the first standard deviation in each device is greater than or equal to the comparison coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter.

determining the difference value between the health state evaluation parameter of each device in the same type of device and the first mean value to obtain a target error value corresponding to each device;

determining the ratio of the target error value corresponding to each device to the first standard deviation;

and if the ratio of the target error value corresponding to the target device to the first standard deviation in each device is greater than or equal to the Grabbs critical value, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter, wherein the Grabbs critical value is related to the preset significance level coefficient and the number of all the health state evaluation parameters.

determining a second mean value and a second standard deviation corresponding to the remaining health state evaluation parameters in all the health state evaluation parameters, wherein the remaining health state evaluation parameters are all the health state evaluation parameters except the health state evaluation parameters corresponding to the target device in all the health state evaluation parameters;

determining the absolute value of the difference value between the health state evaluation parameter corresponding to the target device and the second mean value to obtain a second absolute error corresponding to the target device;

determining a ratio of a second absolute error corresponding to the target device to the second standard deviation;

and if the ratio of the second absolute error corresponding to the target device to the second standard deviation is greater than or equal to the Romanofsky check coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter, wherein the Romanofsky check coefficient is related to the preset significance level coefficient and the number of all the health state evaluation parameters.

a. determining extreme values according to the sizes of all the health state evaluation parameters, wherein the extreme values comprise a maximum evaluation parameter and a minimum evaluation parameter;

b. determining a statistical value corresponding to the extreme size value according to a Dixon statistical formula corresponding to the extreme size value;

c. determining whether the statistical value is greater than or equal to a Dike's critical value corresponding to the statistical value, wherein the Dike's critical value is related to a preset significance level coefficient and the number of all health state evaluation parameters;

d. if the statistical value is larger than or equal to the Dike critical value corresponding to the statistical value, determining the health state evaluation parameter corresponding to the extreme size value as a suspicious parameter, and removing the extreme size value from all the health state evaluation parameters;

e. and repeating the steps a-e on the remaining health state evaluation parameters after the extreme value is removed until all suspicious parameters are determined.

Further, if there is no suspicious parameter, the method further comprises:

comparing each health status assessment parameter of all health status assessment parameters with a corresponding limit parameter;

and if the health state evaluation parameter is larger than the corresponding limit parameter, determining that the device corresponding to the health state evaluation parameter is in an unhealthy state.

determining the number of all health state evaluation parameters of the same type of device;

determining a corresponding suspicious parameter diagnosis mode according to the quantity;

and diagnosing whether suspicious parameters exist in all the health state evaluation parameters according to the corresponding suspicious parameter diagnosis mode.

selecting more than two suspicious parameter diagnosis modes to determine whether suspicious parameters exist in all health state evaluation parameters of the same type of devices;

if the target health state evaluation parameter is determined to be a suspicious parameter by more than two suspicious parameter diagnosis modes, determining the target health state evaluation parameter to be a suspicious parameter;

and if the diagnosis results of more than two suspicious parameter diagnosis modes are inconsistent, acquiring the health state evaluation parameters of the devices of the same type again, and diagnosing the suspicious parameters again.

Further, the health state evaluation parameter is a temperature of the device, a resistance value across the device, or a voltage difference across the device.

Further, all the health state evaluation parameters of the same type of device are the health state evaluation parameters acquired at the same time.

A second aspect of the invention provides a device diagnostic platform comprising:

the acquisition module is used for acquiring the health state evaluation parameters of the same type of devices of the same type of equipment in real time, and the health state evaluation parameters are associated with the current state of the devices;

the determining module is used for determining whether suspicious parameters exist in all the health state evaluation parameters of the same type of devices, and the suspicious parameters are parameters with preset errors with other parameters of all the health state evaluation parameters;

and the diagnosis module is used for diagnosing that the device corresponding to the suspicious parameters is in an unhealthy state if the suspicious parameters exist.

In a third aspect, the invention provides a device diagnostic system comprising a plurality of homogeneous devices and a device diagnostic platform;

the equipment comprises a plurality of devices of the same type, a device diagnosis platform and a control system, wherein the devices of the same type are used for uploading health state evaluation parameters of devices of the same type to the device diagnosis platform in real time;

a device diagnostic platform for implementing a device diagnostic method according to any one of the preceding first aspects, or for implementing the functionality of a device diagnostic platform according to the preceding second aspect.

A fourth aspect of the present invention provides a readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the device diagnosis method according to any one of the first aspect or implements the functions of the device diagnosis platform according to the second aspect.

According to the technical scheme, the invention has the following advantages:

the invention provides a device diagnosis method, which comprises the steps of firstly obtaining health state evaluation parameters of devices of the same type of equipment in real time, wherein the health state evaluation parameters are associated with the current state of the devices; determining the health state evaluation parameters of the same type of devices according to the health state evaluation parameters of each device; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices, wherein the suspicious parameters are health state evaluation parameters with preset errors with other health state evaluation parameters; and if the suspicious parameters exist, determining that the devices corresponding to the suspicious parameters are in unhealthy states. Therefore, the embodiment of the invention can acquire the health state evaluation parameters of the same type of devices of different types of equipment in real time to determine the health state of each device, so that the service life of each device can be diagnosed, the health state of each device is not judged when the vehicle is maintained and checked regularly, the service life of each device can be monitored in real time, the problem of inaccurate data caused by single detection during regular maintenance can be effectively reduced, and the safety of the equipment is improved.

Drawings

FIG. 1 is a schematic diagram of a system architecture of a device diagnostic system according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a high-voltage distribution of a whole electric vehicle according to an embodiment of the invention;

FIG. 3 is a schematic diagram of another system architecture of the device diagnostic system in an embodiment of the present invention;

FIG. 4 is a schematic diagram of the current and terminal voltage acquisition of the main contact of the contactor under test according to the embodiment of the present invention;

FIG. 5 is a schematic flow chart of a device diagnostic method in an embodiment of the invention;

FIG. 6 is a schematic diagram of the numbering of the contactor matrix in the embodiment of the present invention;

FIG. 7 is a schematic diagram of a device diagnostic platform according to an embodiment of the present invention;

fig. 8 is another structural schematic diagram of the device diagnosis platform in the embodiment of the invention.

Detailed Description

The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged under appropriate circumstances in order to facilitate the description of the embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The invention provides a device diagnosis system and a device diagnosis method based on the device diagnosis system.

As shown in fig. 1, the present invention provides a device diagnostic system, which includes a device diagnostic platform and a plurality of apparatuses of the same type, wherein the device diagnostic platform is connected to each apparatus, for example, the apparatuses of the same type include an apparatus 1, an apparatus 2, an apparatus m, and each apparatus of the plurality of apparatuses of the same type can feed back a health state evaluation parameter of the same type to the device diagnostic platform, so that the device diagnostic platform obtains a health state of the device of each apparatus according to the health state evaluation parameter of the same type of device. It should be noted that the same type of equipment refers to the same batch type or the same model of equipment, in an application scenario, the device diagnosis platform may be a big data platform, a plurality of the same type of equipment refers to the same batch type or the same model of vehicles, and each vehicle may feed back the related information of the same type of device to the big data platform, so that the big data platform obtains the health state of the device of each vehicle according to the health state evaluation parameters of the same type of device. The big data platform has strong data processing capacity, and the health state of the devices of each device can be diagnosed through the big data platform, so that the processing efficiency and the real-time performance can be improved.

