Magnetic resonance imaging component fault positioning method and device and magnetic resonance imaging system

Abstract

The embodiment of the invention discloses a magnetic resonance imaging component fault positioning method and device and a magnetic resonance imaging system. The method comprises the following steps: acquiring parameter values of parameters to be monitored of MRI parts to be monitored and association among the monitoring parameters, and storing the parameter values and the association in a historical database; when it is detected that one MRI component breaks down, a first parameter directly related to the fault is obtained; the method comprises the following steps of: acquiring all levels of related parameters of a first parameter according to correlation among all monitoring parameters, searching an upper-level parameter which is most related to the first parameter according to a parameter value set of the first parameter acquired from a historical database, and searching an upper-level parameter which is most related to the upper-level parameter according to a parameter value set of the upper-level parameter, so as to obtain a second-level parameter which is most related to the upper-level parameter; and the most relevant parameters of the last level are found, and the most relevant parameters of the last level are used as the fault root parameters of the MRI component. According to the embodiment of the invention, automatic fault positioning of the MRI component is realized.

Description

Technical Field

[0001] The present invention relates to the field of MRI (Magnetic Resonance Imaging) technology, in particular to a method and device for locating faults of MRI components and an MRI system. Background Art

[0002] MRI systems include various hardware components, such as magnet monitoring units, cooling components, and radio frequency components. Due to various reasons, components may fail or be damaged, such as magnet quenching or field drop. Currently, the fault can only be located manually after the component fails or is damaged, and then repair or replacement measures are taken. The disadvantages of this are: low fault location efficiency; in addition, if each failure cannot be predicted in advance and corresponding fault prevention measures cannot be taken in advance, the MR (Magnetic Resonance) examination process will always be interrupted, reducing the efficiency of MR imaging and the patient experience; in addition, in the component development process, components can only be developed based on experience and limited unit testing, which will lead to insufficient stability of the developed components. Summary of the invention

[0003] In view of this, the embodiments of the present invention propose, on the one hand, an MRI component fault location method and device to achieve automatic fault location of MRI components and improve the MR imaging work efficiency; on the other hand, an MRI system is proposed to achieve automatic fault location of MRI components and improve the MR imaging work efficiency.

[0004] A method for locating a fault in an MRI component, the method comprising:

[0005] Obtaining parameter values ​​of each parameter to be monitored of each MRI component to be monitored and the relationship between each monitoring parameter and saving them in a historical database;

[0006] When a fault of an MRI component is detected, obtaining a first parameter directly related to the fault;

[0007] According to the association between the various monitoring parameters, the related parameters of the first parameter at various levels are obtained, and according to the parameter value set of the first parameter obtained from the historical database, the upper-level parameter most related to the first parameter is found, and according to the parameter value set of the upper-level parameter, the upper-level parameter most related to the upper-level parameter is found, until the last most related parameter is found, and the last most related parameter is used as the fault root cause parameter of the MRI component.

[0008] The step of searching for the upper level parameter most relevant to the first parameter according to the parameter value set of the first parameter obtained from the historical database includes:

[0009] According to the parameter value sets of the first parameter obtained from the historical database and the parameter value sets of each upper-level parameter related to the first parameter, calculate the correlation coefficients of the first parameter and each upper-level parameter respectively; find the maximum correlation coefficient among the calculated correlation coefficients, and use the upper-level parameter corresponding to the maximum correlation coefficient as the upper-level parameter most relevant to the first parameter.

[0010] The finding of the upper-level parameter most relevant to the upper-level parameter according to the parameter value set of the upper-level parameter includes:

[0011] According to the parameter value set of the upper-level parameter obtained from the historical database and the parameter value sets of each upper-level parameter related to the upper-level parameter, calculate the correlation coefficients of the upper-level parameter and each upper-level parameter respectively; find the maximum correlation coefficient among the calculated correlation coefficients, and use the upper-level parameter corresponding to the maximum correlation coefficient as the upper-level parameter most relevant to the upper-level parameter.

[0012] After using the last-level most relevant parameter as the fault root cause parameter of the MRI component, it further includes:

[0013] According to the parameter value set of the fault root cause parameter and the parameter value set of the first parameter obtained from the historical database, establish the association between the parameter value of the fault root cause parameter and the parameter value of the first parameter;

[0014] When the parameter value set of the fault root cause parameter within the most recent first time period is obtained, use the association between the parameter value of the fault root cause parameter and the parameter value of the first parameter to calculate the predicted value set of the first parameter within the future second time period corresponding to the parameter value set of the fault root cause parameter within the most recent first time period, and judge whether the MRI component will fail within the future second time period according to the predicted value set of the first parameter within the future second time period. If so, issue a fault warning.

[0015] The establishment of the association between the parameter value of the fault root cause parameter and the parameter value of the first parameter includes:

[0016] Establish a linear regression model or an exponential regression model between the parameter value of the fault root cause parameter and the parameter value of the first parameter.

[0017] The judgment of whether the MRI component will fail within the future second time period according to the predicted value set of the first parameter within the future second time period includes:

[0018] For each predicted value in the predicted value set of the first parameter within the future second time period, judge whether the predicted value is within the set fault range. If so, determine that the MRI component will fail at the moment corresponding to the predicted value.

[0019] The fault warning carries the moment when the failure will occur.

[0020] After the above and taking the last - level most relevant parameter as the fault - root parameter of the MRI component, it further includes:

[0021] When the parameter - value set of the fault - root parameter within the most recent first time period is obtained, calculate the variance of the parameter - value set of the fault - root parameter within the most recent first time period, and determine whether the variance is greater than the preset variance threshold. If so, it is determined that the MRI component will fail, and a fault warning is issued.

[0022] The obtaining of the parameter values of the parameters to be monitored for each MRI component to be monitored includes:

[0023] When it is monitored that the current monitoring trigger condition for a parameter to be monitored of an MRI component is satisfied, obtain the parameter value of the parameter to be monitored from this MRI component;

[0024] The monitoring trigger condition is: triggered periodically at a preset time interval, or triggered at a set time point, or triggered when a set event occurs.

[0025] The set event is: the power - on event of the MRI system, or the event that the MRI system has started up completely, or the event that a patient starts to register on the host of the MRI system, or the event that the hospital bed starts to move towards the magnet center of the MRI system, or the event that the hospital bed has moved to the magnet center of the MRI system and is about to start scanning.

