Fault detection method and device, electronic equipment, storage medium and train

Abstract

The invention provides a fault detection method and device, electronic equipment, a storage medium and a train, and the method comprises the steps: obtaining the current operation data of a plurality of systems, and obtaining abnormal parameters in the current operation data; matching the abnormal parameters with a pre-constructed fault link set, determining a target fault source system or a target association system corresponding to the abnormal parameters, and determining target parameter association information; according to the abnormal parameters, the target fault source system or the target association system and the target parameter association information, generating a fault detection result for the train; the fault link set comprises a plurality of fault links, each fault link comprises a plurality of nodes, the plurality of nodes comprise a first node corresponding to the fault source system and a second node corresponding to the associated system, and the dependency relationship between the first node and the second node is used for representing whether the fault state of the fault source system affects the fault state of the associated system; the node has parameter association information.

Description

Technical Field

[0001] This disclosure relates to the field of rail transit technology, and more specifically, to a fault detection method, apparatus, electronic device, storage medium, and train. Background Technology

[0002] In relevant fault detection methods, independent tests are usually conducted on each system of the train. For example, when an abnormality occurs in a certain system, technicians often only rely on the data of that system itself to perform single system fault detection, ignoring the interrelationship between systems, which leads to inaccurate fault detection results and a high rate of false fault diagnosis. Summary of the Invention

[0003] In view of this, the present disclosure provides a fault detection method, apparatus, electronic device, storage medium, and train.

[0004] One aspect of this disclosure provides a fault detection method, comprising: acquiring current operating data of multiple systems for a train, and acquiring abnormal parameters in the current operating data; matching the abnormal parameters with a pre-constructed set of fault links to determine a target fault source system or target associated system corresponding to the abnormal parameters, and determining target parameter association information; generating a fault detection result for the train based on the abnormal parameters, the target fault source system or target associated system, and the target parameter association information; wherein the set of fault links includes multiple fault links, each fault link includes multiple nodes, the multiple nodes include a first node corresponding to the fault source system and a second node corresponding to the associated system, the dependency relationship between the first node and the second node is used to characterize whether the fault state of the fault source system affects the fault state of the associated system, and the nodes have parameter association information.

[0005] According to embodiments of this disclosure, determining the target fault source system or target associated system corresponding to the abnormal parameter, and determining the target parameter association information, includes: determining the target parameter association information matching the abnormal parameter from the fault link set; determining the target first node or target second node corresponding to the target parameter association information from the fault link set, and determining other nodes that have a dependency relationship with the target first node or target second node, thereby obtaining the target fault source system or target associated system.

[0006] According to embodiments of this disclosure, the parameter association information includes: multiple fault parameters corresponding to the fault source system or associated system, and fault parameter thresholds corresponding to each fault parameter.

[0007] According to embodiments of this disclosure, the fault detection method further includes: reading historical detection data for multiple systems generated within a predetermined historical period from a pre-built historical detection database; identifying historical fault systems from the historical detection data, and determining historical fault parameters of multiple fault components corresponding to the historical fault systems; and determining fault parameter thresholds based on the parameter values ​​of the historical fault parameters.

[0008] According to embodiments of this disclosure, the fault detection method further includes: collecting current operating data using target sensors and target cables pre-installed on the train.

[0009] According to embodiments of this disclosure, the fault detection method further includes: obtaining configuration files corresponding to multiple systems; determining the target sensor, the target acquisition frequency, and the target accuracy of the target sensor required for fault detection based on historical detection data and the configuration files; and determining the installation information of the target cable from the configuration files.

[0010] According to embodiments of this disclosure, determining the target sensor, target acquisition frequency, and target accuracy of the target sensor for fault detection based on historical detection data and configuration files includes: determining the initial acquisition frequency and initial acquisition accuracy of the target sensor based on the configuration file; and updating the initial acquisition frequency and initial acquisition accuracy based on historical detection data to obtain the target acquisition frequency and target accuracy.

[0011] Another aspect of this disclosure provides a fault detection device, comprising: an acquisition module for acquiring current operating data of multiple systems for a train and acquiring abnormal parameters in the current operating data; a matching module for matching the abnormal parameters with a pre-constructed set of fault links to determine the target fault source system or target associated system corresponding to the abnormal parameters and to determine the target parameter association information; and a generation module for generating a fault detection result for the train based on the abnormal parameters, the target fault source system or target associated system, and the target parameter association information; wherein the set of fault links includes multiple fault links, each fault link includes multiple nodes, the multiple nodes include a first node corresponding to the fault source system and a second node corresponding to the associated system, the dependency relationship between the first node and the second node is used to characterize whether the fault state of the fault source system affects the fault state of the associated system, and the nodes have parameter association information.

[0012] Another aspect of this disclosure provides an electronic device comprising:

[0013] One or more processors;

[0014] Memory, used to store one or more programs.

[0015] Specifically, when one or more programs are executed by one or more processors, the one or more processors implement the above method.

