Shaft temperature early warning monitoring method and device, electronic equipment and readable storage medium

Abstract

The present disclosure provides an axle temperature early warning monitoring method and device, electronic equipment and storage medium, which can be applied to the field of rail transit technology. The axle temperature early warning monitoring method comprises: analyzing an axle temperature data set from a sensor to obtain temperature rise rate data; determining a time interval in which the first temperature rise rate changes to the second temperature rise rate according to the temperature rise rate data; in the case that the length of the time interval is less than or equal to a preset threshold, determining the axle temperature data corresponding to the time interval in the axle temperature data set as jump data; and performing early warning monitoring according to other axle temperature data in the axle temperature data set except the jump data.

Description

Technical Field

[0001] This disclosure relates to the field of rail transit technology, specifically to a method, device, electronic equipment, and readable storage medium for axle temperature early warning monitoring. Background Technology

[0002] Currently, during the operation of high-speed trains, the temperature of the axle box, gearbox, or motor bearings (hereinafter referred to as axle temperature) rises due to increased friction. To ensure the safety of the train and personnel, axle temperature exceeding a certain threshold can lead to actual malfunctions such as axle combustion accidents. Therefore, the system needs to monitor and warn of axle temperature to prompt staff to stop the train operation in a timely manner and carry out inspection and maintenance.

[0003] However, in the process of realizing the present invention, the inventors discovered that the related technology has at least the following problems: In actual working conditions, false alarms are often caused by non-real faults such as sensor failure or poor contact of the acquisition line, which affects the operation of the EMU. Summary of the Invention

[0004] In view of the above problems, this disclosure provides methods, devices, equipment, media and procedures for improving shaft temperature early warning monitoring.

[0005] The first aspect of this disclosure provides a shaft temperature early warning monitoring method, comprising: analyzing a shaft temperature dataset from a sensor to obtain temperature rise rate data; determining a time interval for the transition from a first temperature rise rate to a second temperature rise rate based on the temperature rise rate data; identifying shaft temperature data in the shaft temperature dataset corresponding to the time interval as transition data when the length of the time interval is less than or equal to a preset threshold; and performing early warning monitoring based on other shaft temperature data in the shaft temperature dataset besides the transition data.

[0006] According to embodiments of this disclosure, the method further includes: when the length of the time interval is greater than a preset threshold, identifying the shaft temperature data corresponding to the time interval in the shaft temperature data set as abnormal data; and when the sensor itself is in normal performance condition, issuing an early warning for the abnormal data.

[0007] According to an embodiment of this disclosure, determining the time interval for the transition from a first temperature rise rate to a second temperature rise rate based on temperature rise rate data includes: determining a first moment corresponding to the first temperature rise rate and a second moment corresponding to the second temperature rise rate in monotonically changing temperature rise rate data; and using the time interval between the first moment and the second moment as the time interval for the transition from the first temperature rise rate to the second temperature rise rate.

[0008] According to embodiments of this disclosure, the method of this disclosure is applied to vehicle operation detection, wherein one of the first temperature rise rate and the second temperature rise rate is a positive temperature rise rate used to predict that the vehicle can operate normally, and the other is the maximum negative temperature rise rate obtained by statistical analysis based on historical jump data.

[0009] According to embodiments of this disclosure, analyzing the shaft temperature dataset from the sensor to obtain temperature rise rate data includes: calculating the temperature change rate at each moment relative to the previous moment based on the shaft temperature data at different times in the shaft temperature dataset to obtain initial temperature rise rate data; and smoothing the initial temperature rise rate data to obtain the temperature rise rate data.

[0010] According to embodiments of this disclosure, the shaft temperature dataset is a shaft temperature dataset of axle box, gearbox, or motor bearing; the method of this disclosure further includes: acquiring historical jump data and historical abnormal data that correspond to the mechanical characteristics of the axle box, gearbox, or motor bearing; and updating a preset threshold based on the historical jump data and historical abnormal data.

[0011] According to embodiments of this disclosure, the preset threshold is 30 to 60 seconds.