[0026] The obtaining of the parameter value of the parameter to be monitored from the MRI component when it is monitored that the current monitoring trigger condition for a parameter to be monitored of an MRI component is satisfied includes:

[0027] When it is monitored that the current monitoring trigger condition for a parameter to be monitored of an MRI component is satisfied, construct a monitoring command including the name of the MRI component and the name of the parameter to be monitored, send the monitoring command to the peripheral control unit, and receive the parameter value of the parameter to be monitored of this MRI component sent by the peripheral control unit;

[0028] Wherein, after receiving the monitoring command, the peripheral control unit parses the name of the MRI component and the name of the parameter to be monitored from the monitoring command, and obtains the parameter value of the parameter to be monitored from this MRI component through the interface between itself and this MRI component.

[0029] The obtaining of the parameter value of the parameter to be monitored from the MRI component when it is monitored that the current monitoring trigger condition for a parameter to be monitored of an MRI component is satisfied includes:

[0030] When it is known from the monitoring information of the configuration file that the monitoring triggering mode of a parameter to be monitored of an MRI component is: periodically triggered according to a preset time interval, then when it is monitored that the current moment reaches the periodic triggering moment, the parameter value of the parameter to be monitored is obtained from the MRI component; or,

[0031] When it is known from the monitoring information of the configuration file that the monitoring triggering mode of a parameter to be monitored of an MRI component is: when triggered at a set time point, when it is monitored that the current moment reaches the set time point, the parameter value of the parameter to be monitored is obtained from the MRI component; or,

[0032] When an event is detected, the monitoring trigger mode matching the event is searched in the monitoring information of the configuration file. If found, the parameter value of the parameter to be monitored is obtained from the MRI component according to the name of the MRI component corresponding to the event and the name of the parameter to be monitored in the monitoring information of the configuration file.

[0033] The configuration file stores monitoring information, including: the name of each MRI component to be monitored, the name of each parameter to be monitored of each MRI component, and the monitoring triggering method of each parameter to be monitored of each MRI component.

[0034] An MRI component fault location device, the device comprising:

[0035] A parameter acquisition module is used to obtain the parameter values ​​of each parameter to be monitored of each MRI component to be monitored and the relationship between each monitoring parameter and save them in a historical database;

[0036] The fault location module is used to obtain a first parameter directly related to a fault when a fault is detected in an MRI component; obtain related parameters of each level of the first parameter according to the association between the monitoring parameters, find the upper level parameter most related to the first parameter according to the parameter value set of the first parameter obtained from the historical database, and find the upper level parameter most related to the upper level parameter according to the parameter value set of the upper level parameter, until the last level most related parameter is found, and the last level most related parameter is used as the fault root parameter of the MRI component.

[0037] The fault location module searches for the upper level parameter most relevant to the first parameter according to the parameter value set of the first parameter obtained from the history database, including:

[0038] Calculate the correlation coefficients between the first parameter and the respective upper-level parameters according to the parameter value set of the first parameter obtained from the historical database and the parameter value sets of the respective upper-level parameters related to the first parameter; find the maximum correlation coefficient among the calculated correlation coefficients, and use the upper-level parameter corresponding to the maximum correlation coefficient as the upper-level parameter most correlated with the first parameter;

[0039] The fault location module searches for the upper - level parameter most relevant to the upper - level parameter according to the parameter value set of the upper - level parameter, including:

[0040] According to the parameter value set of the upper - level parameter obtained from the historical database and the parameter value sets of each upper - level parameter related to the upper - level parameter, calculate the correlation coefficients between the upper - level parameter and each of the upper - level parameters; find the maximum correlation coefficient among the calculated correlation coefficients, and use the upper - level parameter corresponding to the maximum correlation coefficient as the upper - level parameter most relevant to the upper - level parameter.

[0041] The device further includes: an association module, configured to establish an association between the parameter value of the fault root cause parameter and the parameter value of the first parameter according to the parameter value set of the fault root cause parameter and the parameter value set of the first parameter obtained from the historical database.

[0042] The association module establishing the association between the parameter value of the fault root cause parameter and the parameter value of the first parameter includes:

[0043] Establish a linear regression model or an exponential regression model between the parameter value of the fault root cause parameter and the parameter value of the first parameter.

[0044] The device further includes: a fault prediction module, configured to, when obtaining the parameter value set of the fault root cause parameter within the most recent first time period, use the association between the parameter value of the fault root cause parameter and the parameter value of the first parameter to calculate the predicted value set of the first parameter within the next second time period corresponding to the parameter value set of the fault root cause parameter within the most recent first time period, and determine whether the MRI component will fail within the next second time period according to the predicted value set of the first parameter within the next second time period. If so, issue a fault warning.

[0045] The fault prediction module determining whether the MRI component will fail within the next second time period according to the predicted value set of the first parameter within the next second time period includes:

[0046] For each predicted value in the predicted value set of the first parameter within the next second time period, determine whether the predicted value is within the set fault range. If so, determine that the MRI component will fail at the moment corresponding to the predicted value.

[0047] The fault prediction module is further configured to calculate the variance of the parameter value set of the fault root cause parameter within the most recent first time period, and determine whether the variance is greater than the preset variance threshold. If so, determine that the MRI component will fail and issue a fault warning.

[0048] The parameter acquisition module acquires the parameter values of each parameter to be monitored for each MRI component to be monitored, including:

[0049] When it is detected that the current monitoring trigger condition for a monitored parameter of an MRI component is satisfied, obtain the parameter value of the monitored parameter from the MRI component; the monitoring trigger condition is: triggered periodically at a preset time interval, or triggered at a set time point, or triggered when a set event occurs.

[0050] When the parameter acquisition module detects that the current monitoring trigger condition for a monitored parameter of an MRI component is satisfied, obtain the parameter value of the monitored parameter from the MRI component, including:

[0051] When it is detected that the current monitoring trigger condition for a monitored parameter of an MRI component is satisfied, construct a monitoring command including the name of the MRI component and the name of the monitored parameter, send the monitoring command to the peripheral control unit, and receive the parameter value of the monitored parameter of the MRI component sent by the peripheral control unit; wherein, after receiving the monitoring command, the peripheral control unit parses the name of the MRI component and the name of the monitored parameter from the monitoring command, and obtains the parameter value of the monitored parameter from the MRI component through the interface between itself and the MRI component.

[0052] When the parameter acquisition module detects that the current monitoring trigger condition for a monitored parameter of an MRI component is satisfied, obtaining the parameter value of the monitored parameter from the MRI component includes:

[0053] When it is known from the monitoring information in the configuration file that the monitoring trigger mode for a monitored parameter of an MRI component is: triggered periodically at a preset time interval, then when it is detected that the current time reaches the periodic trigger time, obtain the parameter value of the monitored parameter from the MRI component;

[0054] Or, when it is known from the monitoring information in the configuration file that the monitoring trigger mode for a monitored parameter of an MRI component is: triggered at a set time point, then when it is detected that the current time reaches the set time point, obtain the parameter value of the monitored parameter from the MRI component;

[0055] Or, when an event is detected, search for the monitoring trigger mode matching the event in the monitoring information in the configuration file. If found, obtain the parameter value of the monitored parameter from the MRI component according to the name of the MRI component and the name of the monitored parameter corresponding to the event in the monitoring information in the configuration file;

[0056] Among them, the configuration file stores monitoring information, including: the names of the MRI components to be monitored, the names of the monitored parameters of each MRI component, and the monitoring trigger modes of the monitored parameters of each MRI component.