[0016] Another aspect of this disclosure provides a train including a video acquisition device, the imaging quality of which is determined by the aforementioned method for determining the imaging quality of the video acquisition device.

[0017] Another aspect of this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, are used to implement the methods described above.

[0018] Another aspect of this disclosure provides a computer program product including computer-executable instructions that, when executed, are used to implement the methods described above. Attached Figure Description

[0019] The above and other objects, features and advantages of this disclosure will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0020] Figure 1 The illustration schematically shows an exemplary system architecture to which the fault detection methods, apparatus, electronic devices, storage media, and trains disclosed herein can be applied;

[0021] Figure 2 A flowchart illustrating a fault detection method according to an embodiment of the present disclosure is shown schematically.

[0022] Figure 3 A schematic diagram illustrating a partial cable installation location according to an embodiment of the present disclosure is shown.

[0023] Figure 4 A schematic diagram illustrating a partial cable installation location according to another embodiment of the present disclosure is shown.

[0024] Figure 5 A schematic diagram illustrating the installation location of a local sensor according to an embodiment of the present disclosure is shown.

[0025] Figure 6 A block diagram of a fault detection apparatus according to an embodiment of the present disclosure is schematically shown; and

[0026] Figure 7 A block diagram illustrating a suitable method for implementing a fault detection method according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0027] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0028] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0029] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0030] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0031] In the embodiments disclosed herein, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0032] In the embodiments disclosed herein, user authorization or consent is obtained before acquiring or collecting user personal information.

[0033] Early-built track and power collection systems (such as subways) face equipment aging issues such as contact wire wear, track corrugation, and train braking system failures, making trains prone to malfunctions. Furthermore, because the capacity of some operating lines is insufficient to meet actual demand, some lines may reduce the number of seats in trains to increase capacity, but this increases the overall weight of the train and the probability of malfunctions. In addition, there is a trade-off between the maintenance costs and efficiency of vehicles, traction networks, tracks, and tunnels. Nighttime maintenance windows are limited, typically only covering 2-4 hours, making it difficult to balance the needs of inspection depth and operating time, easily leading to the risk of trains operating with defects (i.e., operating in a faulty state).

[0034] While relevant fault detection methods are usually single-system fault detection, the multiple systems of a train are usually not operating in isolation, but rather multiple systems coupled together. A fault in one system may trigger anomalies in multiple systems. Relevant single-system fault detection methods are difficult to clarify the causal relationship between faults and easily overlook the influence between systems, making it difficult to locate the true source of the fault.

[0035] For example, when testing a train's braking system, if an extended braking distance is found, the relevant methods would assume it is a fault in the braking system itself and only test the braking system. However, it may actually be a chain reaction caused by an anomaly in the wheel-rail dynamics system (such as poor wheel-rail contact).

[0036] In view of this, embodiments of the present disclosure provide a fault detection method, comprising: acquiring current operating data of multiple systems for a train, and acquiring abnormal parameters in the current operating data; matching the abnormal parameters with a pre-constructed set of fault links to determine a target fault source system or target associated system corresponding to the abnormal parameters, and determining target parameter association information; generating a fault detection result for the train based on the abnormal parameters, the target fault source system or target associated system, and the target parameter association information; wherein, the set of fault links includes multiple fault links, each fault link includes multiple nodes, the multiple nodes include a first node corresponding to the fault source system, a second node corresponding to the associated system, the dependency relationship between the first node and the second node is used to characterize whether the fault state of the fault source system affects the fault state of the associated system, and the nodes have parameter association information.

[0037] Figure 1 This illustration schematically depicts an exemplary system architecture 100 for determining the image quality of a video acquisition device, an apparatus, an electronic device, a storage medium, and a train, according to embodiments of this disclosure. It should be noted that... Figure 1 The examples shown are merely examples of system architectures that can be applied to the embodiments of this disclosure, in order to help those skilled in the art understand the technical content of this disclosure, but do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments or scenarios.

[0038] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0039] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social media platform software, etc. (for example only).

[0040] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0041] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0042] For example, a user can initiate a fault detection command through a first terminal device 101, a second terminal device 102, and a third terminal device 103. In response to the aforementioned fault detection command, the server 105 can execute a fault detection method, including: acquiring current operating data of multiple systems for the train and acquiring abnormal parameters in the current operating data; matching the abnormal parameters with a pre-constructed set of fault links to determine the target fault source system or target associated system corresponding to the abnormal parameters, and determining the target parameter association information; generating a fault detection result for the train based on the abnormal parameters, the target fault source system or target associated system, and the target parameter association information; wherein, the set of fault links includes multiple fault links, each fault link includes multiple nodes, the multiple nodes include a first node corresponding to the fault source system and a second node corresponding to the associated system, the dependency relationship between the first node and the second node is used to characterize whether the fault state of the fault source system affects the fault state of the associated system, and the nodes have parameter association information.

[0043] It should be noted that the fault detection method provided in this embodiment can generally be executed by server 105. Accordingly, the fault detection system provided in this embodiment can generally be set in server 105. The fault detection method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or server 105.