It should be noted that the device diagnosis system provided by the present invention can be applied to various industries or applications, and the device generally refers to various electronic devices or systems (herein, collectively referred to as devices), and the device generally refers to various devices on the devices. The device diagnosis system provided by the invention can be used for diagnosing related devices on other equipment besides the related devices on the automobile, for example, the device diagnosis system can also be applied to diagnosis of related devices on high-voltage electrical equipment and diagnosis of related devices on a battery energy storage system, and particularly can be used for diagnosing contactors of equipment such as high-voltage electrical equipment, a battery energy storage system and the like. It should be noted that the above-mentioned device is a contactor, which is also only an example, and the device may also be a device such as a pre-charge resistor, a fuse, and the like on a device, for example, the device may refer to a device such as a pre-charge resistor, a fuse, and the like of an automobile high-voltage module, and may also refer to a device such as a pre-charge resistor, a fuse, and the like on other devices, which is not specifically limited and will not be described any more.

The device diagnosis platform is used for acquiring the health state evaluation parameters of the devices uploaded by the devices in real time, the health state evaluation parameters are associated with the current health states of the devices, and the device diagnosis platform can determine the health state evaluation parameters of the devices of the same type according to the health state evaluation parameters of the devices; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices, wherein the suspicious parameters are parameters with preset errors with other parameters of all the health state evaluation parameters; if the suspicious parameter exists, the suspicious parameter is indicated to be more abrupt or abnormal relative to other parameters, so that the device corresponding to the suspicious parameter is determined to be in an unhealthy state, otherwise, the device is determined to be in a healthy state, and the follow-up user can conveniently confirm again. The health state evaluation parameter may refer to a temperature of the device, a resistance value at two ends, or a voltage difference at two ends, and of course, the health state evaluation parameter may refer to other parameters that may reflect a monitoring state of the device, and the present invention is not limited thereto.

For convenience of understanding, the device diagnosis system in the present invention will be described by taking an application scenario in which the device is an electric vehicle and the device is a contactor on the electric vehicle as an example. It will be appreciated that in order to perform certain functions on an electric vehicle, the electric vehicle is typically deployed with a plurality of different types of contactors for use in connection with control of the power cell high voltage circuit system or other circuitry. For example, taking a power battery high-voltage loop system on an electric vehicle as an example, as shown in fig. 2, fig. 2 is a schematic diagram of a whole electric vehicle high-voltage distribution, and includes a battery pack, a hall sensor, a fuse, a maintenance switch, a power battery, an insulation monitoring module (IMD) connected to two ends of the power battery, a diode, a thermal heater (PTC), an electrical device, and contactors. The electric appliance comprises a direct current charging port, an upper assembly, a driving module, a vehicle-mounted charger, a steering module, a DC-DC conversion module and an air conditioner compressor; each of the contactors includes a voltage dividing contactor 1, a PTC contactor 2, a dc charging port positive contactor 3, a dc charging port negative contactor 4, an upper charging contactor 5, a main pre-charging contactor 6, a discharging main contactor 7, an auxiliary pre-charging contactor 8, an ac slow charging contactor 9, and an auxiliary contactor 10. The battery pack is used for providing power for the whole vehicle; the IMD is connected with two poles of the battery pack and is used for monitoring the insulation state of the battery pack body; the voltage division contactor 1 is connected with the negative electrode of the battery pack and controls the up and down electricity of the high-voltage loop; one end of the Hall sensor is connected with the high-voltage main loop and used for monitoring the total current of the high-voltage loop; the maintenance switch plays a role of high-voltage interlocking; the fuse is connected with the main loop and each branch circuit and is used for protecting the overcurrent of the circuit; each contactor is connected between the main loop and the electrical appliance and is responsible for controlling the power on and off of each branch electrical appliance. The connection relationship between the devices of the power battery high-voltage circuit of the example is specifically shown in fig. 2, and the specific connection relationship is not described here. It should be noted that the circuit shown in fig. 2 is only an example in the embodiment of the present invention, and does not limit the embodiment of the present invention.

Therefore, the effectiveness, stability and reliability of the functions of the contactor of the power battery high-voltage loop system are related to the performance of the vehicle, and whether the functions such as the upper loading function, the direct-current charging function and the like are normal or not is related to whether the corresponding contactor works normally, so that the real-time acquisition of the health state of each contactor is very important, and in order to monitor the health state of the contactor in real time, the invention provides a device diagnosis platform which can diagnose the health state of the contactor on the vehicle in real time.

By combining the application scenarios, taking the health state evaluation parameter as the resistance value of the main contact of the contactor as an example, how each electric vehicle acquires the resistance value of the main contact of each contactor will be described. As shown in fig. 3, the vehicle may include a vehicle end acquisition module and a vehicle end calculation module, the vehicle end acquisition module is connected with the vehicle end calculation module, and the vehicle end calculation module is respectively connected with the device diagnosis platform and the vehicle instrument. The vehicle-end acquisition module is used for acquiring terminal voltage and terminal current of each contactor main contact point, the vehicle-end calculation module is used for calculating according to the terminal voltage and the current of each contactor main contact point acquired by the vehicle-end acquisition module so as to determine the resistance value of each contactor main contact point, and the vehicle-end acquisition module is deployed on a vehicle and used for acquiring the current of a loop where each contactor is located and the terminal voltage of two ends of each contactor main contact point. In addition, in the automobile, the vehicle end calculating module may refer to a BMS module on the automobile, and the BMS module is used to calculate the resistance value of the main contact of the contactor.

The vehicle end acquisition module comprises a current sampling module and a voltage sampling module, the current sampling module is used for acquiring the current of a loop where the contactor is located, the voltage sampling module is used for acquiring the terminal voltage of two ends of a main contact of the contactor, as shown in fig. 4, fig. 4 is an acquisition schematic diagram for acquiring the current and the terminal voltage of a main contact point of the tested contactor, and the vehicle end acquisition module comprises a current sampling module 11 and a voltage sampling module 12, and a tested contactor 13, a maintenance switch, a safety device and an electrical appliance. Therefore, the current of the tested contactor 13 can be collected in real time by using the current sampling module 11, and the terminal voltage of the tested contactor 13 can be collected in real time by using the voltage sampling module 11. It should be noted that the current sampling module 12 may be a current probe for acquiring the current of the tested contactor in real time, and the voltage sampling module may be a voltage probe for acquiring the terminal voltage of the two terminals of the tested contactor. It should be noted that various types of contactors on the vehicle may be deployed in the manner shown in fig. 3, so that terminal currents and terminal voltages of various types of contactors on the vehicle may be obtained. It should be noted that, for other devices of the automobile or devices on other apparatuses, the current and the terminal voltage of the device may also be obtained correspondingly in the manner shown in fig. 4, and a detailed description is not provided herein.

After the current of the tested contactor 13 is collected in real time by the current sampling module 11 and the terminal voltage of the tested contactor 13 is collected in real time by the voltage sampling module 12, the vehicle end calculating module is used for acquiring the current of the tested contactor collected in real time from the current sampling module 11 and acquiring the terminal voltage of the tested contactor collected in real time from the voltage sampling module 12, after the vehicle end calculating module 103 acquires the current and the terminal voltage of the tested contactor collected in real time, calculation can be performed according to the current and the terminal voltage of the tested contactor, so that the resistance value of a main contact of the contactor is obtained and used as a health state evaluation parameter of the contactor, and the current I of the tested contactor 13 is collected by the current sampling module 11; the voltage sampling module 12 collects the voltage U of the main contact terminal of the tested contactor 13; then, the resistance of the main contact end of the tested contactor is R ═ U/I, and then the calculated resistance value of the main contact of the contactor can be uploaded to the device diagnosis platform.

