Fault judgment method of temperature sensor and self-powered temperature sensing device

Abstract

The invention discloses a fault judgment method of a temperature sensor and a self-powered temperature sensing device. The fault judgment method comprises the following steps: acquiring a voltage value and a current value output by a thermoelectric power generation unit in real time; acquiring a real-time temperature value of the temperature sensor in real time; acquiring a real-time environment temperature; and comparing the voltage value, the current value, the real-time environment temperature and the real-time temperature value, and judging whether the temperature sensor breaks down or not according to a comparison result. According to the self-powered temperature sensing device, whether the temperature sensor breaks down or not can be judged through the fault judgment method, the sensor does not need to be detached for independent testing, the workload is reduced, the battery does not need to be replaced through the self-powered temperature sensing device, the labor cost and time cost of maintenance are greatly reduced, and meanwhile the situation that when the battery of the sensor is replaced, the battery is damaged is avoided. And the problem of data collection pause is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of sensors, and in particular relates to a fault judgment method of a temperature sensor and a self-powered temperature sensing device. Background Art

[0002] As a commonly used temperature monitoring element, the temperature sensor provides the temperature information of the target to be measured for the subsequent monitoring system or control system. Since the temperature sensor is usually arranged at a remote end, it cannot be discovered in time when the temperature sensor fails. For example, when the temperature value measured by the temperature sensor has a small deviation, it is difficult for the staff to find it; in the prior art, the following methods are usually used to judge the poor temperature sensing: for the PT100 temperature sensor, the resistance value between the two lines of the sensor is usually detected at room temperature using the resistance range of the multimeter to determine whether the sensor is faulty, but this method requires the sensor to be disassembled, which is a large workload. Summary of the invention

[0003] In order to overcome the above technical defects, the present invention provides a fault judgment method of a temperature sensor and a self-powered temperature sensing device, which realize fault diagnosis of the temperature sensor.

[0004] The present invention is achieved through the following scheme:

[0005] A method for determining a fault of a temperature sensor, wherein the temperature sensor is connected to a temperature difference power generation unit, wherein the temperature difference power generation unit is used to provide power for the temperature sensor, and the method for determining a fault comprises the following steps:

[0006] Obtain the voltage and current values ​​output by the temperature difference power generation unit in real time;

[0007] Get the real-time temperature value of the temperature sensor in real time;

[0008] Get real-time ambient temperature;

[0009] The voltage value, current value, real-time ambient temperature and real-time temperature value are compared, and whether the temperature sensor fails is determined based on the comparison result.

[0010] As a further improvement of the present invention, the step of comparing the voltage value, the current value, the real-time ambient temperature and the real-time temperature value, and judging whether the temperature sensor fails according to the comparison result, comprises:

[0011] Setting a mapping relationship table between the power generation of the temperature difference power generation unit and the temperature difference;

[0012] Calculate the real-time temperature difference between the real-time temperature value of the temperature sensor and the ambient temperature value;

[0013] Calculate the real-time power generation of the temperature difference power generation unit according to the voltage value and the current value;

[0014] Obtain the temperature difference corresponding to the real-time power generation from the mapping relationship table;

[0015] If the difference between the real-time temperature difference and the temperature difference is not within the set range, it is determined that the temperature sensor is faulty.

[0016] As a further improvement of the present invention, in the step of comparing the voltage value, current value, real-time ambient temperature and real-time temperature value, and determining whether the temperature sensor fails based on the comparison result, characteristic parameters are extracted from the voltage value, current value, real-time temperature value and real-time ambient temperature, and input into the neural network model to obtain a judgment result.