[0044] Accordingly, the fault detection system provided in this embodiment can also be set in a server or server cluster that is different from server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or server 105. Alternatively, the fault detection method provided in this embodiment can also be executed by the first terminal device 101, the second terminal device 102 or the third terminal device 103, or it can be executed by other terminal devices that are different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0045] Accordingly, the fault detection system provided in this embodiment may also be installed in the first terminal device 101, the second terminal device 102 or the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0046] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0047] Figure 2 A flowchart illustrating a fault detection method according to an embodiment of the present disclosure is shown schematically.

[0048] like Figure 2 As shown, the method includes operations S210~S230.

[0049] In operation S210, current operating data for multiple systems of the train is obtained, and abnormal parameters in the current operating data are also obtained.

[0050] For example, multiple systems may include braking systems, noise systems, traction systems, etc. The current operating data of each of these systems can be collected in real time using sensors. For each system, the current operating data may include multiple parameters corresponding to that system.

[0051] For example, the current operating data of a noise system may include multiple parameters, such as noise sound pressure level and noise duration. Each parameter may have a corresponding fault parameter threshold, and abnormal parameters may include parameters whose values ​​exceed the fault parameter threshold.

[0052] For example, the fault parameter threshold for noise sound pressure level is 85 dB. When the noise sound pressure level after correction by the A-weighted network exceeds 85 dB, it indicates that the noise sound pressure level exceeds the standard value and the noise sound pressure level is an abnormal parameter.

[0053] In operation S220, the abnormal parameters are matched with a pre-built set of fault links to determine the target fault source system or target associated system corresponding to the abnormal parameters, as well as the associated information of the target parameters.

[0054] According to embodiments of this disclosure, the fault link set includes multiple fault links, each fault link includes multiple nodes, the multiple nodes include a first node corresponding to the fault source system and a second node corresponding to the associated system, the dependency relationship between the first node and the second node is used to characterize whether the fault state of the fault source system affects the fault state of the associated system, and the nodes have parameter association information.

[0055] For example, for a faulty link, the fault source system can be the starting point of the faulty link and the root cause of abnormalities in other systems.

[0056] For example, an associated system can be an extension of a fault chain, which is another system that is indirectly affected by the failure of the source system.

[0057] The information included in the set of faulty links is shown in Table 1.

[0058]

[0059] For Table 1, for example, when the wheel-rail dynamics system experiences a "wheel-rail vertical force > 120kN" (i.e., exceeding the fault parameter threshold), it will cause a decrease in the friction coefficient of the braking system. In this case, the wheel-rail dynamics system is the fault source system, and the braking system is the associated system. Alternatively, when the wheel-rail dynamics system experiences a "wheel-rail wear > 0.5mm", it will cause the noise sound pressure level of the noise system to exceed 85dB(A). In this case, the wheel-rail dynamics system is the fault source system, and the noise system is the associated system, where A represents the A-weighted network.

[0060] For example, when the voltage of the current receiving system is abnormal, it will lead to unstable traction power (such as traction current fluctuation of ±15%) and abnormal noise frequency (noise frequency peak of 2000-3000Hz). In this case, the current receiving system is the fault source system, and the traction system and the noise system are related systems.

[0061] Accordingly, the current receiving system can correspond to the first node, the traction system can correspond to the second node 1, and the noise system can correspond to the second node 2. There can be a dependency relationship between the first node and the second node 1, which is used to characterize that the fault state of the current receiving system will affect the fault state of the traction system; there can also be a dependency relationship between the first node and the second node 2, which is used to characterize that the fault state of the current receiving system will affect the fault state of the noise system.

[0062] For example, parameter association information may include parameters that may cause faults in each system and the corresponding fault parameter thresholds. For instance, for a noise system, the noise sound pressure level parameter may be too high, indicating that the train's noise intensity exceeds the standard level. Accordingly, for a node corresponding to the noise system, the parameter association information for that node may include the noise sound pressure level parameter and the corresponding fault parameter threshold.

[0063] For example, parameter correlation information can characterize the correlation between parameters of different systems. For example, for a fault source system such as wheel-rail dynamics, its parameter correlation information may include: when the vertical force of wheel-rail is greater than 120kN, it will cause the braking friction coefficient to decrease; when the wheel-rail wear is greater than 0.5mm, it will cause the noise sound pressure level to be greater than 85dB(A).

[0064] For example, multiple systems may be coupled together; a failure in one system may trigger a failure in another. To accurately and comprehensively determine the cause of abnormal parameters, the abnormal parameters can be matched with a set of fault links to identify all systems and parameters associated with the abnormal parameters. Specifically, the target fault source system or associated system can be identified. For instance, when the system generating the abnormal parameters is the fault source system, the target associated system corresponding to that fault source system can be identified; when the system generating the abnormal parameters is an associated system, the target fault source system corresponding to that fault source system can be identified. Furthermore, the target parameter association information corresponding to the target fault source system or target associated system can be determined.