[0012] A second aspect of this disclosure provides a shaft temperature early warning monitoring device, comprising:

[0013] The analysis module is used to analyze the shaft temperature dataset from the sensor to obtain the temperature rise rate data;

[0014] The first determining module is used to determine the time interval from the first temperature rise rate to the second temperature rise rate based on the temperature rise rate data.

[0015] The second determining module is used to determine the shaft temperature data corresponding to the time interval in the shaft temperature dataset as jump data when the length of the time interval is less than or equal to a preset threshold; and

[0016] The early warning monitoring module is used to perform early warning monitoring based on other shaft temperature data in the shaft temperature dataset, excluding jump data.

[0017] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the aforementioned shaft temperature early warning monitoring method.

[0018] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the aforementioned shaft temperature early warning monitoring method.

[0019] The fifth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned shaft temperature early warning monitoring method.

[0020] According to the embodiments of this disclosure, since the inherent characteristic of inertia in heat transfer and heat dissipation of mechanical components has been discovered, the duration of temperature rise changes under actual fault conditions differs from the duration of temperature jumps caused by sensor malfunctions, etc. Therefore, the duration of the temperature rise rate change is used as the time feature for identifying temperature jumps to determine whether the current temperature rise rate change is abnormal. This eliminates temperature rise data caused by non-real faults such as sensor malfunctions during the early warning monitoring process, effectively reducing the probability of false alarms and improving train operation efficiency and stability. Attached Figure Description

[0021] The foregoing contents, as well as 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:

[0022] Figure 1 The illustration schematically depicts an application scenario of the shaft temperature early warning monitoring method, apparatus, device, medium, and program product according to embodiments of the present disclosure;

[0023] Figure 2 A flowchart illustrating a shaft temperature early warning monitoring method according to an embodiment of the present disclosure is shown schematically.

[0024] Figure 3 The diagram illustrates real and non-real fault scenarios of shaft temperature profiles according to embodiments of the present disclosure.

[0025] Figure 4 A flowchart illustrating an abnormal data early warning method caused by a real fault according to an embodiment of the present disclosure is shown schematically;

[0026] Figure 5 A flowchart illustrating a shaft temperature early warning monitoring method according to another embodiment of the present disclosure is shown schematically;

[0027] Figure 6 A temperature rise rate curve according to an embodiment of the present disclosure is illustrated schematically;

[0028] Figure 7 A schematic diagram illustrating the structure of a shaft temperature early warning monitoring device according to an embodiment of the present disclosure is shown; and

[0029] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a shaft temperature early warning monitoring method according to an embodiment of the present disclosure. Detailed Implementation

[0030] 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.

[0031] 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 the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0032] 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.

[0033] 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 a person 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.).

[0034] In the technical solution of this invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0035] For train mechanical components such as axle boxes, gearboxes, and motor bearings, the inherent mechanical characteristics mean that heat transfer and dissipation must be completed within a certain time. Therefore, a sudden temperature rise or fall at a given moment is not due to actual frictional heat generation between mechanical components or heat dissipation from the components, and can be identified as other factors that do not require warning. Thus, when setting system warning prompts, jump data caused by other factors can be excluded or removed, and warning monitoring can be performed only on abnormal data caused by actual fault conditions. Furthermore, in implementing this disclosed concept, it was discovered that using the duration characteristic of the temperature rise rate change can be used to more accurately identify the aforementioned abnormal data and jump data, thereby reducing the probability of false alarms and improving train operating efficiency and stability.

[0036] The embodiments of this disclosure provide a shaft temperature early warning monitoring method, including: analyzing a shaft temperature dataset from a sensor to obtain temperature rise rate data; determining a time interval for the transition from a first temperature rise rate to a second temperature rise rate based on the temperature rise rate data; identifying shaft temperature data in the shaft temperature dataset corresponding to the time interval as transition data when the length of the time interval is less than or equal to a preset threshold; and performing early warning monitoring based on other shaft temperature data in the shaft temperature dataset besides the transition data.

[0037] Figure 1 The illustration schematically depicts an application scenario of the shaft temperature early warning monitoring method, apparatus, device, medium, and program product according to embodiments of the present disclosure.

[0038] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a server 103, and a network 104. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, and the server 103. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc. The first terminal device 101 may be transportation equipment, such as a train.