Therefore, the device diagnosis platform can acquire the main contact resistance values of the contactors uploaded by the vehicle end calculation modules of the multiple vehicles in real time, for the device diagnosis platform, after acquiring the resistance value data of the main contacts of the contactors uploaded by the vehicle end calculation modules 103 of the multiple vehicles in real time, the health state diagnosis of the contactors can be performed according to the resistance value data of the main contacts of the contactors, and the diagnosis result is fed back to the vehicle end calculation module, so that the vehicle end calculation module can display the diagnosis result through a whole vehicle instrument or driver terminal equipment, and thus, a driver can know the health state of the device in real time.

It should be noted that, the above description is only given by taking the contactor and using the resistance value of the main contact of the contactor as the health status evaluation parameter as an example, the present invention may also use the voltage difference between the positive and negative electrodes of the device, the temperature, and other parameters as the health status evaluation parameters to perform the health diagnosis of the device, and the present invention is not limited thereto. After the device diagnosis platform obtains the health state evaluation parameters of the same type of device in real time, healthy and unhealthy devices can be determined according to the obtained health state evaluation parameters of the same type of device, which will be described in detail below with reference to a device diagnosis method.

As shown in fig. 5, based on the device diagnosis system, a device health diagnosis method is correspondingly provided, and it should be noted that, in the following embodiments, for convenience of description and understanding, an example in which the apparatus is an automobile and the device is a contactor is illustrated, the device diagnosis method includes the following steps:

s10: and acquiring the health state evaluation parameters of the same type of devices of the same type of equipment in real time, wherein the health state evaluation parameters are associated with the current health state of the devices.

The health state evaluation parameter of the device is a parameter related to the health state of the device, and may be, for example, a temperature of the device, a resistance value across the device, or a voltage difference across the device. It should be noted that the same kind of device refers to the same kind of device under the same type of equipment.

Taking the device as a contactor of a vehicle and the health state evaluation parameter as the resistance value of the main contact of the contactor as an example, the same device refers to the same type of contactor, and the same type may refer to vehicles of the same model or the same batch type, such as a voltage dividing contactor, a PTC contactor, a dc charging port positive electrode contactor, a dc charging port negative electrode contactor, an upper charging contactor, a main pre-charging contactor, a discharging main contactor, an auxiliary pre-charging contactor, an ac slow charging contactor or an auxiliary contactor on an automobile.

Referring to fig. 6, fig. 6 is a schematic diagram of numbers of a contactor matrix, where m vehicles of the same type include vehicle 1, vehicle 2, …, and vehicle m, and each of the different vehicles (vehicle 1-vehicle m) classifies each contactor of the vehicle according to the contactor type, and sequentially divides the contactor type of the vehicle into contactor type 1, contactor type 2, contactor type …, and contactor type n. Because the vehicles belong to the same type, the types of contactors included in each vehicle are generally the same, and the types of contactors of each vehicle can be further divided according to the serial number of the vehicle, for example, different types of contactors of the vehicle 1 can be sequentially divided into 1-1, 1-2, … and 1-n; each of the different types of contactors of the vehicle 2 may be divided into 2-1, 2-2, …, 2-n in sequence; each of the different types of contactors of the vehicle 3 may be divided into 3-1, 3-2, …, 3-n in sequence; by analogy, different types of contactors of the vehicle m can be sequentially divided into m-1, m-2, … and m-n, so that a contactor matrix numbering schematic diagram as shown in FIG. 6 can be obtained. It should be noted that the contactor matrix number shown in fig. 6 is only an example and does not limit the present invention, and other numbering manners may be used in other embodiments as long as the device diagnosis platform can distinguish the device types and the vehicles to which the device diagnosis platform belongs. It should be noted that, in other cases, the devices and apparatuses may also adopt the numbering scheme shown in fig. 6, and the specific description of the present invention is not expanded.

In the vehicle 1-vehicle m, each vehicle may acquire the health status evaluation parameter of its own contactor in real time, and for example, the specific acquisition mode may refer to the mode shown in fig. 4, that is, the vehicle may acquire the current and the terminal voltage of the contactor through the deployed vehicle end acquisition module, and calculate the resistance value of the main contact of the contactor through the vehicle end calculation module according to the current and the terminal voltage acquired by the vehicle end acquisition module, and finally upload the resistance value of the main contact of each contactor to the device diagnosis platform through the vehicle end calculation module. For the device diagnosis platform, the device diagnosis platform can obtain the resistance values of the main contacts of the same type of contactor in the vehicles 1 to m in real time, for example, the resistance values of the main contacts of the contactor type 1 can be obtained, that is, the resistance values of the main contacts of the corresponding contactors 1 to n, 2 to n, … and m to n in the vehicles 1 to m can be obtained.

It should be noted that, in an embodiment, all the health state evaluation parameters of the same type of device are health state evaluation parameters acquired at the same time, and the invention is not limited in particular.

S20: determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device, and if the suspicious parameters exist, executing step S30; if no suspicious parameters exist, go to step S40.

After health state evaluation parameters of devices of the same type of equipment of the same type are obtained in real time, whether suspicious parameters exist in all the health state evaluation parameters of the devices of the same type or not is determined, wherein the suspicious parameters are the health state evaluation parameters with preset errors with other health state evaluation parameters. It should be noted that the preset error is a preset error, and the preset error may be an error set according to an empirical value, a gross error determined according to a gross error theory, or other error relationships, and the embodiment of the present invention is not limited in this respect. It is understood that, for the same type of apparatuses, in the case that the same type of devices are normal, the health status evaluation parameters of the same type of devices of each apparatus should be within expectations, if the health status evaluation parameter of one of the devices is beyond expectations, the current health status evaluation parameter of the device is suspicious, and if the suspicious parameter exists, step S30 is executed; if no suspicious parameters exist, go to step S40.

For example, as shown in FIG. 6, let R be the resistance values of the main contacts of contactors 1-n, 2-n, …, m-n, respectively_1n、R_2n、…、R_mn-1、R_mnUpon acquisition of R_1n、R_2n、…、R_mn-1、R_mnThereafter, R can be determined_1n、R_2n、…、R_mn-1、R_mnWhether there is a suspect parameter.

S30: and determining that the device corresponding to the suspicious parameters is in an unhealthy state.

S40: and determining that all devices corresponding to all the health state evaluation parameters are in the health state.

For steps S30-S40, after determining whether there is a suspicious parameter in all the health status evaluation parameters of the same type of device, if there is a suspicious parameter, it is determined that the device corresponding to the suspicious parameter is in an unhealthy state, and it can also be understood that the device corresponding to the suspicious parameter is in a damaged state; if no suspicious parameters exist, determining that all devices corresponding to all the health state evaluation parameters are in a health state, and also understanding that all devices corresponding to all the health state evaluation parameters are in an undamaged state.

For example, obtaining the main contact resistance R of the same type of contactor_1n、R_2n、…、R_mn-1、R_mnThereafter, R can be determined_1n、R_2n、…、R_mn-1、R_mnWhether there is a suspicious parameter in (1); for example, if R is determined_2nIf it is a suspicious parameter, determining the R_2nThe corresponding contactor is in an unhealthy state, that is, the contactor n in the vehicle 2 is in an unhealthy state, and the method is also applicable to other contactors, such as the contactor 1-the contactor n-1, and the specific description is not specifically provided herein; if R is determined_1n、R_2n、…、R_mn-1、R_mnIf no suspect parameter exists, then determine R_1n、R_2n、…、R_mn-1、R_mnThe corresponding contactors are all in a healthy state. I.e. the contactors n in vehicle 1-vehicle m are all in a healthy state.