[0065] When operating S230, fault detection results for the train are generated based on abnormal parameters, the target fault source system or the target associated system, and the target parameter association information.

[0066] For example, the abnormal parameter can be identified as the wheel-rail vertical force. This abnormal parameter corresponds to the wheel-rail dynamics system. Through the fault link set, this system can be identified as the fault source system, and its associated systems include the braking system and the noise system. Using the parameter correlation information in the fault link set, it can be determined that when the wheel-rail vertical force exceeds 120kN, it will cause a decrease in the braking friction coefficient of the braking system. Fault detection results can be generated, which can include the aforementioned abnormal parameter, the aforementioned fault source system and associated systems, as well as the correlation between the parameters of the aforementioned fault source system and associated systems. Therefore, when the wheel-rail vertical force of the current wheel-rail dynamics system is abnormal, maintenance personnel will not only perform fault detection on the wheel-rail dynamics system, but will also detect the braking system and the noise system to determine whether parameters such as the braking friction coefficient are also abnormal.

[0067] According to embodiments of this disclosure, by constructing a fault link set, which includes a first node corresponding to the fault source system and a second node corresponding to the associated system, the dependency relationship between the first node and the second node is used to characterize whether the fault state of the fault source system affects the fault state of the associated system. The nodes have parameter association information, and the fault link set can be used to characterize the coupling relationship between multiple systems and clarify the causal relationship of faults in multiple systems. Compared with methods for detecting related single faults, this method can improve the accuracy of fault detection and quickly locate the root cause fault. For example, when multiple systems malfunction simultaneously, the fault source system and associated system can be directly determined through the fault link set without having to check all systems one by one, thereby significantly shortening the fault detection time.

[0068] According to embodiments of this disclosure, determining the target fault source system or target associated system corresponding to the abnormal parameter, and determining the target parameter association information, includes: determining the target parameter association information matching the abnormal parameter from the fault link set; determining the target first node or target second node corresponding to the target parameter association information from the fault link set, and determining other nodes that have a dependency relationship with the target first node or target second node, thereby obtaining the target fault source system or target associated system.

[0069] For example, parameter association information, including abnormal parameters, can be determined from the set of faulty links to obtain target parameter association information. Since the first node and the second node have parameter association information, the node corresponding to the target parameter association information can be determined.

[0070] For example, if an abnormal decrease in the braking friction coefficient is detected during fault detection, then the abnormal parameter includes the braking friction coefficient. It can be determined from the fault link set which parameter association information includes the braking friction coefficient. For instance, if it is determined that the parameter association information corresponding to a certain second node in fault link 1 includes the braking friction coefficient, then the parameter association information corresponding to that node is the target parameter association information.

[0071] For example, the node corresponding to the target parameter association information can be determined. The target parameter association information may correspond to the target first node, that is, the abnormal parameter corresponds to the fault source system. Further, by determining other second nodes that have a dependency relationship with the target first node, the associated system corresponding to the fault source system can be determined, and the target associated system can be obtained. The target parameter association information may also correspond to the target second node, that is, the abnormal parameter corresponds to the associated system. Further, by determining other first nodes that have a dependency relationship with the target second node, the fault source system corresponding to the associated system can be determined, and the target fault source system can be obtained.

[0072] According to embodiments of this disclosure, by determining target parameter association information that matches the abnormal parameter from the fault link set, and then determining the target first node or target second node corresponding to the target parameter association information from the fault link set, not only can the system corresponding to the abnormal parameter be located, but also other systems that are dependent on the system can be located, thereby enabling comprehensive fault detection and improving the accuracy of fault detection.

[0073] According to embodiments of this disclosure, the parameter association information includes: multiple fault parameters corresponding to the fault source system or associated system, and fault parameter thresholds corresponding to each fault parameter.

[0074] For example, fault parameters include parameters that indicate potential failures in each system. When a fault parameter exceeds a fault parameter threshold, it indicates that the system may fail. For instance, for a current-collecting system, fault parameters may include current-collecting voltage fluctuations, with a threshold of 10%. When the current-collecting voltage fluctuation exceeds 10%, it indicates that this parameter is abnormal, and the current-collecting system may fail.

[0075] According to embodiments of this disclosure, the fault detection method further includes: reading historical detection data for multiple systems generated within a predetermined historical period from a pre-built historical detection database; identifying historical fault systems from the historical detection data, and determining historical fault parameters of multiple fault components corresponding to the historical fault systems; and determining fault parameter thresholds based on the parameter values ​​of the historical fault parameters.

[0076] For example, a historical detection database can be pre-built. This database can store historical detection data obtained during fault detection of multiple systems within a certain historical time interval (which can be set according to actual needs). This fault detection can be performed during the testing phase of the train. The historical detection data can include historical fault events, historical fault systems corresponding to the historical fault events, historical fault parameters corresponding to the historical fault systems, and parameter values, etc.