[0017] As a further improvement of the present invention, the present invention also includes the step of constructing a neural network model:

[0018] Obtaining historical voltage values ​​and historical current values ​​of the temperature difference power generation unit, and extracting historical characteristic parameters; wherein the historical voltage values ​​and historical current values ​​are generated during the production test phase;

[0019] Obtain historical temperature values ​​and historical ambient temperature values ​​of the temperature sensor, calculate historical temperature differences between the historical temperature values ​​and the historical ambient temperature values, and extract historical characteristic parameters;

[0020] Constructing a neural network model with historical feature parameters;

[0021] Divide historical characteristic parameters and historical temperature sensor fault status data into a training set and a test set;

[0022] The historical characteristic parameters in the training set are used as input variables of the neural network, and the historical temperature sensor fault status data in the training set are used as output variables of the neural network model to train the neural network model;

[0023] The historical feature parameters in the test set are used as the input variables of the neural network, and the historical temperature sensor fault status data in the training set is used as the output variable of the neural network model to test the neural network model.

[0024] As a further improvement of the present invention, if the test accuracy of the neural network model is less than a set threshold, the historical characteristic parameters and the historical temperature sensor fault state data are re-divided into a training set and a test set;

[0025] Retrain the neural network model until the test accuracy is greater than or equal to the set threshold.

[0026] As a further improvement of the present invention, after the step of comparing the voltage value, the current value and the real-time temperature value and judging whether the temperature sensor fails according to the comparison result, the following steps are also included:

[0027] If the temperature sensor is determined to be faulty, an alarm signal is generated.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: since the thermoelectric power generation unit is very sensitive to the temperature difference between the cold and hot ends, when the temperature difference changes, it will directly lead to a change in the power generation, and under relatively stable environmental conditions, there is a corresponding relationship between the temperature difference of the thermoelectric power generation unit and the output voltage and current values. Based on this, according to the relationship between the voltage and current values ​​output by the thermoelectric power generation unit and the real-time temperature value of the temperature sensor, it is possible to determine whether the temperature sensor is faulty, without the need to remove the sensor for separate testing, thereby reducing workload.

[0029] In addition, the present invention also provides a self-powered temperature sensing device, comprising: a probe rod, a power supply unit, a control unit and a plurality of temperature difference power generation units; a temperature measurement unit is arranged at the first end of the probe rod; a plurality of temperature difference power generation units are arranged around the surface of the probe rod; an input end of the power supply unit is connected to the temperature difference power generation unit to obtain electric energy, an output end of the power supply unit is connected to the temperature measurement unit to supply power to the temperature measurement unit, the control unit is connected to the temperature difference power generation unit to obtain a current value and a voltage value, and the control unit is connected to the temperature measurement unit to obtain a real-time temperature value;

[0030] The control unit includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the above-mentioned fault judgment method.

[0031] As a further improvement of the present invention, the thermoelectric power generation unit comprises: a thermoelectric power generation sheet and a heat dissipation unit;

[0032] The hot end of the thermoelectric power generation sheet is in contact with the probe rod, and the cold end of the thermoelectric power generation sheet is in contact with the heat dissipation unit;

[0033] The temperature difference power generation sheet is connected to the power supply unit.

[0034] As a further improvement of the present invention, the heat dissipation unit comprises: a contact portion and a heat dissipation fin assembly;

[0035] One end of the contact portion contacts the probe rod, and the other end is provided with the heat dissipation fin assembly.

[0036] As a further improvement of the present invention, the present invention also includes a heat conductive block, a first surface of the heat conductive block contacts the temperature difference power generation unit, a second surface of the heat conductive block contacts the probe rod, and a shape of the second surface of the heat conductive block matches the surface of the probe rod.

[0037] Compared with the prior art, the beneficial effects of the present invention are: a temperature difference power generation unit is used to self-power the temperature sensor. Compared with the existing sensors using batteries, the present invention does not require battery replacement, greatly reducing the labor cost and time cost of maintenance. At the same time, it avoids the problem of suspending data collection when the sensor replaces the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0039] Figure 1 This is a flow chart of the fault judgment method described in Example 1;

[0040] Figure 2 This is a flow chart of the fault judgment method described in Example 2;

[0041] Figure 3 This is a schematic diagram of the structure of the self-powered sensor device described in Example 3;

[0042] Figure 4 This is a schematic diagram of the circuit structure of the self-powered sensor device described in Example 3;

[0043] Figure 5 This is a schematic diagram of the structure of the self-powered temperature sensing device described in Example 3.