It should be noted that, in this embodiment, only the contactor and the health status evaluation parameter are taken as examples for description, and the embodiment of the present invention is not limited thereto. The contactor may be other devices on the vehicle, or contactors of other equipment, or other devices of other equipment; the health state evaluation parameter may be a temperature, a voltage difference, or the like of the device, and is not specifically limited nor exemplified.

Therefore, the embodiment of the invention provides a device diagnosis method, which comprises the steps of firstly obtaining the health state evaluation parameters of devices of the same type of equipment in real time, wherein the health state evaluation parameters are associated with the current state of the devices; determining the health state evaluation parameters of the same type of devices according to the health state evaluation parameters of each device; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices, wherein the suspicious parameters are parameters with preset errors with other parameters of all the health state evaluation parameters; and if the suspicious parameters exist, determining that the devices corresponding to the suspicious parameters are in unhealthy states. Therefore, the embodiment of the invention can acquire the health state evaluation parameters of the same type of devices of different types of equipment in real time to determine the health state of each device, so that the service life of each device can be diagnosed, the health state of each device is not judged when the vehicle is maintained and checked regularly, the service life of each device can be monitored in real time, the problem of inaccurate data caused by single detection during regular maintenance can be effectively reduced, and the safety of the equipment is improved.

It should be noted that, in the embodiment of the present invention, after the health status evaluation parameters of the same type of device of the same type of equipment are obtained in real time, there may be multiple ways to determine whether there is a suspicious parameter in all the health status evaluation parameters of the same type of device, as described above, one of which is determined by using error theory. The following are described separately:

in one embodiment, the step S20 of determining whether there is a suspicious parameter in all the health status evaluation parameters of the same device class includes the following steps:

s21: and determining a first mean value and a first standard deviation corresponding to all the health state evaluation parameters.

S22: and determining whether the health state evaluation parameters of all the devices in the same class are suspicious parameters according to the first mean value and the first standard deviation corresponding to all the health state evaluation parameters.

For steps S21-S22, after acquiring the health status evaluation parameters of the same type of device of the same type of equipment, the mean and standard deviation corresponding to all the health status evaluation parameters of the same type of device may be determined, in order to facilitate distinguishing other mean and standard deviation appearing in subsequent embodiments, in this embodiment, the mean and standard deviation corresponding to all the health status evaluation parameters of the same type of device are respectively referred to as a first mean and a first standard deviation, and whether the health status evaluation parameters of each device in the same type of device are suspicious parameters is determined according to the first mean and the first standard deviation corresponding to all the health status evaluation parameters.

It will be appreciated that the first standard deviation of all of the sets of health state assessment parameters reflects the accuracy of the individual health state assessment parameters in the set, while the first mean is the number of numbers representing the trends in the set of all of the sets of health state assessment parameters, i.e., the first standard deviation and the first mean are the two most important reference values for the trends and the degrees of dispersion in the sets of all of the health state assessment parameters. Therefore, in the embodiment of the present invention, it can be determined whether the health state evaluation parameter corresponding to each device is normal by the first standard deviation and the mean value together, so as to determine whether the health state evaluation parameter of a certain device is a suspicious parameter.

For example, the main contact resistance value R of the same type of contactor n when the same type of vehicles (1-m) of the same lot type is acquired_1n、R_2n、…、R_mn-1、R_mnThereafter, all health assessment parameters R may be determined_1n、R_2n、…、R_mn-1、R_mnCorresponding first mean value R_0nAnd a first standard deviation σ_nWherein, the first mean value R corresponding to the same type of contactor n_0nAnd a first standard deviation σ_nCan be obtained by the following calculation formula:

it should be noted that, in step S22, that is, according to the first mean value and the first standard deviation corresponding to all the health state evaluation parameters, it is determined whether the health state evaluation parameter of each device in the same type of device is a suspicious parameter, and specifically, the determination may be performed in various ways, where in some embodiments, according to the first mean value and the first standard deviation corresponding to all the health state evaluation parameters, it may be determined whether the health state evaluation parameter corresponding to each device is a suspicious parameter based on the following criteria.

The first suspicious parameter diagnosis method is based on the 3 sigma criterion:

that is, in step S22, it is determined whether the health state evaluation parameter corresponding to each device is a suspicious parameter according to the absolute error and the standard deviation corresponding to each device, which specifically includes the following steps:

S221A: determining the absolute value of the difference between the health state evaluation parameter of each device in the same class of devices and the first mean value to obtain a first absolute error corresponding to each device;

after calculating the first mean value R_0nAnd a first standard deviation σ_nThen, the resistance value and the first average value R of the main contacts of all the contactors in the same type of contactor can be determined_0nTo obtain a first absolute error corresponding to each contactor, the first absolute errors corresponding to each contactor being: | R_1n-R_0n|、|R_2n-R_0n|、…、|R_mn-R_0n|。

S222A: determining the ratio of the first absolute error corresponding to each device to the first standard deviation;

S223A: determining a comparison coefficient according to the quantity of all the health state evaluation parameters, wherein the comparison coefficient and the quantity are in positive correlation;

in this step, a comparison coefficient needs to be further determined according to the number of all the health state evaluation parameters, it should be noted that, based on the 3 σ criterion, the comparison coefficient may be selected to be 3, but it is noted that the comparison coefficient may be configured according to the number of all the health state evaluation parameters, and the comparison coefficient and the number have a positive correlation, that is, the greater the number of all the health state evaluation parameters formed by the same type of device in the same type of device, the higher the comparison coefficient. For example, when the number of the health status evaluation parameters is greater than a certain number, the comparison coefficient may also be set to 4, which is not specifically limited and is not described further.

S224A: if the ratio of the first absolute error corresponding to the target device to the first standard deviation in each device is greater than or equal to the comparison coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter; and if the ratio of the corresponding first absolute error to the first standard deviation is smaller than the comparison coefficient, determining that the health state evaluation parameter corresponding to the target device is not a suspicious parameter.

For example, when determining that the first absolute errors corresponding to the contactors are: | R_1n-R_0n|、|R_2n-R_0n|、…、|R_mn-R_0nAnd determining a first standard deviation σ_nThen, determining the ratio of the first absolute error corresponding to each contactor to the first standard deviation, namely the ratio corresponding to each device:

after the comparison coefficient and the ratio of the first absolute error to the first standard deviation corresponding to each device are determined, whether the health state evaluation parameter corresponding to each device is a suspicious parameter or not can be correspondingly determined according to the relationship between the comparison coefficient and the ratio of each device, specifically, the device to be tested is set as a target device, if the ratio of the corresponding first absolute error to the first standard deviation is greater than or equal to the comparison coefficient, the health state evaluation parameter corresponding to the target device is determined as the suspicious parameter, and if the ratio of the first absolute error to the first standard deviation corresponding to the target device is less than the comparison coefficient, the health state evaluation parameter corresponding to the target device is determined as the suspicious parameter.