[0057] An MRI system, which includes the MRI component fault location device described in any of the above.

[0058] The MRI system further includes: a peripheral control unit, which has interfaces connected to each MRI component to be monitored and an interface connected to the MRI component fault location device;

[0059] The peripheral control unit is configured to receive a monitoring command sent by the MRI component fault location device, parse the name of the MRI component and the name of the parameter to be monitored from the monitoring command, obtain the parameter value of the parameter to be monitored from the MRI component through its own interface with the MRI component, and send the parameter value of the parameter to be monitored of the MRI component to the MRI component fault location device.

[0060] In an embodiment of the present invention, by obtaining the parameter values of the parameters to be monitored of each MRI component to be monitored and the associations between the monitoring parameters, when it is detected that an MRI component fails, the first parameter directly related to the failure is obtained; according to the associations between the monitoring parameters, the relevant parameters at all levels of the first parameter are obtained, and according to the parameter value set of the first parameter obtained from the historical database, the upper-level parameter most relevant to the first parameter is found, and according to the parameter value set of the upper-level parameter, the upper-upper-level parameter most relevant to the upper-level parameter is found until the last-level most relevant parameter is found, and the last-level most relevant parameter is used as the fault root parameter of the MRI component, thereby realizing the automatic location of the MRI component fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The following will make the above and other features and advantages of the present invention clearer to those of ordinary skill in the art by describing the preferred embodiments of the present invention in detail with reference to the drawings, where:

[0062] Figure 1 is a flowchart of a method for locating faults of MRI components provided by an embodiment of the present invention;

[0063] Figure 2 is a flowchart of a method for locating faults of MRI components provided by another embodiment of the present invention;

[0064] Figure 3 is a flowchart of a method for obtaining the parameter values of the parameters to be monitored of each MRI component to be monitored provided by an embodiment of the present invention;

[0065] Figure 4 is a schematic structural diagram of an MRI component fault location device provided by an embodiment of the present invention;

[0066] Figure 5 is a schematic structural diagram of an MRI system provided by an embodiment of the present invention.

[0067] Among them, the attached drawing reference numerals are as follows:

[0068]

[0069] Detailed implementation manners

[0070] To make the objectives, technical solutions and advantages of the present invention clearer, the following examples are given to further elaborate on the present invention in detail.

[0071] Figure 1 The following is a flowchart of a method for fault location of MRI components provided by an embodiment of the present invention, and the specific steps are as follows:

[0072] Step 101: Obtain the parameter values of the monitored parameters of each MRI component to be monitored and the associations between the monitored parameters, and save them in the historical database.

[0073] In an optional embodiment, obtaining the parameter values of the monitored parameters of each MRI component to be monitored includes: when it is detected that the current monitoring trigger condition for a monitored parameter of an MRI component is satisfied, obtaining the parameter value of the monitored parameter from the MRI component; the monitoring trigger condition is: being periodically triggered at a preset time interval, or being triggered at a set time point, or being triggered when a set event occurs, and so on.

[0074] In an optional embodiment, the set event is: the power-on event of the MRI system, or the event that the MRI system has started up completely, or the event that a patient starts to register on the host of the MRI system, or the event that the hospital bed starts to move towards the magnet center of the MRI system, or the event that the hospital bed has moved to the magnet center of the MRI system and is about to start scanning, and so on.

[0075] In an optional embodiment, when it is detected that the current monitoring trigger condition for a monitored parameter of an MRI component is satisfied, obtaining the parameter value of the monitored parameter from the MRI component includes: when it is detected that the current monitoring trigger condition for a monitored parameter of an MRI component is satisfied, constructing a monitoring command including the name of the MRI component and the name of the monitored parameter, sending the monitoring command to the peripheral control unit, and receiving the parameter value of the monitored parameter of the MRI component sent by the peripheral control unit; wherein, after receiving the monitoring command, the peripheral control unit parses the name of the MRI component and the name of the monitored parameter from the monitoring command, and obtains the parameter value of the monitored parameter from the MRI component through the interface between itself and the MRI component.

[0076] In an optional embodiment, when it is monitored that a monitoring trigger condition of a parameter to be monitored of an MRI component is currently met, obtaining a parameter value of the parameter to be monitored from the MRI component includes: when it is known from the monitoring information of the configuration file that a monitoring trigger mode of a parameter to be monitored of an MRI component is: periodically triggered at a preset time interval, then when it is monitored that the current moment reaches the periodic trigger moment, then obtaining the parameter value of the parameter to be monitored from the MRI component; or, when it is known from the monitoring information of the configuration file that a monitoring trigger mode of a parameter to be monitored of an MRI component is: triggered at a set time point, then when the monitoring When the current moment reaches the set time point, the parameter value of the parameter to be monitored is obtained from the MRI component; when an event is detected, the monitoring trigger method matching the event is searched in the monitoring information of the configuration file. If found, the parameter value of the parameter to be monitored is obtained from the MRI component according to the name of the MRI component corresponding to the event and the name of the parameter to be monitored in the monitoring information of the configuration file; wherein the configuration file stores the monitoring information, including: the name of each MRI component to be monitored, the name of each parameter to be monitored of each MRI component, and the monitoring trigger method of each parameter to be monitored of each MRI component.

[0077] Step 102: When a fault of an MRI component is detected, a first parameter directly related to the fault is obtained.

[0078] When an MRI component fails, the parameter values ​​of the monitoring parameters of the MRI component can be queried to determine which monitoring parameter's parameter value exceeds the normal range, thereby determining the first parameter directly related to the failure.

[0079] Step 103: According to the association between each monitoring parameter, obtain the related parameters of each level of the first parameter, find the upper level parameter most related to the first parameter according to the parameter value set of the first parameter obtained from the historical database, and find the upper level parameter most related to the upper level parameter according to the parameter value set of the upper level parameter, until the last level most related parameter is found, and the last level most related parameter is used as the fault root cause parameter of the MRI component.

[0080] The fault root parameter and the first parameter may belong to the same MRI component, or may belong to different MRI components.