[0039] Users can use the first terminal device 101 and the second terminal device 102 to interact with the server 103 via the network 104 to receive or send messages, etc. Various communication client applications, such as data analysis software, can be installed on the first terminal device 101 and the second terminal device 102 (for example only).

[0040] The second terminal device 102 can be various electronic devices with a display screen, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0041] Server 103 can be a server that provides various services, such as a back-end management server that supports the shaft temperature early warning monitoring device used by the user using the first terminal device 101 and the second terminal device 102 (this is just an example). The back-end management server can analyze and process the received user requests or data such as shaft temperature from sensors, 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] It should be noted that the axle temperature early warning monitoring method provided in this embodiment can generally be executed by server 103. Correspondingly, the axle temperature early warning monitoring device provided in this embodiment can generally be installed in server 103. The axle temperature early warning monitoring method provided in this embodiment can also be executed by a server or server cluster that is different from server 103 and capable of communicating with the first terminal device 101, the second terminal device 102, and / or server 103. Correspondingly, the axle temperature early warning monitoring device provided in this embodiment can also be installed in a server or server cluster that is different from server 103 and capable of communicating with the first terminal device 101, the second terminal device 102, and / or server 103. Alternatively, the axle temperature early warning monitoring device provided in this embodiment can also be installed in a terminal device and executed directly by the terminal device.

[0043] 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.

[0044] The following will be based on Figure 1 The described scene, through Figures 2-6 The shaft temperature early warning monitoring method according to the embodiments of this disclosure will be described in detail.

[0045] Figure 2 A flowchart illustrating a shaft temperature early warning monitoring method according to an embodiment of the present disclosure is shown. Figure 3 The diagram illustrates, schematically, shaft temperature profiles for both real and unreal fault scenarios according to embodiments of the present disclosure. The following is in conjunction with... Figure 2 and Figure 3 Detailed explanation.

[0046] like Figure 2 As shown, the shaft temperature early warning monitoring method of this embodiment includes operations S210 to S240.

[0047] In operation S210, the shaft temperature dataset from the sensor is analyzed to obtain the temperature rise rate data.

[0048] According to embodiments of this disclosure, the axle temperature dataset consists of axle temperature data acquired through real-time temperature monitoring of train mechanical components such as axle boxes, gearboxes, and motor bearings using sensors. The temperature rise rate data characterizes the rate of temperature change over time, i.e., the relationship between the temperature rise rate and time. The temperature rise rate can be positive or negative. By acquiring the axle temperature data monitored in real-time by sensors, the temperature rise rate can be calculated based on time and the amount of temperature change.

[0049] In operation S220, the time interval for the transition from the first temperature rise rate to the second temperature rise rate is determined based on the temperature rise rate data.

[0050] According to embodiments of this disclosure, the transition between the first temperature rise rate and the second temperature rise rate can be a transition between a positive temperature rise rate and a negative temperature rise rate, but it is not limited to this. In some embodiments, it can be a transition between a positive or negative temperature rise rate and 0 (i.e., the temperature remains unchanged). However, the transition between positive and negative temperature rise rates is more in line with the jump law.

[0051] According to embodiments of this disclosure, a time interval is determined based on the time corresponding to the first temperature rise rate and the time corresponding to the second temperature rise rate. The duration of this time interval is compared with a preset threshold. For example, in the temperature rise rate data, if the time corresponding to the first temperature rise rate is 18:03:00 and the time corresponding to the second temperature rise rate is 18:23:00, then the determined time interval is [18:03:00, 18:23:00].

[0052] In operation S230, if the length of the time interval is less than or equal to a preset threshold, the shaft temperature data in the shaft temperature dataset that corresponds to the time interval is identified as jump data.

[0053] According to embodiments of this disclosure, a preset threshold is used to characterize the time required for a change in the rate of temperature rise caused by a real fault in a train's mechanical components. For example... Figure 3 The diagram shows temperature curves plotted from three types of axle temperature data. Temperature changes caused by actual faults are relatively gradual and last for a relatively long time, while upward and downward temperature jumps show rapid abrupt changes. Temperature changes are small when there is no actual fault or no jump. The preset threshold can be obtained based on analysis of train parameters and historical axle temperature data. Illustratively, train parameters can include train type, train number, car number, and train speed, etc.