For example, taking a proportionality coefficient of 3 as an example, the health state evaluation parameter determination method corresponding to each device is as follows:

let R_1nThe corresponding device is the target device if

That is | R_1n-R_0n|≥3σ_nThen determine R_1nFor suspicious parameters, the R_1nThe corresponding device is in an unhealthy state and may be in a damaged state; otherwise, then R is_1nThe corresponding device is in a healthy state and not in a damaged state.

Let R_2nThe corresponding device is the target device if

That is | R_2n-R_0n|≥3σ_nThen determine R_2nFor suspicious parameters, the R_2nThe corresponding device is in an unhealthy state and may be in a damaged state; otherwise, then R is_2nThe corresponding device is in a healthy state and not in a damaged state.

Let R_mnThe corresponding device is the target device if

That is | R_mn-R_0n|≥3σ_nThen determine R_mnFor suspicious parameters, the R_mnThe corresponding device is in an unhealthy state and may be in a damaged state; otherwise, then R is_mnThe corresponding device is in a healthy state and is not in a damaged state;

the health status of each device can be obtained by analogy with other devices in each device, and the description is not repeated here.

The second suspicious parameter diagnosis mode is based on the "Grabbs criterion":

that is, in step S22, that is, according to the first mean value and the first standard deviation corresponding to all the health state evaluation parameters, it is determined whether the health state evaluation parameters of each device in the same type of device are suspicious parameters, which specifically includes the following steps:

S221B: and determining the difference value between the health state evaluation parameter of each device in the same type of device and the first mean value to obtain a target error value corresponding to each device.

After calculating the first mean value R_0nAnd a first standard deviation σ_nThen, the resistance value and the first average value R of the main contacts of all the contactors in the same type of contactor can be determined_0nTo obtain a target error value corresponding to each contactor, the target error values corresponding to each contactor are respectively: r_1n-R_0n、R_2n-R_0n、…、R_mn-R_0n。

S222B: and determining the ratio of the target error value corresponding to each device to the first standard deviation.

For example, after determining that the target error values corresponding to the contactors are: r_1n-R_0n、R_2n-R_0n、…、R_mn-R_0nAnd determining a first standard deviation sigma_nThereafter, a target error value and a first standard deviation σ corresponding to each contactor may be determined_nThe ratio of (A) to (B) is respectively:

S223B: and if the ratio of the target error value corresponding to the target device to the first standard deviation in each device is greater than or equal to the Graves critical value, determining that the health state evaluation parameter corresponding to the target device is a suspicious parameter, and if the ratio of the target error value corresponding to the target device to the first standard deviation is less than the Graves critical value, determining that the health state evaluation parameter corresponding to the target device is not a suspicious parameter.

In this step, a threshold value of grassbs is first determined, and the critical value of grassbs is related to the number of all the health status evaluation parameters and the predetermined significance level coefficient α.

Obtaining the main contact resistance value R of the same type of contactor n of the same batch type of vehicles (1-m)_1n、R_2n、…、R_mn-1、R_mnThen, can be aligned with R_1n、R_2n、…、R_mn-1、R_mnSorting according to size, and setting as R₍₁₎≦R₍₂₎≦…≦R_(m). According to the Grabbs criterion, can be derived

And

and a predetermined significance level coefficient α is 0.05 or 0.01, a grabbs critical value table shown in the following table 1 can be obtained, wherein g is₀(m, α) represents a Grabbs threshold, m represents a number:

TABLE 1

As shown in table 1, the grassbs threshold is related to the number m of all the health status evaluation parameters and the predetermined significance level coefficient α, for example, when the number m of all the health status evaluation parameters is 3 and the predetermined significance level coefficient α is 0.05, the corresponding grassbs threshold g is obtained₀(m, α) was 1.15. Corresponding Grabbs critical values g under various combinations of m and alpha can be obtained according to the table 1₀(m, α), which are not specifically exemplified here. It should be noted that the values of the predetermined significance level coefficient α and the corresponding table 1 are only exemplary and do not limit the embodiment of the present invention. According to the health diagnosis precision, the size of the preset significance level coefficient alpha can be flexibly set, so that a corresponding Grabbs critical value table is derived.

Therefore, in this embodiment, the ratio of the target error value of the target device to the first standard deviation in each device is set to

If g is_(i)≥g₀(m, α), determining the health state evaluation parameter corresponding to the target device as a suspicious parameter; g_(i)<g₀(m, α), determining that the health state evaluation parameter corresponding to the target device is not a suspicious parameter.

For example, if the number of all the health status evaluation parameters is 50 and the predetermined significance level coefficient α is 0.05, the corresponding threshold g of the grubbs is determined₀When (50,0.05) — 2.96, the health state evaluation parameter determination method for each device is as follows:

let R_1nCorresponding devices being objectsDevice if

That is, then R is determined_1nFor suspicious parameters, the R_1nThe corresponding device is in an unhealthy state and may be in a damaged state; otherwise, then R is_1nThe corresponding device is in a healthy state and not in a damaged state.

Let R_2nThe corresponding device is the target device if

Then determine R_2nFor suspicious parameters, the R_2nThe corresponding device is in an unhealthy state and may be in a damaged state; otherwise, then R is_2nThe corresponding device is in a healthy state and not in a damaged state.

Let R_mnThe corresponding device is the target device if

Then determine R_mnFor suspicious parameters, the R_mnThe corresponding device is in an unhealthy state and may be in a damaged state; otherwise, then R is_mnThe corresponding device is in a healthy state and not in a damaged state.

It should be noted that, in addition to the first and second manners of determining whether there is a suspicious parameter in all the health status evaluation parameters of the same type of device, there may be other manners in the embodiment of the present invention, as shown below:

the third suspicious parameter diagnosis mode is based on the Romanov criterion:

in one embodiment, namely step S20, namely determining whether there is a suspicious parameter in all the health status evaluation parameters of the same type of device, the method includes the following steps:

s21': and determining a second mean value and a second standard deviation corresponding to the rest health state evaluation parameters in all the health state evaluation parameters.

The remaining health state evaluation parameters are all the health state evaluation parameters except the health state evaluation parameter corresponding to the target device among all the health state evaluation parameters.

S22': and determining the absolute value of the difference value between the health state evaluation parameter corresponding to the target device and the second mean value to obtain a second absolute error corresponding to the target device.

For steps S21 '-S22', after acquiring the health status assessment parameters of the same type of devices, determining a second mean value R 'corresponding to the remaining health status assessment parameters in all the health status assessment parameters'_0nAnd second standard deviation σ'_nDetermining a health state evaluation parameter corresponding to the target device and a second mean value R'_0nTo obtain a second absolute error corresponding to the target device. Setting the health state evaluation parameter corresponding to the target device as R_(j)Then the R is rejected_(j)And then, a second mean value R 'corresponding to other residual health state evaluation parameters'_0nAnd second standard deviation σ'_nCan be determined by the following calculation:

wherein v is_i＝(R_i-R′_0n)

For example, let the main contact resistance R of the same type of contactor n_1n、R_2n、…、R_mn-1、R_mnThe total number of the resistance values of m is set as R for the main contact corresponding to the target device_1nThen determine divide by R_1nBesides, the second mean value R 'corresponding to other residual health state evaluation parameters'_0nAnd σ'_nSecond standard deviation, i.e. R_2n、…、R_mn-1、R_mnCorresponding second mean value R'_0nAnd second standard deviation σ'_n。

Determining the absolute value of the difference between the health state evaluation parameter corresponding to the target device and the second mean value to obtain a second absolute error corresponding to the target device, i.e. the absolute error corresponding to the target device is | R_j-R′_0n|。

S23': and determining the ratio of the second absolute error corresponding to the target device to the second standard deviation.