[0044] Marking description: 1. Probe rod; 2. Power supply unit; 3. Temperature difference power generation unit; 31. Temperature difference power generation sheet; 32. Heat dissipation unit; 321. Contact part; 322. Heat dissipation fin; 4. Heat conduction block; 5. Housing; 6. Control unit; 7. Wireless communication circuit; 8. Display unit; 9. Temperature measurement unit. DETAILED DESCRIPTION

[0045] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0046] It should be noted that similar reference numerals and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in the subsequent figures. At the same time, in the description of the present invention, the serial numbers of each step are only used to distinguish between steps, and do not mean that each step needs to be strictly executed in the order of the serial numbers.

[0047] Example 1

[0048] This embodiment provides a method for determining a fault of a temperature sensor. The temperature sensor is connected to a temperature difference power generation unit, and the temperature difference power generation unit is used to provide power for the temperature sensor. Figure 1 As shown, the fault judgment method includes the steps of:

[0049] S101, obtaining the voltage value and current value output by the temperature difference power generation unit in real time;

[0050] S102, obtaining a real-time temperature value of a temperature sensor in real time;

[0051] S103, obtaining real-time ambient temperature;

[0052] S104, comparing the voltage value, the current value, the real-time ambient temperature and the real-time temperature value, and judging whether the temperature sensor fails according to the comparison result. Specifically, step S104 includes:

[0053] S1041, setting a mapping relationship table between the power generation of the temperature difference power generation unit and the temperature difference;

[0054] Taking into account that the power generation of the thermoelectric power generation unit is related to the logarithm, material and size of its P / N type semiconductor, after the thermoelectric power generation unit is manufactured, these parameters have been determined and will not change again; the power generation of the thermoelectric power generation unit is also directly related to the temperature difference across the P / N type semiconductor, that is, in actual use, the power generation of the thermoelectric power generation unit is mainly related to the temperature difference across the P / N type semiconductor. After the thermoelectric power generation unit is manufactured, corresponding tests can be carried out to determine the mapping relationship table between the power generation of the thermoelectric power generation unit and the temperature difference, and the mapping relationship table can be stored in advance.

[0055] S1042: Calculate the real-time temperature difference between the real-time temperature value of the temperature sensor and the ambient temperature value.

[0056] Considering that the temperature sensor is installed at a fixed position during use, and will only be replaced or changed in the event of a fault, the installation position of the temperature sensor is relatively fixed, and its corresponding ambient temperature value should also be relatively stable or change periodically over time (years). For example, the ambient temperature value of a temperature sensor installed indoors may be roughly the same, or change regularly according to the operating conditions of the equipment; and the ambient temperature value of a temperature sensor installed outdoors also changes regularly according to the regular changes of the four seasons of each year. Therefore, the ambient temperature data (the comparison curve of time and ambient temperature) can be stored in advance, and the corresponding ambient temperature value can be queried according to the time. In addition, if the user can provide a real-time ambient temperature value, it can also be obtained through wireless communication.

[0057] S1043, calculating the real-time power generation of the temperature difference power generation unit according to the voltage value and the current value. For example, the real-time power generation can be calculated using the following formula:

[0058] Among them, I out is the current value, V out is the voltage value.

[0059] S1044, obtaining a temperature difference corresponding to the real-time power generation from the mapping relationship table;

[0060] S1045: If the difference between the real-time temperature difference and the temperature difference is not within the set range, it is determined that the temperature sensor is faulty.

[0061] S105: If the temperature sensor is judged to be faulty, an alarm signal is generated. The temperature sensor fault can be prompted by an audible or visual alarm, or the alarm signal can be sent to a monitoring terminal to remind relevant staff.

[0062] When the temperature difference power generation unit generates a large amount of electricity, it means that the temperature difference between the cold end and the hot end of the temperature difference power generation is large. At the same time, it also means that the temperature of the liquid in the measured pipeline is high. If the temperature displayed by the temperature sensor is too low or too high at this time, it means that the temperature sensor has a fault. The fault judgment method of this embodiment can realize the rapid judgment of the temperature sensor fault. In this embodiment, the mapping relationship table is adopted, which is simple to deploy and is relatively more suitable for scenarios with a small number of temperature sensors. Because there is no need for big data processing, fault judgment can be performed without the help of external equipment. At the same time, targeted adjustments and optimizations can be made according to the usage scenarios to make the prediction more accurate and can be flexibly applied to different scenarios.