[0054] According to embodiments of this disclosure, when the length of the current time interval is less than or equal to a preset threshold, the duration of the temperature rise rate change is insufficient to reach the duration of the temperature rise rate change during a real fault, but the temperature rise rate exhibits a change process. Therefore, the change in the temperature rise rate is determined to be a temperature rise rate change not caused by a real fault, and is identified as jump data. For example... Figure 3As shown, the shaft temperature data caused by non-real faults exhibits steep upward and downward jumps that are short-lived.

[0055] In operation S240, early warning monitoring is performed based on other shaft temperature data in the shaft temperature dataset, excluding jump data.

[0056] According to embodiments of this disclosure, in the actual working scenario of monitoring train axle temperature, it is necessary to issue early warnings for temperature rise phenomena caused by real faults, and to avoid issuing early warnings for temperature rise phenomena caused by non-real faults (such as axle temperature data jumps). Therefore, after confirming that the temperature rise rate within a time interval is jump data, jump data can be excluded or removed, and early warning monitoring can be performed on other data within the axle temperature dataset.

[0057] According to embodiments of this disclosure, since the inherent characteristic of inertia in heat transfer and heat dissipation of mechanical components has been discovered, the duration of temperature rise changes under actual fault conditions differs from the duration of temperature jumps caused by sensor malfunctions, etc. Therefore, the duration of the temperature rise rate change is used as the time feature for identifying temperature jumps to determine whether the current temperature rise rate change is abnormal. This eliminates temperature rise data caused by non-real faults such as sensor malfunctions during the early warning monitoring process, greatly reducing the probability of false alarms and improving train operation efficiency and stability.

[0058] Figure 4 A flowchart illustrating an abnormal data early warning method caused by a real fault according to an embodiment of this disclosure is shown schematically. Figure 4 As shown, the shaft temperature early warning monitoring method also includes operations S310 to S320.

[0059] In operation S310, if the length of the time interval is greater than a preset threshold, the shaft temperature data in the shaft temperature dataset that corresponds to the time interval is identified as abnormal data.

[0060] According to an embodiment of this disclosure, in operation S310, if the length of the time interval is greater than a preset threshold, indicating that the duration of the temperature rise rate change reaches the duration of the temperature rise rate change when a real fault occurs, then the change in the temperature rise rate is determined to be a temperature rise rate change caused by a real fault, and is determined to be abnormal data.

[0061] When operating the S320, under the condition that the sensor itself is in normal performance, an early warning is issued for abnormal data.

[0062] According to an embodiment of this disclosure, in operation S320, before issuing an early warning for the temperature rise phenomenon, the performance of the sensor itself is further checked. If the sensor is in normal condition, it is determined that the abnormal data is a temperature rise caused by a real fault, so as to further eliminate false alarms caused by sensor faults and reduce the probability of false alarms.

[0063] Figure 5 A flowchart illustrating a shaft temperature early warning monitoring method according to another embodiment of the present disclosure is shown.

[0064] like Figure 5 As shown, the shaft temperature early warning monitoring method of this disclosure includes S510 to S530:

[0065] When operating S510, temperature rise rate jump detection is performed, which specifically includes: obtaining shaft temperature dataset based on the temperature signal detected by the sensor; analyzing the shaft temperature dataset, and determining that the shaft temperature data corresponding to the time interval of temperature rise rate change is jump data if the length of the time interval is less than or equal to a preset threshold; otherwise, it is determined to be abnormal data.

[0066] When operating the S520, sensor fault diagnosis specifically includes: detecting the sensor's own performance status based on the temperature signal detected by the sensor. Specific detection methods can be implemented using algorithms known in the art, and will not be elaborated upon here.

[0067] When operating the S530, a shaft temperature warning is issued based on the temperature rise rate jump detection results and sensor fault diagnosis results, and the warning results are output. Specifically, the warning is issued when the shaft temperature data is determined to be abnormal (i.e. no jump data is reported) and the sensor itself is in normal performance condition.

[0068] According to an embodiment of this disclosure, in operation S210, analyzing the shaft temperature dataset from the sensor to obtain temperature rise rate data includes: calculating the temperature change rate at each moment relative to the previous moment based on the shaft temperature data at different times in the shaft temperature dataset to obtain initial temperature rise rate data; and smoothing the initial temperature rise rate data to obtain temperature rise rate data.