Determining a second absolute error R corresponding to the target device_j-R′_0nAnd second standard deviation σ'_nThereafter, the absolute error R corresponding to the target device can be determined_j-R′_0nAnd standard deviation sigma'_nThe ratio between, that is:

s24': if the ratio of the second absolute error corresponding to the target device to the second standard deviation is greater than or equal to the Romannofski test coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter; and if the ratio of the second absolute error corresponding to the target device to the second standard deviation is smaller than the Romannofski test coefficient, determining that the health state evaluation parameter corresponding to the target device is not a suspicious parameter.

In this step, a romanofsky check coefficient is first determined, which is related to the number of all health status evaluation parameters and a preset significance level coefficient α, which may be set to 0.05 or 0.01.

Obtaining the main contact resistance value R of the same type of contactor n_1n、R_2n、…、R_mn-1、R_mnThen, based on the Romanofsky criterion and the predetermined significance level coefficient α, a Romanofsky test coefficient table, K, can be obtained as shown in Table 2 below₀(m, α) represents the Romanov test coefficient, m represents the number, and Table 2 is as follows:

TABLE 2

As shown in table 2, the romanofsky check coefficient is related to the number m of all the health status evaluation parameters and the preset significance level coefficient α, for example, when the number m of all the health status evaluation parameters is 10 and the preset significance level coefficient α is 0.05, the corresponding romanofsky check coefficient K is determined₀(m, α) was 2.43. The corresponding Romanofowski test coefficients K under various combinations of m and alpha can be obtained according to the table 2₀(m, α), and specific examples thereof are not individually illustrated here. It should be noted that the values of the predetermined significance level coefficient α and the corresponding table 2 are only exemplary and do not limit the embodiment of the present invention. According to the health diagnosis precision, the size of the preset significance level coefficient alpha can be flexibly set, so that a corresponding Romanofsky test coefficient table is derived.

Therefore, in this embodiment, the ratio of the second absolute error to the second standard deviation corresponding to the target device is set to

If K_(j)≥K₀(m, α), determining the health state evaluation parameter corresponding to the target device as a suspicious parameter; k_(j)<K₀(m, α), determining that the health state evaluation parameter corresponding to the target device is not a suspicious parameter.

Illustratively, taking the number of all the health status evaluation parameters as 10 and the predetermined significance level coefficient α as 0.05 as an example, the corresponding romanofsky test coefficient K is₀When (10,0.05) — 2.43, the health state evaluation parameter determination method for each device is as follows:

let R_1nThe corresponding device is the target device if

That is, then R is determined_1nFor suspicious parameters, the R_1nThe corresponding device is in an unhealthy state, possiblyIn a damaged state; otherwise, then R is_1nThe corresponding device is in a healthy state and not in a damaged state.

Let R_2nThe corresponding device is the target device if

Let R_mnThe corresponding device is the target device if

It should be noted that, in the above several manners of determining the suspicious parameters, the devices corresponding to the resistance values at the two ends may be preferentially diagnosed as the target devices, that is, the health state of the device corresponding to the largest evaluation parameter and the health state of the device corresponding to the smallest evaluation parameter in all the health state evaluation parameters are determined according to the manner of determining the health state according to the grassbris critical value, and if the devices corresponding to the largest and the smallest evaluation parameters are all in the health state, it is determined that the same batch of devices are all in the health state. If the devices corresponding to the maximum and/or small values of the health state evaluation parameters are not in the health state, the health state evaluation parameters corresponding to the devices in the health state are removed, and the health states of the rest devices are continuously determined according to the health state diagnosis mode until the diagnosis is finished. It can be understood that the maximum value and the minimum value of the health state assessment parameters are parameters which are most prone to gross errors, and therefore, in order to improve the diagnosis efficiency, it may be determined whether the maximum assessment parameter and the minimum assessment parameter are suspicious parameters, and if the maximum assessment parameter and the minimum assessment parameter are not suspicious parameters, it may be determined that the remaining other health state assessment parameters in all the health state assessment parameters are not suspicious parameters, so that it is diagnosed that all the devices in the batch are in a health state, and unnecessary diagnosis work is reduced.

The fourth suspicious parameter diagnosis method is based on the dirichson criterion:

for example, in this step, the main contact resistance value R of the same type of contactor n is acquired_1n、R_2n、…、R_mn-1、R_mnThen, can be aligned with R_1n、R_2n、…、R_mn-1、R_mnSorting according to size, and setting as R₍₁₎≦R₍₂₎≦…≦R_(m). Then R is₍₁₎For maximum evaluation parameter, R_(m)Is the maximum evaluation parameter.

for step b, it can be understood that let the formula of the dirac statistic corresponding to the maximum evaluation parameter be r_ijThe formula of the Dixon statistic corresponding to the minimum evaluation parameter is r'_ijThen, according to the number m of all health state evaluation parameters and the dirough criterion, the following cases can be classified:

in step d, a dirke threshold is determined, which is related to the number of all health status evaluation parameters and the predetermined significance level coefficient α,

for example, let the main contact resistance R of the same type of contactor n_1n、R_2n、…、R_mn-1、R_mnThe predetermined significance level coefficient α is set to be 0.05 or 0.01, and a dirichson critical value table, r, shown in the following table 3 can be obtained according to the dirichson criterion and the predetermined significance level coefficient α₀(m, α) represents a critical value of Dixon, m represents a quantity, and Table 3 is as follows:

TABLE 3

As shown in table 3, the dirke critical value is related to the number m of all the health status evaluation parameters and the preset significance level coefficient α, and the dirke statistic formula corresponding to the maximum evaluation parameter is different from the dirke statistic formula corresponding to the minimum evaluation parameter, for example, when the number m of all the health status evaluation parameters is 10 and the preset significance level coefficient α is 0.05, the corresponding dirke critical value r is the corresponding dirke critical value r₀(10,0.05) is 0.477, and the maximum and small evaluation parameters correspond to the statistical formula of the dirichson as follows:

and

it can be seen that the corresponding critical value r of the dirichson can be obtained under various combinations of m and α according to the above table 3₀(m, α), not to be taken as specific examples hereinAnd (4) explanation. It should be noted that the value of the third predetermined significance level coefficient α and the corresponding table 3 are only exemplary and do not limit the embodiment of the present invention. According to the health diagnosis precision, the magnitude of the third preset significance level coefficient alpha can be flexibly set, so that the corresponding critical value of the Dixon is derived.

for example, if the number of all the health status evaluation parameters is 10 and the third predetermined significance level coefficient α is 0.05, the dirke threshold r is obtained₀If (10,0.05) is 0.477, the health status evaluation parameter determination method corresponding to each device is as follows:

since m is 10, the formula of the dirac statistic corresponding to the maximum evaluation parameter is adopted

Calculating if

Determining the maximum evaluation parameter as a suspicious parameter, wherein the device corresponding to the maximum evaluation parameter is in an unhealthy state and possibly in a damaged state; otherwise, the maximum evaluation parameter is not a suspicious parameter, and the device corresponding to the maximum evaluation parameter is in a healthy state and is not in a damaged state.

Since m is 10, the formula of the dirac statistic corresponding to the minimum evaluation parameter is adopted

Calculating if

Determining the minimum evaluation parameter as a suspicious parameter, wherein the device corresponding to the minimum evaluation parameter is in an unhealthy state and may be in a damaged stateState; the minimum evaluation parameter is not a suspicious parameter, and the device corresponding to the minimum evaluation parameter is in a healthy state and is not in a damaged state.