[0081] For example, the association between the monitoring parameters is defined as follows: the first parameter is related to parameters a and b, parameter a is related to parameters a1 and a2, and parameter b is related to parameters b1 and b2. Then the first parameter's related parameters at each level are: a, b; a1, a2, b1, b2. Among them, a and b are the first parameter's upper level parameters, a1 and a2 are a's upper level parameters, and b1 and b2 are b's upper level parameters.

[0082] In an alternative embodiment, to find the upper-level parameter most relevant to the first parameter according to the set of parameter values of the first parameter obtained from the historical database, the method includes: calculating the correlation coefficients between the first parameter and each upper-level parameter relevant to the first parameter respectively according to the set of parameter values of the first parameter obtained from the historical database and the sets of parameter values of each upper-level parameter relevant to the first parameter; finding the maximum correlation coefficient among the calculated correlation coefficients, and taking the upper-level parameter corresponding to the maximum correlation coefficient as the upper-level parameter most relevant to the first parameter.

[0083] In an alternative embodiment, to find the upper-upper-level parameter most relevant to the upper-level parameter according to the set of parameter values of the upper-level parameter, the method includes: calculating the correlation coefficients between the upper-level parameter and each upper-upper-level parameter relevant to the upper-level parameter respectively according to the set of parameter values of the upper-level parameter obtained from the historical database and the sets of parameter values of each upper-upper-level parameter relevant to the upper-level parameter; finding the maximum correlation coefficient among the calculated correlation coefficients, and taking the upper-upper-level parameter corresponding to the maximum correlation coefficient as the upper-upper-level parameter most relevant to the upper-level parameter.

[0084] Among them, the correlation coefficient can be the Pearson correlation coefficient, etc.

[0085] For example: when it is detected that a Radio Frequency Power Amplifier (RFPA) fails and the direct cause of the failure is that the voltage of the RFPA exceeds the normal voltage range, then according to the first-level relevant parameters of the voltage of the RFPA defined in advance: the temperature of the RFPA and the voltage of the voltage stabilizer power supply, the historical voltage value set, historical temperature value set of the RFPA, and historical voltage value set of the voltage stabilizer power supply for a preset duration are obtained from the historical database. Calculate the correlation coefficient between the historical voltage value set of the RFPA and the historical temperature value set, and the correlation coefficient between the historical voltage value set of the RFPA and the historical voltage value set of the voltage stabilizer power supply. Take the larger value of the two correlation coefficients. It is found that the correlation coefficient between the historical voltage value set of the RFPA and the historical temperature value set is larger. Then it is considered that the temperature of the RFPA is the first-level parameter most relevant to the voltage of the RFPA. Then continue to find the relevant parameters of the temperature of the RFPA defined in advance, and calculate the correlation coefficients between the historical temperature value set of the RFPA and the historical parameter value sets of each relevant parameter respectively. Take the maximum correlation coefficient among the calculated correlation coefficients, and take the relevant parameter corresponding to the maximum correlation coefficient as the second-level parameter most relevant to the voltage of the RFPA (i.e., the upper-level parameter most relevant to the temperature of the RFPA). Repeat this process until the last-level parameter most relevant to the RFPA, that is, the fault root cause parameter of the MRI component, is found.

[0086] In the above embodiments, by obtaining the parameter values of the monitored parameters of each MRI component to be monitored and the associations between the monitored parameters, when a failure of an MRI component is detected, a first parameter directly related to the failure is obtained; according to the associations between the monitored parameters, the relevant parameters at all levels of the first parameter are obtained, and according to the set of parameter values of the first parameter obtained from the historical database, the upper-level parameter most relevant to the first parameter is found, and according to the set of parameter values of the upper-level parameter, the upper-upper-level parameter most relevant to the upper-level parameter is found until the last-level most relevant parameter is found, and the last-level most relevant parameter is used as the root cause parameter of the failure of the MRI component, thus realizing the automatic positioning of the failure of the MRI component.

[0087] Figure 2 The flowchart of the fault location method provided by another embodiment of the present invention is as follows:

[0088] Step 201: Obtain the parameter values of the monitored parameters of each MRI component to be monitored and the associations between the monitored parameters and save them in the historical database.

[0089] Step 202: When a failure of an MRI component is detected, obtain a first parameter directly related to the failure.

[0090] Step 203: According to the associations between the monitored parameters, obtain the relevant parameters at all levels of the first parameter. According to the set of parameter values of the first parameter obtained from the historical database, find the upper-level parameter most relevant to the first parameter, and according to the set of parameter values of the upper-level parameter, find the upper-upper-level parameter most relevant to the upper-level parameter until the last-level most relevant parameter is found, and use the last-level most relevant parameter as the root cause parameter of the failure of the MRI component.

[0091] Step 204: According to the set of parameter values of the root cause parameter and the set of parameter values of the first parameter obtained from the historical database, establish an association between the parameter value of the root cause parameter and the parameter value of the first parameter.

[0092] For example: Obtain the set of parameter values of the root cause parameter in the first time period and the set of parameter values of the first parameter in the first time period from the historical database. The selection of the first time period can be set according to experience, etc., or the first time period closest to the current time can be directly selected.

[0093] Step 205: When the parameter value set of the fault root cause parameter within the most recent first time period is obtained, calculate the predicted value set of the first parameter within the next second time period corresponding to the parameter value set of the fault root cause parameter within the most recent first time period by using the correlation between the parameter value of the fault root cause parameter and the parameter value of the first parameter. Determine whether the MRI component will fail within the next second time period according to the predicted value set of the first parameter within the next second time period. If so, issue a fault warning.

[0094] Among them, the values of the first time period and the second time period can be set according to actual needs. For example, if it is desired to predict the fault situation of the MRI component within the next week, then the second time period is taken as within the next week; the longer the length of the first time period, the higher the fault prediction accuracy. In practical applications, the length of the first time period can be comprehensively determined according to the computing power of the device running the method of the embodiment of the present invention and in combination with the required fault prediction accuracy.

[0095] In an alternative embodiment, establishing the correlation between the parameter value of the fault root cause parameter and the parameter value of the first parameter includes: establishing a linear regression model or an exponential regression model between the parameter value of the fault root cause parameter and the parameter value of the first parameter.

[0096] The linear regression model and the exponential regression model are mature models, and their establishment methods are mature technologies and will not be elaborated here.

[0097] In an alternative embodiment, determining whether the MRI component will fail within the next second time period according to the predicted value set of the first parameter within the next second time period includes: for each predicted value in the predicted value set of the first parameter within the next second time period, determine whether the predicted value is within the set fault range. If so, determine that the MRI component will fail at the moment corresponding to the predicted value. Among them, the set fault range can be found in the technical manual of the MRI component.

[0098] In an alternative embodiment, the fault warning carries the moment when the failure will occur.