[0069] According to embodiments of this disclosure, a dataset of shaft temperature data monitored in real time by a sensor is obtained, wherein each shaft temperature data point can be the temperature corresponding to a certain moment. For example: 12:30:20, shaft temperature is 25°C; 12:40:20, shaft temperature is 26°C; 12:50:20, shaft temperature is 24°C, etc.

[0070] According to an embodiment of this disclosure, the initial data of the temperature rise rate can be further calculated based on the shaft temperature data at different times using the following formula (1):

[0071] dT t =(T t -T t-1 ) / dt (1);

[0072] Among them, dT t Let T be the initial value of the temperature rise rate at time t. t Let T be the shaft temperature at time t.t-1 Let dt be the shaft temperature at time t-1, and dt be the interval between time t and time t-1.

[0073] For example, if the temperature at time t increases by 0.03℃ relative to time t-1 at a sensor sampling frequency of 1000Hz, then the initial temperature rise rate at time t is a positive temperature rise rate of 30℃ / s.

[0074] Furthermore, the obtained initial temperature rise rate data can be smoothed according to formula (2) to obtain a smooth temperature rise rate curve within this time period.

[0075]

[0076] Among them, P t Let dT be the smoothed temperature rise rate at time t. t dT t+i dT t-i The initial values ​​of the temperature rise rate at time t, time t+i, and time ti are given in the initial temperature rise rate data, where n is 1, 2, or 3.

[0077] According to embodiments of this disclosure, the temperature rise rate data obtained by smoothing can reduce data noise to prevent interference from noisy data, which is beneficial for accurately determining whether the preset temperature rise rate, namely the first temperature rise rate and the second temperature rise rate, has been reached.

[0078] According to embodiments of this disclosure, a temperature rise rate curve can be generated based on the smoothed temperature rise rate data. Figure 6 A temperature rise rate curve according to an embodiment of the present disclosure is illustrated schematically. Figure 6 As shown, in the axial temperature curve coordinate system, the horizontal axis represents time, and the vertical axis represents the temperature rise rate. With the accumulation of time and the temperature rise rate, a fluctuating curve can be formed along the X-axis. The degree of fluctuation of the curve varies with the temperature rise rate; when the temperature rise rate is positive, the curve is above the X-axis; when the temperature rise rate is negative, the curve is below the X-axis. Compared to the black curve showing no jump, the gray curve shows the case with a downward jump.

[0079] According to embodiments of this disclosure, the method of this disclosure is applied to vehicle operation detection, wherein one of the first temperature rise rate and the second temperature rise rate is a positive temperature rise rate used to predict that the vehicle can operate normally, and the other is the maximum negative temperature rise rate obtained by statistical analysis based on historical jump data.

[0080] According to embodiments of this disclosure, the first temperature rise rate or the second temperature rise rate can be set based on train parameters and historical temperature rise rates. Indicatively, train parameters can be the train model. For example, the real-time axle temperature system of an AA-type EMU is set to a temperature rise rate ≥8℃ / min as a prediction, indicating normal vehicle operation; a temperature rise rate ≥10℃ / min as a warning, requiring the vehicle to reduce speed; and a temperature rise rate ≥15℃ / min, requiring the vehicle to stop for inspection. Therefore, taking a positive first temperature rise rate P1 and a negative second temperature rise rate P2 as an example, i.e., taking an upward jump in temperature rise rate data, for this AA-type EMU, the first temperature rise rate P1 should be set before the warning temperature rise rate, for example, 8℃ / min, to ensure normal vehicle operation. The second temperature rise rate P2 can be the maximum negative temperature rise rate achievable by approximately 98% or more of the historical jump data, which can be determined by statistically analyzing historical temperature rise rate data and actual jump situations.

[0081] According to embodiments of this disclosure, setting the first temperature rise rate or the second temperature rise rate to a positive temperature rise rate and a negative temperature rise rate, respectively, can be used to determine the duration of temperature rise and fall, which is more accurate than judging the temperature rise or temperature fall state alone.