[0077] For example, historical fault events may include 100 vehicle type test fault events, faulty systems may include traction systems, braking systems, etc., and historical fault parameters may include traction motor temperature, torque fluctuations, brake cylinder pressure response delays, etc.

[0078] For example, the parameter values ​​of each historical fault parameter in historical fault events can be determined, and fault parameter thresholds can be determined based on these parameter values. For instance, by statistically analyzing historical parameter values ​​of the same fault type, such as analyzing the temperature and torque fluctuations of the traction motor in 10 traction system overload faults, the parameter value distribution can be determined based on the statistical results, and the parameter concentration range when most faults occur can be identified. For example, in 80% of traction system overload faults, the traction motor temperature is concentrated above 150°C and the torque fluctuation is greater than 8%, so the fault parameter threshold for traction motor temperature can be set to 150°C, and the fault parameter threshold for torque fluctuation can be set to 8%. As another example, if the statistical results also show that in most braking system faults, the brake cylinder pressure response delay is greater than 0.5 seconds, then the fault parameter threshold for brake cylinder pressure response delay can be set to 0.5 seconds.

[0079] For example, historical fault parameters and fault parameter thresholds can be stored in the host computer processing unit for unified management of historical fault parameters and fault parameter thresholds. They can be queried or transmitted in real time, thereby improving fault response efficiency.

[0080] According to embodiments of this disclosure, the fault link set also includes parameter association information. This information includes multiple fault parameters and corresponding fault parameter thresholds. This not only identifies all faulty systems but also determines the specific fault parameters corresponding to each faulty system, enabling precise fault location. Furthermore, by determining the fault parameter thresholds based on historical fault parameter values, historical train inspection data can be referenced to accurately determine the fault parameter thresholds.

[0081] According to embodiments of this disclosure, the fault detection method further includes: collecting current operating data using target sensors and target cables pre-installed on the train.

[0082] For example, in related methods, sensors are typically only temporarily installed on the train during the testing phase for fault testing. After the test is completed, the sensors are removed, and then reinstalled for the next test. On the one hand, this leads to a lack of comparability of data from different test cycles. For instance, the fixed points of the sensors may vary slightly each time they are installed, and such deviations directly cause distortion in the parameter acquisition results. On the other hand, this increases the testing cycle and the costs of manpower and resources. For example, each sensor installation may take 2 to 3 days, and removal after the test takes 1 day. Repeated testing would consume a significant amount of installation and removal time, resulting in low deployment efficiency of the testing system. Furthermore, the temporary installation mode can only collect data during the test period, making it difficult to cover the vehicle's operating status outside of the testing phase, and difficult to detect sudden vehicle malfunctions, thus hindering long-term monitoring.

[0083] According to embodiments of this disclosure, by pre-installing the target sensor and target cable on the train instead of temporarily fixing them, the lifespan of the test system (including the target sensor and target cable) is consistent with the lifespan of the train itself. This allows for real-time, long-term, multiple, accurate, and effective data acquisition, and outputs fault detection results, while also improving the deployment efficiency of the test system.

[0084] According to embodiments of this disclosure, the fault detection method further includes: acquiring configuration files corresponding to multiple systems; determining the target sensor, target acquisition frequency, and target accuracy of the target sensor based on historical detection data and the configuration files; and determining the installation information of the target cable from the configuration files. For example, the configuration files may include test outlines for systems such as the train's traction system, braking system, current collection system, wheel-rail dynamics, and noise system. By reviewing the configuration files, the test objectives, standards, test items, and judgment criteria for each system can be determined. For example, for the traction system, the configuration files can determine the test requirements for core indicators such as traction power, acceleration performance, and regenerative braking efficiency; for the noise system, the operating conditions, measurement point locations, and noise limit standards for internal and external noise tests need to be clarified. By combining the test objectives, standards, test items, and judgment criteria in the configuration files with historical detection data, the type of sensor can be determined, resulting in the target sensor; the type of cable can be determined, resulting in the target cable; and the target acquisition frequency, target accuracy, and installation information of the target cable can also be determined. The installation information of the target cable includes, for example, the cable type, installation location, and number of cables installed.

[0085] For example, the parameters required for fault location can also be determined based on the configuration file in order to determine the type of sensor that can collect the corresponding parameters.

[0086] According to embodiments of this disclosure, by pre-installing target sensors and target cables on the train for an extended period, the temporary installation of sensors and cables can be upgraded to an integrated vehicle configuration, enabling long-term, repeated use, reducing testing costs, and improving data consistency.

[0087] The specific details of the vehicle integrated configuration items are shown in Table 2. For example, by reviewing the configuration file of the wheel-rail dynamics system, and based on the test objectives, test items, and judgment criteria, the fault location-related parameters corresponding to this system are determined, including wheel-rail vertical force, lateral force, and wear. That is, during vehicle operation, wheel-rail dynamics faults may be caused by one or more of these parameters. The types of sensors capable of acquiring these parameters can be determined, including strain gauge wheel-rail force sensors and laser wear sensors. The acquisition frequency of these sensors is determined to be 100Hz, with accuracy requirements of force ±2%FS and wear ±0.01mm, respectively. Correspondingly, the configuration file of the current collection system can be reviewed to determine the corresponding fault location-related parameters, sensor types, acquisition frequencies, and accuracy requirements, which will not be elaborated further here.