[0099] In the above embodiments, after obtaining the fault root cause parameters of the MRI component, an association is established between the parameter value set of the fault root cause parameters obtained from the historical database and the parameter value set of the first parameter. Then, when the parameter value set of the fault root cause parameters within the most recent first time period is obtained, the association between the parameter value of the fault root cause parameter and the parameter value of the first parameter is used to calculate the predicted value set of the first parameter within the next second time period corresponding to the parameter value set of the fault root cause parameter within the most recent first time period. According to the predicted value set of the first parameter within the next second time period, it is determined whether the MRI component will fail within the next second time period. If so, a fault warning is issued, thereby enabling fault prediction of the MRI component, giving a warning before the fault occurs, facilitating maintenance personnel to locate the fault in a timely manner and eliminate the fault before the fault occurs, improving the working efficiency of MR imaging, and enhancing the patient experience. At the same time, if the above embodiments are applied during the development of an MRI component, only a small number of such MRI components need to be developed to monitor and predict the performance of the MRI component, thus not only shortening the development time, improving the development efficiency, but also enhancing the stability of the developed MRI component.

[0100] In an alternative embodiment, after step 103 or step 203, it may further include: when the parameter value set of the fault root cause parameter within the most recent first time period is obtained, calculate the variance of the parameter value set of the fault root cause parameter within the most recent first time period, and determine whether the variance is greater than a preset variance threshold. If so, it is determined that the MRI component will fail, and a fault warning is issued. Among them, the preset variance threshold can be found in the technical manual of the MRI component corresponding to the fault root cause parameter.

[0101] Figure 1 and Figure 2 The execution subject of the illustrated embodiment may be a processor of the MRI system.

[0102] Figure 3 The following is a flowchart of a method for obtaining the parameter values of the parameters to be monitored for each MRI component provided by an embodiment of the present invention, and the specific steps are as follows:

[0103] Step 301: Pre-configure monitoring information in the MRI system, including: the names of the MRI components to be monitored, the names of the parameters to be monitored for each MRI component, and the monitoring trigger methods for the parameters to be monitored for each MRI component. Save the monitoring information to a configuration file, and save the configuration file to the memory of the MRI system.

[0104] The parameters to be monitored for the MRI component are, for example: the peak temperature, peak voltage, etc. of the RFPA. The RFPA automatically calculates the peak temperature and peak voltage within a certain time period at regular intervals.

[0105] The monitoring trigger methods are as follows: periodic triggering according to a preset time interval, or triggering at a set time point, or triggering when a set event occurs. Among them, the set time points are such as: every Wednesday, or Monday of the third week of each month, etc.; the set events are such as: the power-on event of the MRI system, or the event that the MRI system has finished starting up, or the event that a patient starts to register on the host of the MRI system, or the event that the hospital bed starts to move towards the magnet center of the MRI system, or the event that the hospital bed has moved to the magnet center of the MRI system and is about to start scanning, etc.

[0106] Step 302: When the processor of the MRI system learns from the monitoring information in the configuration file that the monitoring trigger method of a monitored parameter of an MRI component is: periodic triggering according to a preset time interval, then when it is monitored that the current time reaches the periodic triggering time, a monitoring command is constructed. This monitoring command carries the name of the MRI component and the name of the parameter to be detected, and the monitoring command is put into the monitoring queue.

[0107] In practical applications, if the monitoring trigger method further includes the trigger duration for each trigger, the monitoring command further includes the monitoring duration, and the monitoring duration is equal to the trigger duration for each trigger.

[0108] Step 303: When the processor of the MRI system learns from the monitoring information in the configuration file that the monitoring trigger method of a monitored parameter of an MRI component is: triggering at a set time point, then when it is monitored that the current time reaches the set time point, a monitoring command is constructed. This monitoring command carries the name of the MRI component and the name of the parameter to be detected, and the monitoring command is put into the monitoring queue.

[0109] In practical applications, if the monitoring trigger method further includes the trigger duration for each trigger, the monitoring command further includes the monitoring duration, and the monitoring duration is equal to the trigger duration for each trigger. For example: if it is set to trigger every Wednesday, then this trigger method actually includes two trigger information, one is that the trigger start time point is 0:00 on Wednesday, and the other is that the trigger duration is 24 hours.

[0110] Step 304: When the processor of the MRI system detects that an event occurs, the event is put into the event queue; and, the event is read from the event queue in real time, and the monitoring trigger method matching the read event is searched for in the monitoring information of the configuration file. If found, a monitoring command is constructed. This monitoring command carries the name of the MRI component corresponding to the event in the monitoring information of the configuration file and the name of the parameter to be monitored.

[0111] In practical applications, if the monitoring trigger method matching the read event further includes the trigger duration for each trigger, the monitoring command further includes the monitoring duration, and the monitoring duration is equal to the trigger duration for each trigger.

[0112] Step 305: The processor of the MRI system reads the monitoring commands from the monitoring queue in real time and sends the read monitoring commands to the peripheral control unit of the MRI system.

[0113] Step 306: The peripheral control unit receives the monitoring commands, parses the name of the MRI component and the name of the parameter to be monitored from the monitoring commands, obtains the parameter value of the parameter to be monitored through its interface with the MRI component, and sends the name of the MRI component and the parameter value of the parameter to be monitored to the processor of the MRI system.

[0114] In practical applications, if the monitoring duration is carried in the monitoring commands, after receiving the monitoring commands, the peripheral control unit continuously obtains the parameter value of the parameter to be monitored through its interface with the MRI component within the monitoring duration, and sends the name of the MRI component and the parameter value of the parameter to be monitored to the processor of the MRI system.

[0115] Step 307: The processor of the MRI system receives the name of the MRI component and the parameter value of the parameter to be monitored, and saves the name of the MRI component and the parameter value of the parameter to be monitored into the historical database.

[0116] Figure 4 FIG. 40 is a schematic structural diagram of an MRI component fault location device 40 provided by an embodiment of the present invention. The device 40 mainly includes: a parameter acquisition module 41, a fault location module 42, an association module 43, and a fault prediction module 44, where:

[0117] The parameter acquisition module 41 is configured to acquire the parameter values of the parameters to be monitored of each MRI component to be monitored and the association between the monitoring parameters, and save them into the historical database.

[0118] The fault location module 42 is configured to, when detecting that an MRI component fails, acquire a first parameter directly related to the failure; according to the association between the monitoring parameters, acquire the relevant parameters at all levels of the first parameter, find the upper-level parameter most relevant to the first parameter according to the parameter value set of the first parameter obtained from the historical database, and find the upper-level parameter most relevant to the upper-level parameter according to the parameter value set of the upper-level parameter, until the last-level most relevant parameter is found, and use the last-level most relevant parameter as the fault root parameter of the MRI component.