[0063] Example 2

[0064] This embodiment provides another method for determining a fault of a temperature sensor. The temperature sensor is connected to a temperature difference power generation unit, and the temperature difference power generation unit is used to provide power for the temperature sensor. Figure 2 As shown, the fault judgment method includes the steps of:

[0065] S201, obtaining the voltage value and current value output by the temperature difference power generation unit in real time;

[0066] S202, obtaining a real-time temperature value of a temperature sensor in real time;

[0067] S203, obtaining real-time ambient temperature;

[0068] S204, extracting characteristic parameters from the voltage value, current value, real-time temperature value, and real-time ambient temperature, inputting them into the neural network model, obtaining a judgment result, and judging whether the temperature sensor is faulty based on the comparison result.

[0069] Installing a temperature sensor on a pipeline to detect the temperature of the fluid in the pipeline is one of the application scenarios of this embodiment. After the temperature sensor is installed on the pipeline to be tested, the installation position will not be replaced or changed unless a fault occurs, that is, the position of the temperature sensor is relatively fixed, and the fluid in the pipeline is also relatively fixed, so the temperature value of the fluid in the pipeline, the ambient temperature value, and the current value, voltage value and other parameters of the thermoelectric power generation unit have the characteristics of data flow. The historical data of these parameters are analyzed using data flow technology to extract the dynamic trend of its operation process. On this basis, feature vectors such as the power generation and temperature difference of the thermoelectric power generation unit are constructed respectively, and artificial neural networks are used for learning and training, thereby realizing a self-powered temperature sensor fault judgment model based on neural networks. Finally, the voltage value, current value, real-time ambient temperature and real-time temperature value of the thermoelectric power generation unit collected in real time are analyzed to extract feature data, and these feature data are used as inputs of the BP neural network to predict the operating status of the self-powered temperature sensor.

[0070] Furthermore, the neural network model in step S204 is constructed by the following steps:

[0071] S2041. Obtain historical voltage values ​​and historical current values ​​of the temperature difference power generation unit, and extract historical characteristic parameters; wherein the historical voltage values ​​and historical current values ​​are generated in the production test phase.

[0072] After the temperature sensor is installed and used, a temperature difference is formed at both ends of the thermoelectric power generation unit. According to the Seebeck effect, the heat energy is directly converted into electrical energy. The current value and voltage value output by the thermoelectric power generation unit can be collected, and then the power generation and other data can be calculated. The characteristic parameters extracted by this technical solution based on the power generation historical data stream are as follows. Preferably, the time window is selected as every hour, and less preferably, the time window is selected as every half hour. In this embodiment, the characteristic parameters may include the following parameters:

[0073] (1) Average current I avg : reflects the current level during this period of time. The calculation formula is:

[0074]

[0075] Among them, I n is the current sampling value, and N is the total number of current acquisition points in the time window.

[0076] (2) Average voltage U avg: Reflects the voltage level during this time period. The calculation formula is:

[0077]

[0078] Among them, U n is the voltage sampling value, and N is the total number of voltage acquisition points in the time window.

[0079] (3) Average current rising slope avg_I_up:

[0080]

[0081] Among them: i=1,2......N_Iup; I_up i is the slope of the data segment with a positive slope, and N_Iup is the number of current rising areas observed in this time period.

[0082] (4) Average current down slope avg_I_down:

[0083]

[0084] Among them: i=1,2......N_Idown; I_down i is the slope of the data segment with a negative slope, and N_Idown is the number of current drop regions observed in this time period.

[0085] (5) Average voltage rising slope avg_U_up:

[0086]

[0087] Among them: i=1,2......N_Uup; U_up i is the slope of the data segment with a positive slope, and N_Uup is the number of voltage rising areas observed in this time period.