[0088]

[0089] Figure 3A schematic diagram illustrating a partial cable installation location according to an embodiment of the present disclosure is shown.

[0090] like Figure 3 The partial side view of the train shown (i.e.) Figure 3 In the view from the top (center), the force-measuring wheelsets of the EMU can be installed at the bogie wheelset position to measure the forces between the wheel and rail (such as vertical and lateral forces). The required cables, their installation positions, and quantities can be determined according to the configuration file. For example, the configuration file can determine that two traction test cables will be installed in both cars 1 and 4, and that 22 dynamic test cables and 5 noise test cables can be installed. In the partial interior plan view of the train (i.e.,...) Figure 3 In the view below, the cables to be set, including two noise test cables, can be determined according to the configuration file, and the installation positions of these two noise test cables can be determined.

[0091] Figure 4 A schematic diagram illustrating a partial cable installation location according to another embodiment of the present disclosure is shown.

[0092] like Figure 4 The partial side view of the train shown (i.e.) Figure 4 In the view from the top (center), trailer-mounted force-measuring wheelsets for measuring the forces acting on the wheelsets can be installed in the train. The required cables, determined from the configuration file, include 2 traction test cables, 19 dynamic test cables, and 3 noise test cables, with the traction test cables for cars 1 and 3 being identical. In the partial interior plan view of the train (i.e.,...) Figure 4 In the view below, you can determine the cables to be set according to the configuration file, including one dynamic test cable and one noise test cable.

[0093] Figure 5 A schematic diagram illustrating the installation location of a local sensor according to an embodiment of the present disclosure is shown.

[0094] like Figure 5 In the partial side view of the train shown, it can be determined from the configuration file that four pressure sensors will be installed on the train's pantograph, and that cables connected to the insulation components can be installed.

[0095] like Figures 3-5 As shown, cables and sensors can be integrated into the train configuration items. For example, cables and sensors can be pre-installed on the train for long-term, repeated output of train operating parameters and other data, rather than just temporary installations.

[0096] According to the embodiments of this disclosure, by using the testing equipment for each system of the vehicle as a vehicle configuration component and installing and debugging it according to the drawings, there is no need to add additional temporary testing equipment. The lifespan of the testing system is consistent with that of the vehicle itself. Real-time data acquisition can be achieved, and the data fed back by each system can be used to determine whether the vehicle is "operating with defects" and can ensure the operation of the vehicle under overload conditions.

[0097] According to embodiments of this disclosure, each testing system can be debugged after the train is manufactured to ensure normal operation and output. Maintenance and repair plans can be developed for each testing system to ensure continuous and stable operation.

[0098] According to embodiments of this disclosure, by reviewing the test outline and test plan for the system type test of the vehicle, the placement of the test sensors and the data acquisition host can be determined, thereby determining the routing scheme of the test cables. By integrating the sensors, test cables, and data acquisition host into the design process of the test vehicle, multiple and long-term test verifications can be conducted without the need for additional test equipment. This not only allows for real-time monitoring of the vehicle's operational quality and triggering alarms based on thresholds of each system to remind operators to perform corresponding system maintenance, but also verifies the evolution of the track and vehicle status and performance during operation. This provides a large amount of test data support for the future development of rail transit equipment. Furthermore, by integrating existing sensors into the vehicle configuration, no additional temporary testing equipment is required, reducing testing costs.

[0099] According to embodiments of this disclosure, determining the target sensor, target acquisition frequency, and target accuracy of the target sensor for fault detection based on historical detection data and configuration files includes: determining the initial acquisition frequency and initial acquisition accuracy of the target sensor based on the configuration file; and updating the initial acquisition frequency and initial acquisition accuracy based on historical detection data to obtain the target acquisition frequency and target accuracy.

[0100] For example, the initial acquisition frequency and initial acquisition accuracy can be determined based on the test objectives, standards, test items, and judgment criteria in the configuration file. For instance, the test objectives can determine which parameters are core parameters and which are auxiliary parameters, thereby setting higher acquisition frequencies and accuracy for the core parameters. For example, minimum requirements for acquisition frequency and accuracy can be determined based on industry standards, such as determining the minimum acquisition frequency and minimum accuracy.

[0101] For example, the initial acquisition frequency and initial acquisition accuracy determined based on the configuration file are theoretical values ​​determined by industry standards, equipment parameters, and theoretical calculations, which may deviate from the actual operating conditions of the train. For instance, the theoretically set wheel-rail force acquisition frequency is 100Hz, but in actual train operation, the wheel-rail force fluctuates more frequently due to dynamic factors such as track smoothness, load changes, and ambient temperature, requiring a higher frequency to capture the peak value.