[0119] The association module 43 is configured to establish an association between the parameter value of the fault root parameter and the parameter value of the first parameter according to the parameter value set of the fault root parameter and the parameter value set of the first parameter obtained from the historical database.

[0120] The fault prediction module 44 is configured to, when obtaining a set of parameter values of the fault root cause parameter within the most recent first time period, calculate a predicted value set of the first parameter within the next second time period corresponding to the set of parameter values of the fault root cause parameter within the most recent first time period by using the association between the parameter values of the fault root cause parameter and the parameter values of the first parameter, and determine whether the MRI component will fail within the next second time period according to the predicted value set of the first parameter within the next second time period. If so, a fault warning is issued.

[0121] In an optional embodiment, the fault location module 42 searches for the upper-level parameter most relevant to the first parameter according to the set of parameter values of the first parameter obtained from the historical database, including: calculating the correlation coefficients between the first parameter and each of the upper-level parameters related to the first parameter respectively according to the set of parameter values of the first parameter obtained from the historical database and the set of parameter values of each of the upper-level parameters related to the first parameter; searching for the maximum correlation coefficient among the calculated correlation coefficients, and taking the upper-level parameter corresponding to the maximum correlation coefficient as the upper-level parameter most relevant to the first parameter.

[0122] In an optional embodiment, the fault location module 42 searches for the upper-upper-level parameter most relevant to the upper-level parameter according to the set of parameter values of the upper-level parameter, including: calculating the correlation coefficients between the upper-level parameter and each of the upper-upper-level parameters related to the upper-level parameter respectively according to the set of parameter values of the upper-level parameter obtained from the historical database and the set of parameter values of each of the upper-upper-level parameters related to the upper-level parameter; searching for the maximum correlation coefficient among the calculated correlation coefficients, and taking the upper-upper-level parameter corresponding to the maximum correlation coefficient as the upper-upper-level parameter most relevant to the upper-level parameter.

[0123] In an optional embodiment, the association module 43 establishes the association between the parameter values of the fault root cause parameter and the parameter values of the first parameter, including: establishing a linear regression model or an exponential regression model between the parameter values of the fault root cause parameter and the parameter values of the first parameter.

[0124] In an optional embodiment, the fault prediction module 44 determines whether the MRI component will fail within the next second time period according to the predicted value set of the first parameter within the next second time period, including: for each predicted value in the predicted value set of the first parameter within the next second time period, determining whether the predicted value is within the set fault range. If so, it is determined that the MRI component will fail at the moment corresponding to the predicted value.

[0125] In an optional embodiment, the fault warning issued by the fault prediction module 44 carries the moment when the failure will occur.

[0126] In an alternative embodiment, the fault prediction module 44 is further configured to calculate the variance of the set of parameter values of the fault root cause parameters within the most recent first time period, determine whether the variance is greater than a preset variance threshold, and if so, determine that the MRI component will fail and issue a fault warning.

[0127] In an alternative embodiment, the parameter acquisition module 41 acquires the parameter values of the parameters to be monitored for each MRI component to be monitored, including: when it is detected that the current monitoring trigger condition for a parameter to be monitored of an MRI component is satisfied, acquiring the parameter value of the parameter to be monitored from the MRI component; the monitoring trigger condition is: being periodically triggered at a preset time interval, or being triggered at a set time point, or being triggered when a set event occurs.

[0128] In an alternative embodiment, when the parameter acquisition module 41 detects that the current monitoring trigger condition for a parameter to be monitored of an MRI component is satisfied, acquiring the parameter value of the parameter to be monitored from the MRI component includes: when it is detected that the current monitoring trigger condition for a parameter to be monitored of an MRI component is satisfied, constructing a monitoring command including the name of the MRI component and the name of the parameter to be monitored, sending the monitoring command to the peripheral control unit, and receiving the parameter value of the parameter to be monitored of the MRI component sent by the peripheral control unit; wherein, after receiving the monitoring command, the peripheral control unit parses the name of the MRI component and the name of the parameter to be monitored from the monitoring command, and acquires the parameter value of the parameter to be monitored from the MRI component through the interface between itself and the MRI component.

[0129] In an alternative embodiment, when the parameter acquisition module 41 detects that the current monitoring trigger condition for a parameter to be monitored of an MRI component is satisfied, acquiring the parameter value of the parameter to be monitored from the MRI component includes: when it is known from the monitoring information in the configuration file that the monitoring trigger mode for a parameter to be monitored of an MRI component is: being periodically triggered at a preset time interval, then when it is detected that the current time reaches the periodic trigger time, acquiring the parameter value of the parameter to be monitored from the MRI component; or, when it is known from the monitoring information in the configuration file that the monitoring trigger mode for a parameter to be monitored of an MRI component is: being triggered at a set time point, then when it is detected that the current time reaches the set time point, acquiring the parameter value of the parameter to be monitored from the MRI component; or, when an event is detected, searching in the monitoring information in the configuration file for a monitoring trigger mode matching the event, and if found, acquiring the parameter value of the parameter to be monitored from the MRI component according to the name of the MRI component and the name of the parameter to be monitored corresponding to the event in the monitoring information in the configuration file; wherein, the configuration file stores monitoring information, including: the names of the MRI components to be monitored, the names of the parameters to be monitored for each MRI component, and the monitoring trigger modes for the parameters to be monitored for each MRI component.

[0130] Figure 5 This is a schematic structural diagram of the MRI system 50 provided by the embodiments of the present invention. As Figure 5 shown, the MRI system 50 includes the MRI component fault location device 40 described in any of the above embodiments.

[0131] In an alternative embodiment, the MRI system 50 further includes: a peripheral control unit 51, the peripheral control unit 51 having an interface connected to each MRI component to be monitored and having an interface connected to the MRI component fault location device 40;

[0132] The peripheral control unit 51 is configured to receive a monitoring command sent by the MRI component fault location device 40, parse out the name of the MRI component and the name of the parameter to be monitored from the monitoring command, obtain the parameter value of the parameter to be monitored from the MRI component through its own interface with the MRI component, and send the parameter value of the parameter to be monitored of the MRI component to the MRI component fault location device 40.

[0133] Those skilled in the art can understand that the features recited in the various embodiments and / or claims of the present disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly recited in the present application. In particular, without departing from the spirit and teachings of the present application, the features recited in the various embodiments and / or claims of the present application can be combined and / or combined in various ways, and all such combinations and / or combinations fall within the scope of the disclosure of the present application.