[0082] According to embodiments of this disclosure, when the actual temperature rise rate reaches a first temperature rise rate P1, the current time is determined as the first time t1. For example, if the actual temperature rise rate satisfies P1 ± 1℃, then the first temperature rise rate P1 is considered to have been reached. If the actual positive temperature rise rate at the current time 09:05:00 is 8.5℃ / min, then t1 can be determined as 09:05:00.

[0083] Similarly, when the actual temperature rise rate reaches the second temperature rise rate P2, the current time can be defined as the second time t2. For example, if the actual temperature rise rate satisfies P2 ± 1℃, then the second temperature rise rate P2 can be considered to have been reached.

[0084] According to an embodiment of this disclosure, in operation S220, determining the time interval for the transition from the first temperature rise rate to the second temperature rise rate based on the temperature rise rate data includes: determining a first moment corresponding to the first temperature rise rate and a second moment corresponding to the second temperature rise rate in the monotonically changing temperature rise rate data; and using the time interval between the first moment and the second moment as the time interval for the transition from the first temperature rise rate to the second temperature rise rate.

[0085] According to embodiments of this disclosure, a first moment is used to characterize the moment corresponding to a first temperature rise rate in the temperature rise rate data, such as... Figure 6 The second time point is shown at time t3; the second time point is used to characterize the time corresponding to the second temperature rise rate in the temperature rise rate data, as shown in the example. Figure 6The time interval is shown at time t1. Time t2, however, does not exhibit a monotonically changing process with time t1 and is not used to determine the time interval. According to an embodiment of this disclosure, the time length between the first time t3 and the second time t1 can be represented by Δt, where Δt = t1 - t3.

[0086] According to embodiments of this disclosure, based on the inherent mechanical characteristics, namely the inertia of heat transfer and dissipation, the temperature rise phenomenon caused by non-real fault conditions is accurately eliminated. The duration of temperature change is used as a judgment condition and compared with a preset threshold to determine whether it is jump data or abnormal data, thereby further improving the accuracy of the shaft temperature early warning monitoring method.

[0087] According to embodiments of this disclosure, the preset threshold is 30 to 60 seconds.

[0088] According to embodiments of this disclosure, the preset threshold for the time duration is 30–60 seconds. This preset threshold is used to characterize the time required for the first and second temperature rise rates to change under actual fault conditions.

[0089] The preset threshold can be obtained based on train parameters and historical actual temperature rise data, as shown in Table 1.

[0090]

[0091] According to an embodiment of this disclosure, exemplarily, based on the jump data and abnormal data under actual faults in Table 1, it can be seen that for 10 jump abnormal data, only 1 historical data exceeds the preset threshold, which can reduce false alarm faults by about 90%.

[0092] According to embodiments of this disclosure, the shaft temperature dataset is a shaft temperature dataset of axle box, gearbox, or motor bearing; the shaft temperature early warning monitoring method further includes: acquiring historical jump data and historical abnormal data that conform to the mechanical characteristics of the axle box, gearbox, or motor bearing; and updating a preset threshold based on the historical jump data and historical abnormal data.

[0093] According to embodiments of this disclosure, historical jump data is used to characterize jump data caused by non-real faults in historical data, and historical abnormal data is used to characterize abnormal data caused by real faults in historical data. During long-term train use, the axle temperature varies due to wear and tear on various components or changes in the duration of temperature sensing. Different train parameters result in different axle temperatures. Therefore, preset thresholds for different train parameters can be updated based on historical jump data and historical abnormal data.

[0094] For example, it can acquire recent historical temperature fluctuation data and historical anomaly data for different trains. The recent time range can be measured in months or years, such as the last 3 months, last 6 months, last 9 months, last 12 months, last two years, and last three years. Furthermore, a preset threshold can be selected to distinguish between historical temperature fluctuation data and historical anomaly data. For example, the preset threshold should ensure that the duration of temperature rise rate changes in 100% of the historical anomaly data is greater than the preset threshold, while the duration of temperature rise rate changes in at least 90% of the historical temperature fluctuation data should be less than or equal to the preset threshold.

[0095] According to embodiments of this disclosure, by determining a preset threshold based on historical data, the actual usage and data of the train under real conditions can be further determined based on different train models, train speeds, etc., thereby further improving monitoring accuracy and reducing the probability of false alarms.