[0102] For example, historical detection data can reflect the actual operating conditions of the train during operation. Therefore, the initial acquisition frequency and initial acquisition accuracy can be updated based on historical detection data. For example, regarding the parameter of traction current, historical detection data shows that the brake cylinder pressure rises from 0 to the rated value within 0.5 seconds during braking, and the fluctuation frequency is 200Hz. Therefore, an initial acquisition frequency of 100Hz may cause the critical fluctuation to be missed. Thus, it is necessary to increase the initial acquisition frequency to obtain the target acquisition frequency.

[0103] According to embodiments of this disclosure, by determining the initial acquisition frequency and initial acquisition accuracy of the target sensor based on a configuration file, and updating the initial acquisition frequency and initial acquisition accuracy based on historical detection data to obtain the target acquisition frequency and target accuracy, the acquisition frequency and accuracy of the sensor can be made more consistent with the actual operating state of the train, ensuring the accuracy and adaptability of data acquisition, thereby improving the accuracy of fault detection.

[0104] According to embodiments of this disclosure, the sensor cable can synchronously transmit abnormal parameters to the host computer, and the cable's shielding level can meet the requirements for electromagnetic interference resistance. For example, shielded twisted-pair cable can be used to improve the interference resistance level and avoid data transmission errors affecting fault diagnosis.

[0105] According to embodiments of this disclosure, fault detection results can be output to the driver's cab screen in real time to alert the driver to the vehicle status.

[0106] Figure 6 A block diagram of a fault detection apparatus according to an embodiment of the present disclosure is shown schematically.

[0107] like Figure 6 As shown, the fault detection device 600 includes an acquisition module 610, a matching module 620, and a generation module 630.

[0108] The acquisition module 610 is used to acquire current operating data for multiple systems of the train, and to acquire abnormal parameters in the current operating data. In one embodiment, the acquisition module 610 can be used to perform the operation S210 described above, which will not be repeated here.

[0109] The matching module 620 is used to match abnormal parameters with a pre-built set of fault links to determine the target fault source system or target associated system corresponding to the abnormal parameters, and to determine the association information of the target parameters. In one embodiment, the matching module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0110] The generation module 630 is used to generate fault detection results for the train based on abnormal parameters, the target fault source system or the target associated system, and the target parameter association information. In one embodiment, the generation module 630 can be used to perform the operation S230 described above, which will not be repeated here.

[0111] According to embodiments of this disclosure, the fault link set includes multiple fault links, each fault link includes multiple nodes, the multiple nodes include a first node corresponding to the fault source system and a second node corresponding to the associated system, the dependency relationship between the first node and the second node is used to characterize whether the fault state of the fault source system affects the fault state of the associated system, and the nodes have parameter association information.

[0112] According to embodiments of this disclosure, the matching module includes a first determining submodule, used to determine target parameter association information that matches the abnormal parameters from the set of fault links; and a second determining submodule, used to determine a target first node or target second node corresponding to the target parameter association information from the set of fault links, and to determine other nodes that have a dependency relationship with the target first node or target second node, thereby obtaining a target fault source system or a target associated system.

[0113] According to embodiments of this disclosure, the parameter association information includes: multiple fault parameters corresponding to the fault source system or associated system, and fault parameter thresholds corresponding to each fault parameter.

[0114] According to embodiments of this disclosure, the fault detection device further includes a reading module for reading historical detection data for multiple systems generated within a predetermined historical period from a pre-built historical detection database; it also includes a first determining module for determining historical fault systems from the historical detection data and determining historical fault parameters of multiple fault components corresponding to the historical fault systems; and a second determining module for determining fault parameter thresholds based on the parameter values ​​of the historical fault parameters.

[0115] According to embodiments of this disclosure, the fault detection device further includes a data acquisition module for acquiring current operating data using target sensors and target cables pre-installed on the train.

[0116] According to embodiments of this disclosure, the fault detection device further includes a file acquisition module for acquiring configuration files corresponding to multiple systems; a third determination module for determining the target sensor, the target acquisition frequency of the target sensor, and the target accuracy required for fault detection based on historical detection data and configuration files; and a fourth determination module for determining the installation information of the target cable from the configuration files.

[0117] According to an embodiment of this disclosure, the third determining module includes a third determining submodule, used to determine the initial acquisition frequency and initial acquisition accuracy of the target sensor according to a configuration file; and an updating submodule, used to update the initial acquisition frequency and initial acquisition accuracy according to historical detection data to obtain the target acquisition frequency and target accuracy.

[0118] Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure, or at least part of the functions of any one or more of them, can be implemented in one module. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be implemented by dividing them into multiple modules. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be at least partially implemented as hardware circuitry, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System-on-Chip, a System-on-a-Substrate, a System-on-Package, an Application-Specific Integrated Circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0119] For example, any plurality of the acquisition module 610, matching module 620, and generation module 630 can be combined into one module / unit / subunit, or any one of these modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functionality of one or more of these modules / units / subunits can be combined with at least part of the functionality of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of this disclosure, at least one of the acquisition module 610, matching module 620, and generation module 630 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 610, matching module 620, and generation module 630 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0120] It should be noted that the data processing system part in the embodiments of this disclosure corresponds to the data processing method part in the embodiments of this disclosure. The specific description of the data processing system part is referred to in the data processing method part, and will not be repeated here.