[0134] Specific embodiments are used herein to illustrate the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application, and is not used to limit the present application. For those skilled in the art, changes can be made in the specific implementation manners and application scopes according to the ideas, spirits and principles of the present application. Any modifications, equivalent replacements, improvements, etc. made by them shall be included within the scope of protection of the present application.

Claims

1. A method for locating faults in magnetic resonance imaging (MRI) components, It is characterized in that The method includes: Obtaining parameter values ​​of each parameter to be monitored of each MRI component to be monitored and the relationship between each monitoring parameter and saving them in a historical database; When a fault of an MRI component is detected, obtaining a first parameter directly related to the fault; According to the association between the various monitoring parameters, the related parameters of the first parameter at various levels are obtained, and according to the parameter value set of the first parameter obtained from the historical database, the upper-level parameter most related to the first parameter is found, and according to the parameter value set of the upper-level parameter, the upper-level parameter most related to the upper-level parameter is found, until the last most related parameter is found, and the last most related parameter is used as the fault root cause parameter of the MRI component.

2. The method according to claim 1, It is characterized in that The step of searching for the upper level parameter most relevant to the first parameter according to the parameter value set of the first parameter obtained from the historical database includes: According to the parameter value set of the first parameter obtained from the historical database and the parameter value sets of the upper-level parameters related to the first parameter, the correlation coefficients of the first parameter and the upper-level parameters are calculated respectively; the maximum correlation coefficient is found among the calculated correlation coefficients, and the upper-level parameter corresponding to the maximum correlation coefficient is used as the upper-level parameter most correlated with the first parameter.

3. The method according to claim 1 or 2, It is characterized in that The step of searching, based on the parameter value set of the previous level parameter, the next higher level parameter that is most relevant to the previous level parameter, includes: According to the parameter value set of the previous level parameter and the parameter value sets of the next previous level parameters related to the previous level parameter obtained from the historical database, the correlation coefficients of the previous level parameter and the next previous level parameters are calculated respectively; the maximum correlation coefficient is found among the calculated correlation coefficients, and the next previous level parameter corresponding to the maximum correlation coefficient is used as the next previous level parameter most correlated with the previous level parameter.

4. The method according to claim 1, It is characterized in that After taking the last level most relevant parameter as the fault root parameter of the MRI component, the method further comprises: Establishing an association between the parameter value of the fault root cause parameter and the parameter value of the first parameter according to the parameter value set of the fault root cause parameter and the parameter value set of the first parameter acquired from the historical database; When the parameter value set of the root cause parameter of the fault within the most recent first time period is obtained, the association between the parameter value of the root cause parameter and the parameter value of the first parameter is used to calculate the predicted value set of the first parameter in the future second time period corresponding to the parameter value set of the root cause parameter of the fault within the most recent first time period, and based on the predicted value set of the first parameter in the future second time period, it is determined whether the MRI component will fail in the future second time period; if so, a fault warning is issued.

5. The method according to claim 4, It is characterized in that The establishing of an association between the parameter value of the fault root cause parameter and the parameter value of the first parameter includes: A linear regression model or an exponential regression model is established between the parameter value of the fault root parameter and the parameter value of the first parameter.

6. The method according to claim 4, It is characterized in that judging whether the MRI component will fail within a future second time period according to the predicted value set of the first parameter within the future second time period includes: for each predicted value in the predicted value set of the first parameter within the future second time period, judging whether the predicted value is within a set failure range, and if so, determining that the MRI component will fail at the moment corresponding to the predicted value.

7. The method according to claim 6, It is characterized in that the fault alarm carries the moment when the failure will occur.

8. The method according to claim 4, It is characterized in that after using the last-level most relevant parameter as the fault root cause parameter of the MRI component, it further includes: when obtaining the parameter value set of the fault root cause parameter within the most recent first time period, calculating the variance of the parameter value set of the fault root cause parameter within the most recent first time period, judging whether the variance is greater than a preset variance threshold, and if so, determining that the MRI component will fail and sending a fault warning.

9. The method according to claim 1, It is characterized in that obtaining the parameter values of the parameters to be monitored of each MRI component to be monitored includes: when it is monitored that the current monitoring trigger condition of a parameter to be monitored of an MRI component is satisfied, obtaining the parameter value of the parameter to be monitored from the MRI component; the monitoring trigger condition is: triggering periodically at a preset time interval, or triggering at a set time point, or triggering when a set event occurs.

10. The method according to claim 9, It is characterized in that the set event is: the MRI system power-on event, or the MRI system startup completion event, or the patient starts to register on the host of the MRI system event, or the hospital bed starts to move towards the magnet center of the MRI system event, or the hospital bed has moved to the magnet center of the MRI system and is about to start scanning event.

11. The method according to claim 9 or 10, It is characterized in that when it is monitored that the current monitoring trigger condition of a parameter to be monitored of an MRI component is satisfied, obtaining the parameter value of the parameter to be monitored from the MRI component includes: when it is monitored that the current monitoring trigger condition of a parameter to be monitored of an MRI component is satisfied, constructing a monitoring command including the name of the MRI component and the name of the parameter to be monitored, sending the monitoring command to the peripheral control unit, and receiving the parameter value of the parameter to be monitored of the MRI component sent by the peripheral control unit; wherein, after receiving the monitoring command, the peripheral control unit parses the name of the MRI component and the name of the parameter to be monitored from the monitoring command, and obtains the parameter value of the parameter to be monitored from the MRI component through the interface between itself and the MRI component.

12. The method according to claim 10, It is characterized in that when it is monitored that the current monitoring trigger condition of a parameter to be monitored of an MRI component is satisfied, obtaining the parameter value of the parameter to be monitored from the MRI component includes: When it is known from the monitoring information of the configuration file that the monitoring triggering mode of a parameter to be monitored of an MRI component is: periodically triggered according to a preset time interval, then when it is monitored that the current moment reaches the periodic triggering moment, the parameter value of the parameter to be monitored is obtained from the MRI component; or, When it is known from the monitoring information of the configuration file that the monitoring triggering mode of a parameter to be monitored of an MRI component is: when triggered at a set time point, when it is monitored that the current moment reaches the set time point, the parameter value of the parameter to be monitored is obtained from the MRI component; or, When an event is detected, the monitoring trigger mode matching the event is searched in the monitoring information of the configuration file. If found, the parameter value of the parameter to be monitored is obtained from the MRI component according to the name of the MRI component corresponding to the event and the name of the parameter to be monitored in the monitoring information of the configuration file. The configuration file stores monitoring information, including: the name of each MRI component to be monitored, the name of each parameter to be monitored of each MRI component, and the monitoring triggering method of each parameter to be monitored of each MRI component.