After step e, if it is determined that the maximum evaluation parameter and the minimum evaluation parameter are not suspicious parameters, determining that other remaining health state evaluation parameters in all the health state evaluation parameters are not suspicious parameters, if it is determined that the maximum evaluation parameter and/or the minimum evaluation parameter are suspicious parameters, correspondingly eliminating the maximum evaluation parameter and/or the minimum evaluation parameter, continuously selecting new maximum evaluation parameter and/or new minimum evaluation parameter, and re-executing the steps a to e until all the suspicious parameters are determined, so that all the suspicious parameters in m health state evaluation parameters can be determined, and whether all the devices are in a health state is determined.

It should be noted that, in the above embodiments, a plurality of ways of determining whether there is a suspicious parameter in the acquired health status evaluation parameters of the same type of device are provided, so that the richness and the implementability of the scheme are improved. Meanwhile, it is noted that the above-mentioned manner is only an example, and in practical applications, after the health state evaluation parameters of the same type of device in the same type of device are obtained, there may be another manner of determining whether there is a suspicious parameter. For example, other gross error theories may be used in conjunction with the health of the same type of device to evaluate the parameter for abnormal parameters to diagnose the presence of damaged or unhealthy devices.

The fifth suspicious parameter diagnosis mode is based on the diagnosis principle of 'extreme health state evaluation parameters':

it should be noted that, when the number of the obtained health state evaluation parameters of the same type of device is small, for example, less than 3, a fifth suspicious parameter diagnosis mode may also be adopted, that is, the obtained health state evaluation parameters are directly compared with limit parameters, where the health state evaluation parameters are, for example, the main contact resistance value of the contactor, and the limit parameters are limit resistances of the contactor within a healthy range, and when the health state evaluation parameters exceed the limit resistances, it is indicated that the corresponding contactor is in an unhealthy state and may be damaged, and otherwise, the contactor is in a healthy state and is not damaged.

It should be noted that, in the present invention, a scheme for performing health diagnosis after acquiring health state evaluation parameters of a plurality of devices of the same type is provided, and as can be seen, the health state evaluation parameters of the devices of the same type have an influence on accuracy of subsequent diagnosis, and generally, the more data, the more information that can be referred to for diagnostic analysis, therefore, in order to improve diagnostic accuracy, in an embodiment, determining whether there is a suspicious parameter in all the health state evaluation parameters of the devices of the same type includes:

It is worth emphasizing that, in this embodiment, different corresponding suspicious parameter diagnosis manners are selected based on the number of the health state assessment parameters, so as to improve the precision of suspicious parameter diagnosis, and all the suspicious parameter diagnosis manners are applicable to any number of the health state assessment parameters, that is, any suspicious parameter diagnosis manner provided by the present invention may be adopted for any number of the health state assessment parameters, which is not specifically limited.

Specifically, the method for diagnosing whether suspicious parameters exist in all health state evaluation parameters according to the corresponding suspicious parameter diagnosis mode comprises the following steps:

if the number is larger than a first preset number threshold, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices according to a first suspicious parameter diagnosis mode;

if the number is equal to a first preset number threshold value, or is larger than a second preset number threshold value and smaller than the first preset number threshold value, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device according to a second suspicious parameter diagnosis mode;

if the number is equal to a second preset number threshold value, or is greater than a third preset number threshold value and smaller than the second preset number threshold value, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device according to a third or fourth suspicious parameter diagnosis mode;

and if the number is smaller than or equal to a third preset number threshold, determining whether suspicious parameters exist in all the health state evaluation parameters of the same type of devices according to a fifth suspicious parameter diagnosis mode.

The first, second, third, fourth and fifth preset quantity thresholds are preset values, and can be configured according to requirements, and the corresponding suspicious parameter diagnosis mode can select a suitable calculation mode according to experience, and exemplarily, the preset quantity is m, as shown below:

if m is greater than 50, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device according to the first suspicious parameter diagnosis mode;

if m is more than or equal to 30 and less than or equal to 50, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device according to a second suspicious parameter diagnosis mode;

if m is more than or equal to 3 and less than or equal to 30, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device according to a third or fourth suspicious parameter diagnosis mode;

and if m is less than 3, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices according to a fifth suspicious parameter diagnosis mode.

It should be noted that, in an embodiment, more than two suspicious parameter diagnosis methods may be selected to determine whether there is a suspicious parameter, and when a certain health state evaluation parameter is considered as a suspicious parameter, the certain health state evaluation parameter is determined as a suspicious parameter. When the judgment results of the methods are inconsistent, the diagnosis result is discarded, and the health state evaluation parameters of the devices of the same type are obtained again so as to carry out the health state evaluation process again. It can be understood that in the embodiment of the present invention, the error problem caused by using a single criterion can be avoided by comparing in several ways, thereby further improving the accuracy of diagnosis.

In one embodiment, if no suspect parameters exist, the method further comprises the steps of:

s50: comparing each health status assessment parameter of all health status assessment parameters with a corresponding limit parameter;

s60: and if the health state evaluation parameter is larger than the corresponding limit parameter, determining that the device corresponding to the health state evaluation parameter is in an unhealthy state.

For example, if the contactor is diagnosed as unhealthy based on the suspect parameters, maintenance may be required. Meanwhile, the contactor has a limit resistance Rn within a healthy range, and when the resistance value of the main contact of the contactor exceeds the limit resistance value, the contactor is also in an unhealthy state and needs to be maintained. Therefore, the health state of the contactor can be judged based on the suspicious parameter diagnosis mode and the threshold value, and the method and the device are more accurate and effective.

It should be noted that, in an embodiment, when the number of all the health status evaluation parameters is large, any one of the first to fourth suspected parameter diagnosis methods may be adopted to determine the suspected parameters, and finally, the re-diagnosis may be performed according to the limit parameters to diagnose whether a certain device is healthy. For example, if the health state evaluation parameter a is determined to be a suspicious parameter by any one of the first to fourth suspicious parameter diagnosis methods, it is preliminarily determined that the device corresponding to the health state evaluation parameter a is unhealthy, and then the health state evaluation parameter a is compared with the corresponding limit parameter B, and if the health state evaluation parameter a is greater than the limit parameter B, it is finally determined that the device corresponding to the health state evaluation parameter a is unhealthy, and may be damaged and needs to be repaired or replaced, thereby effectively improving the diagnosis accuracy.

In one embodiment, the device diagnostic method further comprises the steps of:

s70: and feeding back the device diagnosis result to corresponding equipment so that the corresponding equipment displays the device diagnosis result.

By taking the equipment as an example, the diagnosis result of the device can be fed back to the vehicle-end calculation module of the corresponding vehicle, so that the vehicle-end calculation module can display the diagnosis result through a whole vehicle instrument or driver terminal equipment, thus a driver can know the health state of the device in real time, and the device can be replaced in time when unhealthy devices exist, and the safety of the equipment is effectively improved.

Referring to fig. 7, a device diagnosis platform in the device diagnosis method according to the present invention is described in detail below, and the device diagnosis platform in the embodiment of the present invention includes:

the acquiring module 101 is configured to acquire health state evaluation parameters of devices of the same type of equipment in real time, where the health state evaluation parameters are associated with a current state of the device;

the determining module 102 is configured to determine whether suspicious parameters exist in all health state evaluation parameters of the same type of device, where the suspicious parameters are parameters having preset errors with other parameters of all health state evaluation parameters;

the diagnosis module 103 is configured to diagnose that a device corresponding to the suspicious parameter is in an unhealthy state if the suspicious parameter exists.