[0088] (6) Average voltage down slope avg_U_down:

[0089]

[0090] Among them: i=1,2......N_Udown; U_down i is the slope of the data segment with a negative slope, and N_Udown is the number of voltage drop regions observed in this time period.

[0091] (7) Maximum current Imax and minimum current Imin:

[0092] The maximum and minimum values ​​of the current collected during this time period.

[0093] (8) Maximum voltage Umax and minimum voltage Umin:

[0094] The maximum and minimum values ​​of the voltage collected during this time period.

[0095] S2042, obtaining historical temperature values ​​and historical ambient temperature values ​​of the temperature sensor, calculating the historical temperature difference between the historical temperature values ​​and the historical ambient temperature values, and extracting historical characteristic parameters.

[0096] After the self-powered temperature sensor is installed and used, it will collect the measured temperature value of the fluid in the measured pipeline at a set frequency, and simultaneously obtain the ambient temperature value, and then calculate the temperature difference. Preferably, the time window is consistent with the historical data stream of power generation, preferably, it is selected as every hour, and less preferably, the time window is selected as every half hour.

[0097] In this embodiment, the characteristic parameters may include the following parameters:

[0098] (1) Average temperature difference T avg : reflects the temperature difference level during this period of time. The calculation formula is:

[0099]

[0100] Among them, T n is the temperature difference sampling value, and N is the total number of temperature difference collection points in the time window.

[0101] (2) Average rising slope of temperature difference avg_T_up:

[0102]

[0103] Where: i=1,2......N_Tup; T_up i is the slope of the data segment with a positive slope, and N_Tup is the number of temperature difference rising zones observed in this time period.

[0104] (3) Average downward slope of temperature difference avg_T_down:

[0105]

[0106] Among them: i=1,2......N_Tdown; I_down i is the slope of the data segment with a negative slope, and N_Tdown is the number of temperature difference drop zones observed in this time period.

[0107] (4) The average value of temperature difference swing Tsw reflects the stability of temperature difference data. Its calculation formula is:

[0108] (When T n+1-T n ≥10 hours)

[0109] Among them, T n is the sampling value of the temperature difference, N is the total number of temperature difference collection points in the time window, and M is the number of points where the temperature difference deviates, that is, the number of temperature difference swings. The temperature difference swing value is the absolute value of the difference between the current sampling value and the previous sampling value. If the temperature swing value is greater than 10°C, the value is accumulated, the number of swings M is increased by 1, and the average value within the time period is calculated, that is, the average value of the temperature difference swing within the time period is obtained.

[0110] (5) Mean square error Tσ of temperature difference data stream:

[0111]

[0112] Among them, N is the amount of data flow in this time period, T i is the value of the i-th temperature difference data.

[0113] (6) Waveform fluctuation value Tμ:

[0114]

[0115] S2043. Constructing a neural network model with historical feature parameters:

[0116] S = net(I avg , U avg , avg_I_up, avg_I_down, avg_U_up, avg_U_down, Imax, Imin, Umax, Umin, T avg , avg_T_up, avg_T_down, Tsw, Tσ, Tμ)

[0117] S2044. Divide the historical characteristic parameters and historical temperature sensor fault status data into a training set and a test set.

[0118] S2045. Use the historical feature parameters in the training set as input variables of the neural network, use the historical temperature sensor fault status data in the training set as output variables of the neural network model, and train the neural network model.

[0119] S2046. Use the historical voltage values, historical current values, historical temperature values, and historical ambient temperature values ​​in the test set as input variables of the neural network, and use the temperature sensor fault status data in the training set as output variables of the neural network model to test the neural network model.

[0120] If the test accuracy of the neural network model is less than the set threshold, the historical voltage values, historical current values, historical temperature values, historical ambient temperature values, and temperature sensor fault status data are re-divided into training sets and test sets; the neural network model is retrained until the test accuracy is greater than or equal to the set threshold.

[0121] S205: If the temperature sensor is judged to be faulty, an alarm signal is generated. The temperature sensor fault can be prompted by an audible or visual alarm, or the alarm signal can be sent to a monitoring terminal to remind relevant staff.