[0096] Based on the above-described shaft temperature early warning monitoring method, this disclosure also provides a shaft temperature early warning monitoring device. The following will be combined with... Figure 7 The device is described in detail.

[0097] Figure 7 A schematic block diagram of a shaft temperature early warning monitoring device according to an embodiment of the present disclosure is shown.

[0098] like Figure 7 As shown, the shaft temperature early warning monitoring device 700 of this embodiment includes an analysis module 710, a first determination module 720, a second determination module 730, and an early warning monitoring module 740.

[0099] The analysis module 710 is used to analyze the shaft temperature dataset from the sensor to obtain temperature rise rate data. In one embodiment, the analysis module 710 can be used to perform the operation S210 described above, which will not be repeated here.

[0100] The first determining module 720 is used to determine the time interval between the transition from the first temperature rise rate to the second temperature rise rate based on the temperature rise rate data. In one embodiment, the first determining module 720 can be used to perform the operation S220 described above, which will not be repeated here.

[0101] The second determining module 730 is used to determine the shaft temperature data corresponding to the time interval in the shaft temperature data set as jump data when the length of the time interval is less than or equal to a preset threshold, wherein the preset threshold is 30 to 70 seconds. In one embodiment, the second determining module 730 can be used to perform the operation S230 described above, which will not be repeated here.

[0102] The early warning monitoring module 740 is used to perform early warning monitoring based on other shaft temperature data in the shaft temperature dataset, excluding jump data. In one embodiment, the early warning monitoring module 740 can be used to perform the operation S240 described above, which will not be repeated here.

[0103] According to embodiments of this disclosure, the shaft temperature early warning monitoring device 700 further includes:

[0104] The third determination module is used to determine the shaft temperature data corresponding to the time interval in the shaft temperature data set as abnormal data when the length of the time interval is greater than a preset threshold.

[0105] The early warning module is used to issue early warnings for abnormal data when the sensor itself is in normal working order.

[0106] According to embodiments of this disclosure, the first determining module 720 includes:

[0107] The first determining submodule is used to determine, from the monotonically changing temperature rise rate data, the first time corresponding to the first temperature rise rate and the second time corresponding to the second temperature rise rate.

[0108] The second determining submodule is used to take the time interval between the first time moment and the second time moment as the time interval for the transition from the first temperature rise rate to the second temperature rise rate.

[0109] According to embodiments of this disclosure, the axle temperature early warning monitoring method is applied to vehicle operation detection. One of the first temperature rise rate and the second temperature rise rate is a positive temperature rise rate used to predict that the vehicle can operate normally, and the other is the maximum negative temperature rise rate obtained by statistical analysis based on historical jump data.

[0110] According to embodiments of this disclosure, the analysis module 710 includes:

[0111] The calculation submodule is used to calculate the rate of temperature change at each moment relative to the previous moment based on the shaft temperature data at different times in the shaft temperature dataset, and to obtain the initial data of the temperature rise rate.

[0112] The processing submodule is used to smooth the initial temperature rise rate data to obtain the temperature rise rate data.

[0113] According to embodiments of this disclosure, the shaft temperature dataset is a shaft temperature dataset of the axle box, gearbox, or motor bearing; the shaft temperature early warning monitoring device 700 further includes:

[0114] The acquisition module is used to acquire historical jump data and historical anomaly data that match the mechanical characteristics of the axle box, gearbox, or motor bearing;

[0115] The update module is used to update preset thresholds based on historical jump data and historical abnormal data.

[0116] According to embodiments of this disclosure, any plurality of modules among the analysis module 710, the first determination module 720, the second determination module 730, and the early warning monitoring module 740 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the analysis module 710, the first determination module 720, the second determination module 730, and the early warning monitoring module 740 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 implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the analysis module 710, the first determination module 720, the second determination module 730, and the early warning monitoring module 740 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0117] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a shaft temperature early warning monitoring method according to an embodiment of the present disclosure.

[0118] like Figure 8 As shown, an electronic device 800 according to an embodiment of this disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage portion 808 into a random access memory (RAM) 803. The processor 801 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 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this disclosure.