[0121] Figure 7 A block diagram of an electronic device suitable for implementing the methods described above, according to embodiments of the present disclosure, is illustrated schematically. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0122] like Figure 7 As shown, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0123] RAM 703 stores various programs and data required for the operation of electronic device 700. Processor 701, ROM 702, and RAM 703 are interconnected via bus 704. Processor 701 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 702 and / or RAM 703. It should be noted that programs may also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in one or more memories.

[0124] According to embodiments of this disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to a bus 704. The electronic device 700 may also include one or more of the following components connected to the input / output (I / O) interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0125] According to embodiments of this disclosure, the method flow according to embodiments of this disclosure can be implemented as a computer software program. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by processor 701, it performs the functions defined in the system of embodiments of this disclosure. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0126] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0127] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0128] For example, according to embodiments of this disclosure, a computer-readable storage medium may include the ROM 702 and / or RAM 703 described above and / or one or more memories other than ROM 702 and RAM 703.

[0129] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of this disclosure. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the method for determining the imaging quality of the video acquisition device provided in the embodiments of this disclosure.

[0130] When the computer program is executed by the processor 701, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0131] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 709, and / or installed from a removable medium 711. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0132] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of this disclosure may be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0134] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A fault detection method, characterized in that, The method includes: Obtain current operating data for multiple systems of the train, and obtain abnormal parameters in the current operating data; The abnormal parameters are matched with a pre-built set of fault links to determine the target fault source system or target associated system corresponding to the abnormal parameters, as well as the associated information of the target parameters. Based on the abnormal parameters, the target fault source system or the target associated system, and the target parameter association information, a fault detection result for the train is generated; The fault link set includes multiple fault links, each fault link includes multiple nodes, the multiple nodes include a first node corresponding to the fault source system and a second node corresponding to the associated system, the dependency relationship between the first node and the second node is used to characterize whether the fault state of the fault source system affects the fault state of the associated system, and the nodes have parameter association information.

2. The method according to claim 1, characterized in that, The determination of the target fault source system or target associated system corresponding to the abnormal parameter, and the determination of the target parameter association information, include: Determine the association information of the target parameter that matches the abnormal parameter from the set of fault links; From the set of fault links, determine the first target node or the second target node corresponding to the target parameter association information, and determine other nodes that have a dependency relationship with the first target node or the second target node, to obtain the target fault source system or the target associated system.

3. The method according to claim 1, characterized in that, The parameter association information includes: multiple fault parameters corresponding to the fault source system or the associated system, and fault parameter thresholds corresponding to each fault parameter.

4. The method according to claim 3, characterized in that, The method further includes: Read historical detection data for the multiple systems generated within a predetermined historical period from a pre-built historical detection database; Identify historical fault systems from the historical detection data, and determine the historical fault parameters of multiple fault components corresponding to the historical fault systems; The fault parameter threshold is determined based on the parameter values ​​of the historical fault parameters.

5. The method according to claim 4, characterized in that, The method further includes: The current operating data is collected using target sensors and target cables pre-installed on the train; The method further includes: Obtain the configuration files corresponding to the multiple systems; Based on the historical detection data and the configuration file, the target sensor required for fault detection, the target acquisition frequency of the target sensor, and the target accuracy are determined. The installation information of the target cable is determined from the configuration file.

6. The method according to claim 5, characterized in that, The step of determining the target sensor required for fault detection, the target acquisition frequency of the target sensor, and the target accuracy of the target sensor based on the historical detection data and the configuration file includes: Based on the configuration file, determine the initial acquisition frequency and initial acquisition accuracy of the target sensor; The initial acquisition frequency and the initial acquisition accuracy are updated based on the historical detection data to obtain the target acquisition frequency and the target accuracy.

7. A fault detection device, characterized in that, The method includes: The acquisition module is used to acquire current operating data of multiple systems for the train, and to acquire abnormal parameters in the current operating data; The matching module is used to match the abnormal parameters with a pre-built set of fault links to determine the target fault source system or target associated system corresponding to the abnormal parameters, and to determine the target parameter association information. The generation module is used to generate fault detection results for the train based on the abnormal parameters, the target fault source system or the target associated system, and the target parameter association information. The fault link set includes multiple fault links, each fault link includes multiple nodes, the multiple nodes include a first node corresponding to the fault source system and a second node corresponding to the associated system, the dependency relationship between the first node and the second node is used to characterize whether the fault state of the fault source system affects the fault state of the associated system, and the nodes have parameter association information.

8. An electronic device, comprising: One or more processors; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 6.

10. A train, comprising: The electronic device according to claim 8.

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