[0122] When the temperature difference power generation unit generates a large amount of electricity, it means that the temperature difference between the cold end and the hot end of the temperature difference power generation is large. At the same time, it also means that the temperature of the liquid in the measured pipeline is high. If the temperature displayed by the temperature sensor is too low or too high at this time, it means that the temperature sensor has a fault. The fault judgment method of this embodiment can realize the rapid judgment of the temperature sensor fault. This embodiment adopts a neural network model, which can be suitable for scenarios with a large number of temperature sensors installed. It can have enough new data for continuous verification and optimization, so that the prediction of the neural network model can be more accurate.

[0123] Example 3

[0124] With the rise of the Internet of Things era and the rapid development of communication methods such as 5G and WIFI, more and more sensors use wireless communication technology for signal transmission. Because it does not require instrumentation and control wiring, nor does it require the addition of signal acquisition ports in the instrumentation and control system, it has been widely used in the industrial field. However, existing wireless sensors are usually powered by batteries. Due to the limited power of the battery, the battery needs to be replaced regularly, which requires high maintenance manpower and time costs. Moreover, during the battery replacement period, the sensor cannot collect data normally and cannot provide continuous protection for the operation of industrial equipment. In addition, existing wireless sensors usually reduce the frequency of data collection and data transmission to extend the battery life, which also affects their use in scenarios where equipment operating parameters need to be monitored in a timely manner.

[0125] In order to solve the above problems, this embodiment provides a self-powered temperature sensing device, which can be applied to pipelines and has the function of wireless data transmission, such as Figure 3 and Figure 4As shown, it includes: a probe rod 1, a power supply unit 2, a control unit 6 and a plurality of temperature difference power generation units 3; a temperature measurement unit is arranged at the first end of the probe rod 1; a plurality of temperature difference power generation units 3 are arranged around the surface of the probe rod 1; the input end of the power supply unit 2 is connected to the temperature difference power generation unit 3 to obtain electric energy, the output end of the power supply unit 2 is connected to the temperature measurement unit, and is used to power the temperature measurement unit, the control unit 6 is connected to the temperature difference power generation unit 3 to obtain current value and voltage value, the control unit 6 is connected to the temperature measurement unit to obtain real-time temperature value, and the control unit 6 includes a processor and a memory, and the memory stores at least one instruction, at least one program, code set or instruction set, and at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the fault judgment method in embodiment 1 or embodiment 2. The power supply unit 2 is used to convert the electric energy of the temperature difference power generation unit 3 into the electric energy required by the temperature measurement unit, and also has a voltage stabilization function.

[0126] Specifically, the probe rod 1 has a cavity inside, and the thermocouple is installed in the cavity. When in use, the first end with the thermocouple is removed and inserted into the pipeline. The surface of the probe rod 1 is flat, so that the temperature difference power generation unit 3 can be close to the probe rod 1, that is, the cross-section of the probe rod 1 can be a triangle, square, pentagon, etc.

[0127] Alternatively, the probe rod 1 is set to be cylindrical, and is provided with a heat conducting block 4, the first surface of the heat conducting block 4 is in contact with the thermoelectric power generation unit 3, the second surface of the heat conducting block 4 is in contact with the probe rod 1, and the shape of the second surface of the heat conducting block 4 matches the surface of the probe rod 1. Specifically, the second surface is an inwardly concave semicircle, so as to fit with the cylindrical probe rod 1. Thus, the thermoelectric heating sheet can obtain the temperature of the probe rod 1 to the greatest extent.

[0128] Preferably, the heat conducting block 4 can be made of a metal material that is resistant to high temperatures, corrosion, and has strong thermal conductivity, such as aluminum, copper, or an alloy thereof.