13. A magnetic resonance imaging (MRI) component fault location device (40), It is characterized in that The device (40) comprises: A parameter acquisition module (41) is used to acquire the parameter values ​​of each parameter to be monitored of each MRI component to be monitored and the relationship between each monitoring parameter and save them in a history database; A fault location module (42) is used to obtain a first parameter directly related to a fault when a fault is detected in an MRI component; obtain related parameters of each level of the first parameter according to the association between the monitoring parameters; find the upper level parameter most related to the first parameter according to the parameter value set of the first parameter obtained from the historical database; and find the upper level parameter most related to the upper level parameter according to the parameter value set of the upper level parameter, until the last level most related parameter is found, and use the last level most related parameter as the fault root parameter of the MRI component.

14. The device (40) according to claim 13, It is characterized in that The fault location module (42) searches for the upper level parameter most relevant to the first parameter based on the parameter value set of the first parameter obtained from the historical database, including: Calculate the correlation coefficients between the first parameter and the respective upper-level parameters according to the parameter value set of the first parameter obtained from the historical database and the parameter value sets of the respective upper-level parameters related to the first parameter; find the maximum correlation coefficient among the calculated correlation coefficients, and use the upper-level parameter corresponding to the maximum correlation coefficient as the upper-level parameter most correlated with the first parameter; The fault location module (42) searches for a higher level parameter that is most relevant to the higher level parameter according to the parameter value set of the higher level parameter, including: According to the parameter value sets of the upper-level parameter obtained from the historical database and the parameter value sets of each upper-upper-level parameter related to the upper-level parameter, calculate the correlation coefficients of the upper-level parameter and each of the upper-upper-level parameters respectively; find the maximum correlation coefficient among the calculated correlation coefficients, and use the upper-upper-level parameter corresponding to the maximum correlation coefficient as the upper-upper-level parameter most relevant to the upper-level parameter.

15. The apparatus (40) according to claim 13, wherein, the apparatus further comprises: an association module (43) for establishing an association between the parameter value of the root cause parameter and the parameter value of the first parameter according to the parameter value set of the root cause parameter and the parameter value set of the first parameter obtained from the historical database.

16. The apparatus (40) according to claim 15, wherein, the association module (43) establishing an association between the parameter value of the root cause parameter and the parameter value of the first parameter includes: establishing a linear regression model or an exponential regression model between the parameter value of the root cause parameter and the parameter value of the first parameter.

17. The apparatus (40) according to claim 15 or 16, wherein, the apparatus (40) further comprises: a fault prediction module (44) for, when obtaining the parameter value set of the root cause parameter within the most recent first time period, using the association between the parameter value of the root cause parameter and the parameter value of the first parameter to calculate the predicted value set of the first parameter within the next second time period corresponding to the parameter value set of the root cause parameter within the most recent first time period, and judging whether the MRI component will fail within the next second time period according to the predicted value set of the first parameter within the next second time period, and if so, issuing a fault warning.

18. The apparatus (40) according to claim 17, wherein, the fault prediction module (44) judging whether the MRI component will fail within the next second time period according to the predicted value set of the first parameter within the next second time period includes: for each predicted value in the predicted value set of the first parameter within the next second time period, judging whether the predicted value is within the set fault range, and if so, determining that the MRI component will fail at the moment corresponding to the predicted value.

19. The apparatus (40) according to claim 17, wherein, the fault prediction module (44) is further configured to calculate the variance of the parameter value set of the root cause parameter within the most recent first time period, judge whether the variance is greater than a preset variance threshold, and if so, determine that the MRI component will fail and issue a fault warning.

20. The apparatus (40) according to claim 13, wherein, the parameter acquisition module (41) acquiring the parameter values of the parameters to be monitored of each MRI component to be monitored includes: when it is monitored that the current monitoring trigger condition of a parameter to be monitored of an MRI component is satisfied, acquiring the parameter value of the parameter to be monitored from the MRI component; the monitoring trigger condition is: being periodically triggered at a preset time interval, or being triggered at a set time point, or being triggered when a set event occurs.

21. The device (40) according to claim 20, wherein, when the parameter acquisition module (41) monitors that the monitoring trigger condition of a parameter to be monitored of an MRI component is currently satisfied, it acquires the parameter value of the parameter to be monitored from the MRI component, including: when the monitoring trigger condition of a parameter to be monitored of an MRI component is monitored to be currently satisfied, a monitoring command including the name of the MRI component and the name of the parameter to be monitored is constructed, the monitoring command is sent to the peripheral control unit, and the parameter value of the parameter to be monitored of the MRI component sent by the peripheral control unit is received; wherein, after receiving the monitoring command, the peripheral control unit parses the name of the MRI component and the name of the parameter to be monitored from the monitoring command, and acquires the parameter value of the parameter to be monitored from the MRI component through the interface between itself and the MRI component.

22. The device (40) according to claim 20, wherein, when the parameter acquisition module (41) monitors that the monitoring trigger condition of a parameter to be monitored of an MRI component is currently satisfied, the acquisition of the parameter value of the parameter to be monitored from the MRI component includes: when it is known from the monitoring information in the configuration file that the monitoring trigger mode of a parameter to be monitored of an MRI component is: periodic triggering at a preset time interval, then when it is monitored that the current time reaches the periodic triggering time, the parameter value of the parameter to be monitored is acquired from the MRI component; or, when it is known from the monitoring information in the configuration file that the monitoring trigger mode of a parameter to be monitored of an MRI component is: triggering at a set time point, then when it is monitored that the current time reaches the set time point, the parameter value of the parameter to be monitored is acquired from the MRI component; or, when an event is detected, a monitoring trigger mode matching the event is searched for in the monitoring information in the configuration file, and if found, the parameter value of the parameter to be monitored is acquired from the MRI component according to the name of the MRI component and the name of the parameter to be monitored corresponding to the event in the monitoring information in the configuration file; wherein, the monitoring information is stored in the configuration file, including: the names of the MRI components to be monitored, the names of the parameters to be monitored of each MRI component, and the monitoring trigger modes of the parameters to be monitored of each MRI component.

23. A magnetic resonance imaging (MRI) system (50), wherein, the MRI system (50) includes the MRI component fault location device (40) according to any one of claims 13 to 22.

24. The MRI system (50) according to claim 23, wherein, the MRI system (50) further includes: a peripheral control unit (51), and the peripheral control unit (51) has interfaces connected to each MRI component to be monitored and an interface connected to the MRI component fault location device (40); The peripheral control unit (51) is configured to receive the monitoring command sent by the MRI component fault location device (40), parse the name of the MRI component and the name of the parameter to be monitored from the monitoring command, obtain the parameter value of the parameter to be monitored from the MRI component through the interface between itself and the MRI component, and send the parameter value of the parameter to be monitored of the MRI component to the MRI component fault location device (40).

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