In an embodiment, the determining module 102 is specifically configured to:

In an embodiment, if there are no suspicious parameters, the determining module 102 is further configured to:

In an embodiment, all the health status evaluation parameters of the same type of device obtained by the obtaining module 101 are the temperature of the device, the resistance value across the device, or the voltage difference across the device.

In an embodiment, all the health status evaluation parameters of the same type of device acquired by the acquiring module 101 are health status evaluation parameters acquired at the same time.

The invention provides a device diagnosis platform, which is characterized in that health state evaluation parameters of devices of the same type of equipment of the same type are acquired in real time, and the health state evaluation parameters are associated with the current state of the devices; determining the health state evaluation parameters of the same type of devices according to the health state evaluation parameters of each device; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices, wherein the suspicious parameters are parameters with preset errors with other parameters of all the health state evaluation parameters; and if the suspicious parameters exist, determining that the devices corresponding to the suspicious parameters are in unhealthy states. Therefore, the embodiment of the invention can acquire the health state evaluation parameters of the same type of devices of different types of equipment in real time to determine the health state of each device, so that the service life of each device can be diagnosed, the health state of each device is not judged when the vehicle is maintained and checked regularly, the service life of each device can be monitored in real time, the problem of inaccurate data caused by single detection during regular maintenance can be effectively reduced, and the safety of the equipment is improved.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. For specific limitations of the device diagnostic platform, reference may be made to the limitations of the device diagnostic method above, and further description thereof is omitted here. The various modules in the device diagnostic platform described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a device diagnostic platform is provided, which may be a big data platform whose internal structure diagram may be as shown in FIG. 8. The device diagnosis platform comprises a processor, a memory and a communication interface which are connected through a system bus. Wherein the processor of the device diagnostic platform is configured to provide computational and control capabilities. The memory of the device diagnostic platform includes volatile and non-volatile storage media, internal memory. The storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and computer programs in the storage medium to run. The communication interface of the device diagnosis platform is used for realizing communication connection with external equipment, such as the BMS module in the vehicle in the foregoing embodiment, so that the BMS module can upload the health state evaluation information of the contactor to the device diagnosis platform through the communication interface. The computer program is executed by a processor to implement a device diagnostic method, which may correspond to the description of the aforementioned method embodiments, and will not be repeated here.

The embodiment of the present invention further provides a readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the device diagnosis method or the functions of the device diagnosis platform are implemented, which are not described repeatedly herein.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A device diagnostic method, comprising:

acquiring health state evaluation parameters of devices of the same type of equipment in real time, wherein the health state evaluation parameters are associated with the current health state of the devices;

2. The method of claim 1, wherein determining whether there is a suspect parameter among all health assessment parameters for the same class of devices comprises:

3. The method of claim 2, wherein determining whether the health status evaluation parameter of each device in the same class of devices is a suspicious parameter according to the first mean and the first standard deviation corresponding to all the health status evaluation parameters comprises:

determining the absolute value of the difference between the health state evaluation parameter of each device and the first mean value to obtain a first absolute error corresponding to each device;

determining a comparison coefficient according to the number of all the health state evaluation parameters, wherein the comparison coefficient and the number have positive correlation;

and if the ratio of the first absolute error corresponding to the target device to the first standard deviation in the devices is greater than or equal to the comparison coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter.

4. The method of claim 2, wherein determining whether the health status evaluation parameter of each device in the same class of devices is a suspicious parameter according to the first mean and the first standard deviation corresponding to all the health status evaluation parameters comprises:

determining the difference value between the health state evaluation parameter of each device and the first mean value to obtain a target error value corresponding to each device;

and if the ratio of the target error value corresponding to the target device to the first standard deviation in each device is greater than or equal to a Grabas critical value, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter, wherein the Grabas critical value is related to a preset significance level coefficient and the number of all the health state evaluation parameters.

5. The method of claim 1, wherein determining whether there is a suspect parameter among all health assessment parameters for the same class of devices comprises:

determining a second mean value and a second standard deviation corresponding to remaining health state evaluation parameters in all the health state evaluation parameters, wherein the remaining health state evaluation parameters are all the health state evaluation parameters except the health state evaluation parameters corresponding to the target device in all the health state evaluation parameters;

determining a difference absolute value between the health state evaluation parameter corresponding to the target device and the second mean value to obtain a second absolute error corresponding to the target device;

and if the ratio of the second absolute error corresponding to the target device to the second standard deviation is greater than or equal to a Romanofsky check coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter, wherein the Romanofsky check coefficient is related to a preset significance level coefficient and the number of all the health state evaluation parameters.

6. The method of claim 1, wherein determining whether there is a suspect parameter among all health assessment parameters for the same class of devices comprises:

c. determining whether the statistical value is greater than or equal to a Dike's threshold corresponding to the statistical value, the Dike's threshold being related to a preset significance level coefficient and the number of all health status assessment parameters;

d. if the statistic value is larger than or equal to the Dike critical value corresponding to the statistic value, determining that the health state evaluation parameter corresponding to the extreme value is a suspicious parameter, and removing the extreme value from all the health state evaluation parameters;

7. The method of claim 1, wherein determining whether there is a suspect parameter among all health assessment parameters for the same class of devices comprises:

determining a corresponding suspicious parameter diagnosis mode according to the quantity of all health state evaluation parameters;

8. The method of claim 1, wherein determining whether there is a suspect parameter among all health assessment parameters for the same class of devices comprises:

selecting more than two suspicious parameter diagnosis modes to determine whether suspicious parameters exist in all health state evaluation parameters of the same type of device;

if more than two suspicious parameter diagnosis modes determine that the target health state evaluation parameter is a suspicious parameter, determining that the target health state evaluation parameter is a suspicious parameter;

and if the diagnosis results of more than two suspicious parameter diagnosis modes are inconsistent, acquiring the health state evaluation parameters of each device again, and diagnosing the suspicious parameters again.

9. The method according to any of claims 1-8, wherein if the suspect parameter is not present, the method further comprises:

comparing each health status assessment parameter of the all health status assessment parameters with a corresponding limit parameter;

10. The method according to any one of claims 1-8, wherein the health assessment parameter is a temperature of the device, a resistance across the device, or a voltage difference across the device.

11. The method of any one of claims 1-8, wherein all health assessment parameters of the same type of device are health assessment parameters collected at the same time.

12. A device diagnostic platform, comprising:

the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring health state evaluation parameters of devices of the same type of equipment in real time, and the health state evaluation parameters are associated with the current state of the devices;

the determining module is used for determining whether suspicious parameters exist in all the health state evaluation parameters of the same type of device, wherein the suspicious parameters are parameters with preset errors with other parameters of all the health state evaluation parameters;

13. A device diagnostic system comprising a plurality of homogeneous device types and a device diagnostic platform;

the multiple devices of the same type are used for uploading the health state evaluation parameters of the devices of the same type to the device diagnosis platform in real time;

the device diagnostic platform for implementing the device diagnostic method according to any one of claims 1 to 11, or for implementing the functionality of the device diagnostic platform according to claim 12.

14. A readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the device diagnostic method according to any one of claims 1 to 11 or implements the functionality of the device diagnostic platform according to claim 12.