[0129] In addition, thermal conductive material can be filled on the interface between the probe rod 1 and the heat conductive block 4, and between the heat conductive block 4 and the thermoelectric power generation sheet 31, so that the temperature of the hot end of the thermoelectric power generation sheet 31 is as close as possible to the temperature of the surface of the probe rod 1; similarly, thermal conductive material is filled on the interface between the thermoelectric power generation sheet 31 and the heat dissipation unit 32, so that the temperature of the cold end surface of the thermoelectric power generation unit 3 is as close as possible to the temperature of the heat dissipation unit 32, so as to further improve the thermal conductivity efficiency.

[0130] For ease of installation, the outer surface of the first end of the probe rod 1 is provided with threads, which are adapted to the threaded holes on the measured pipe. The probe rod 1 can be tightened and fixed on the measured pipe, and the relevant parameters of the fluid in the pipe can be sensed through the probe rod 1.

[0131] The power supply unit 2 and the control unit 6 are placed in the housing 5. To facilitate installation, the second end of the probe rod 1 is provided with a thread, and the lower end of the housing 5 is provided with a threaded hole. The thread matches the threaded hole so that the housing 5 can be tightened on the probe rod 1.

[0132] Furthermore, the thermoelectric power generation unit 3 includes: a thermoelectric power generation sheet 31 and a heat dissipation unit 32; the hot end of the thermoelectric power generation sheet 31 is in contact with the probe rod 1, and the cold end of the thermoelectric power generation sheet 31 is in contact with the heat dissipation unit 32; the thermoelectric power generation sheet 31 is connected to the power supply unit 2. Based on this, the heat of the probe rod 1 is transferred to the thermoelectric power generation sheet 31, and then transferred to the external environment through the heat dissipation unit 32, so that the cold end and the hot end of the thermoelectric power generation unit 3 form a temperature difference, thereby generating electrical energy.

[0133] The heat dissipation fin assembly includes: a plurality of heat dissipation fins 322, which are arranged on the contact portion 321 in a divergent shape, and the material of the heat dissipation fins 322 is preferably aluminum, copper, iron or alloys thereof. The thickness of each heat dissipation fin 322 is 0.5-3 mm, preferably 1 mm, 1.5 mm or 2 mm. Selecting a heat dissipation fin 322 with a thinner thickness can facilitate manual adjustment of the position of the heat dissipation fin 322. When assembling the heat dissipation unit 32, in order to provide sufficient assembly space, the heat dissipation fins 322 at both ends can be gathered together. After the assembly is completed, the position of the heat dissipation fin 322 is manually adjusted to make the heat dissipation fins 322 of the entire heat dissipation unit 32 more divergent.

[0134] Furthermore, a plurality of through holes are provided on the surface of the heat dissipation fins 322, which can enhance the flow of the fluid around the fin surface and improve the heat dissipation efficiency.

[0135] In addition, if Figure 5 As shown, this embodiment also includes; a wireless communication circuit 7, a processor connected to the wireless communication circuit 7 and a temperature measurement unit, the processor controls the temperature measurement unit to obtain the sensed temperature of the measured object, and controls the wireless communication circuit 7 to send the sensed temperature to an external detection system.

[0136] Furthermore, this embodiment further includes a display unit 8 and a control unit 6 for displaying the sensed temperature.

[0137] This embodiment uses a temperature difference power generation unit to self-power the sensor. Compared with existing sensors using batteries, the present invention does not require battery replacement, greatly reducing the labor cost and time cost of maintenance, and avoiding the problem of suspending data collection when the sensor replaces the battery. At the same time, there is no need to consider the power issue, and the sensor's collection frequency and data sending frequency can be set according to actual usage.

[0138] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for determining a temperature sensor fault, characterized in that: The temperature sensor is connected to a temperature difference power generation unit, and the temperature difference power generation unit is used to provide power for the temperature sensor. The fault judgment method comprises the steps of: Obtain the voltage and current values ​​output by the temperature difference power generation unit in real time; Get the real-time temperature value of the temperature sensor in real time; Get real-time ambient temperature; The voltage value, current value, real-time ambient temperature and real-time temperature value are compared, and whether the temperature sensor fails is determined based on the comparison result.