[0119] RAM 803 stores various programs and data required for the operation of electronic device 800. Processor 801, ROM 802, and RAM 803 are interconnected via bus 804. Processor 801 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 802 and / or RAM 803. It should be noted that the programs may also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0120] According to embodiments of this disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to a bus 804. The electronic device 800 may also include one or more of the following components connected to the I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0121] 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.

[0122] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but 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 may 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. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 802 and / or RAM 803 and / or one or more memories other than ROM 802 and RAM 803 described above.

[0123] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the item recommendation method provided in the embodiments of this disclosure.

[0124] When the computer program is executed by the processor 801, 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.

[0125] 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 809, and / or installed from a removable medium 811. 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.

[0126] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by processor 801, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0127] 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 the user's computing device, partially on the 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).

[0128] 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 this 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.

[0129] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined 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.

[0130] 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. The scope of this disclosure is defined by the appended claims and their equivalents. 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 method for early warning and monitoring of axle temperature, applied in vehicle operation monitoring, the method comprising: The shaft temperature dataset from the sensor is analyzed to obtain the temperature rise rate data; Based on the temperature rise rate data, the time interval from the first temperature rise rate to the second temperature rise rate is determined. One of the first temperature rise rate and the second temperature rise rate is a positive temperature rise rate used to predict that the vehicle can operate normally, and the other is the maximum negative temperature rise rate obtained by statistical analysis of historical jump data. If the length of the time interval is less than or equal to a preset threshold, the shaft temperature data in the shaft temperature dataset that corresponds to the time interval is identified as jump data. Early warning monitoring is performed based on other shaft temperature data in the shaft temperature dataset, excluding the jump data. If the length of the time interval is greater than the preset threshold, the shaft temperature data in the shaft temperature dataset that corresponds to the time interval will be identified as abnormal data. If the sensor itself is in normal working order, an early warning will be issued for the abnormal data.

2. The method according to claim 1, wherein, The step of determining the time interval for the transition from the first temperature rise rate to the second temperature rise rate based on the temperature rise rate data includes: In the monotonically changing temperature rise rate data, determine the first time corresponding to the first temperature rise rate and the second time corresponding to the second temperature rise rate; The time interval between the first moment and the second moment is defined as the time interval between the transition from the first temperature rise rate to the second temperature rise rate.

3. The method according to claim 1, wherein, The analysis of the shaft temperature dataset from the sensor to obtain the temperature rise rate data includes: Based on the shaft temperature data at different times in the shaft temperature dataset, calculate the rate of temperature change at each time relative to the previous time to obtain the initial data of the temperature rise rate. The initial temperature rise rate data is smoothed to obtain the temperature rise rate data.

4. The method according to claim 1, wherein, The shaft temperature dataset is a shaft temperature dataset of a shaft box, gearbox, or motor bearing; the method further includes: Obtain historical jump data and historical anomaly data that correspond to the mechanical characteristics of the axle box, gearbox, or motor bearing; The preset threshold is updated based on the historical jump data and the historical anomaly data.

5. The method according to claim 1 or 4, wherein, The preset threshold is 30~60s.

6. A shaft temperature early warning monitoring device, applied in vehicle operation monitoring, the device comprising: The analysis module is used to analyze the shaft temperature dataset from the sensor to obtain the temperature rise rate data; The first determining module is used to determine the time interval from the first temperature rise rate to the second temperature rise rate based on the temperature rise rate data. One of the first temperature rise rate and the second temperature rise rate is a positive temperature rise rate used to predict that the vehicle can operate normally, and the other is the maximum negative temperature rise rate obtained by statistical analysis based on historical jump data. The second determining module is used to determine the shaft temperature data corresponding to the time interval in the shaft temperature data set as jump data when the length of the time interval is less than or equal to a preset threshold. as well as The early warning monitoring module is used to perform early warning monitoring based on other shaft temperature data in the shaft temperature dataset besides the jump data; The third determining module is used to determine the shaft temperature data corresponding to the time interval in the shaft temperature data set as abnormal data when the length of the time interval is greater than a preset threshold. The early warning module is used to issue an early warning for the abnormal data when the sensor itself is in normal working order.

7. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 5.

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