2. The fault diagnosis method according to claim 1, characterized in that: The step of comparing the voltage value, the current value, the real-time ambient temperature and the real-time temperature value, and judging whether the temperature sensor fails according to the comparison result, comprises: Setting a mapping relationship table between the power generation of the temperature difference power generation unit and the temperature difference; Calculate the real-time temperature difference between the real-time temperature value of the temperature sensor and the ambient temperature value; Calculate the real-time power generation of the temperature difference power generation unit according to the voltage value and the current value; Obtain the temperature difference corresponding to the real-time power generation from the mapping relationship table; If the difference between the real-time temperature difference and the temperature difference is not within the set range, it is determined that the temperature sensor is faulty.

3. The fault diagnosis method according to claim 1, characterized in that: In the step of comparing the voltage value, current value, real-time ambient temperature and real-time temperature value, and judging whether the temperature sensor fails according to the comparison result, characteristic parameters are extracted from the voltage value, current value, real-time temperature value and real-time ambient temperature, and input into the neural network model to obtain the judgment result.

4. The fault judgment method according to claim 3, characterized in that: It also includes the steps for building a neural network model: Obtaining historical voltage values ​​and historical current values ​​of the temperature difference power generation unit, and extracting historical characteristic parameters; wherein the historical voltage values ​​and historical current values ​​are generated during the production test phase; Obtain historical temperature values ​​and historical ambient temperature values ​​of the temperature sensor, calculate historical temperature differences between the historical temperature values ​​and the historical ambient temperature values, and extract historical characteristic parameters; Constructing a neural network model with historical feature parameters; Divide historical characteristic parameters and historical temperature sensor fault status data into a training set and a test set; The historical characteristic parameters in the training set are used as input variables of the neural network, and the historical temperature sensor fault status data in the training set are used as output variables of the neural network model to train the neural network model; The historical feature parameters in the test set are used as the input variables of the neural network, and the historical temperature sensor fault status data in the training set is used as the output variable of the neural network model to test the neural network model.

5. The fault judgment method according to claim 4, characterized in that: If the test accuracy of the neural network model is less than the set threshold, the historical characteristic parameters and historical temperature sensor fault status data are re-divided into a training set and a test set; Retrain the neural network model until the test accuracy is greater than or equal to the set threshold.

6. The fault diagnosis method according to any one of claims 1 to 7, characterized in that: After the step of comparing the voltage value, the current value and the real-time temperature value and determining whether the temperature sensor fails according to the comparison result, the following steps are also included: If the temperature sensor is determined to be faulty, an alarm signal is generated.

7. A self-powered temperature sensing device, characterized in that: include: A probe rod, a power supply unit, a control unit and a plurality of temperature difference power generation units; a temperature measurement unit is arranged at the first end of the probe rod; a plurality of temperature difference power generation units are arranged around the surface of the probe rod; an input end of the power supply unit is connected to the temperature difference power generation unit to obtain electric energy, an output end of the power supply unit is connected to the temperature measurement unit to supply power to the temperature measurement unit, the control unit is connected to the temperature difference power generation unit to obtain current value and voltage value, and the control unit is connected to the temperature measurement unit to obtain real-time temperature value; The control unit includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the fault judgment method as described in any one of claims 1 to 6.

8. The self-powered temperature sensing device according to claim 7, characterized in that: The thermoelectric power generation unit comprises: a thermoelectric power generation sheet and a heat dissipation unit; The hot end of the thermoelectric power generation sheet is in contact with the probe rod, and the cold end of the thermoelectric power generation sheet is in contact with the heat dissipation unit; The temperature difference power generation sheet is connected to the power supply unit.

9. The self-powered temperature sensing device according to claim 8, characterized in that: The heat dissipation unit comprises: a contact portion and a heat dissipation fin assembly; One end of the contact portion contacts the probe rod, and the other end is provided with the heat dissipation fin assembly.

10. The self-powered temperature sensing device according to claim 7, characterized in that: It also includes a heat conductive block, a first surface of the heat conductive block contacts the temperature difference power generation unit, a second surface of the heat conductive block contacts the probe rod, and a shape of the second surface of the heat conductive block matches the surface of the probe rod.

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