Methods and systems for multiple point temperature measurement with sensor matrix

Abstract

In various forms, methods and systems are provided for measuring a thermal performance of a thermal component. A method includes: providing a plurality of sensors coupled between one or more intermediate wires; providing at least two lead wires coupled to the plurality of sensors; receiving, by a control module, sensor data from the at least two lead wires; determining, by the control module, a resistance associated with each of the plurality of sensors based on the sensor data from the at least two lead wires; determining, by the control module, a temperature at each of the plurality of sensors based on the respective resistance; and generating, by the control module, temperature data of the thermal component based on the temperature at each of the plurality of sensors.

Description

Attorney Docket No.: 0100S-000393-WO-POAMETHODS AND SYSTEMS FOR MULTIPLE POINT TEMPERATUREMEASUREMENT WITH SENSOR MATRIXCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to and the benefit of U.S. Patent Application No. 63 / 715,072, filed on November 1 , 2024. The disclosure of the above application is incorporated herein by reference.FIELD

[0002] The present disclosure relates to thermal / temperature sensors, and more particularly to methods and systems for measuring a temperature distribution of a thermal component, such as by way of example a resistive heater.BACKGROUND

[0003] Thermal systems such as electric heating applications generally include one or more heaters that convert electric energy into heat. In one particular heating application, an electric heater generally includes one or more heating elements, wherein heat is generated by passing electric current through a resistive material of each of the heating elements.

[0004] A temperature sensor may be mounted to such thermal systems or other thermal systems, or in a nearby vicinity, in order to determine the temperature of the thermal system and, in some cases, to control the power being provided to the thermal system in order to control its temperature or temperature distribution. The temperature sensor provides temperature data only at the location at which the temperature sensor is mounted, and thus temperature variations along or throughout the area of the thermal system are unknown without the use of additional discrete temperature sensors. Including additional temperature sensors in the thermal system can increase the wiring and controls complexity, and overall cost of the thermal system.

[0005] These challenges related to determining multiple temperatures of a thermal system are addressed by the present disclosure.Attorney Docket No.: 0100S-000393-WO-POASUMMARY

[0006] This section provides a general summary of the disclosure and is not a comprehensive disclosure of its full scope or all of its features.

[0007] Methods and systems are provided for measuring a thermal performance of a thermal component. In one form, a method includes: providing a plurality of sensors coupled between one or more intermediate wires; providing at least two lead wires coupled to the plurality of sensors; receiving, by a control module, sensor data from the at least two lead wires; determining, by the control module, a resistance associated with each of the sensors based on the sensor data from the at least two lead wires; determining, by the control module, a temperature at each of the plurality of sensors based on the respective resistance; and generating, by the control module, temperature data of the thermal component based on the temperature at each of the plurality of sensors.

[0008] In variations of the method, which may be implemented individually or in any combination: the determining the resistance associated with each of the sensors includes determining a plurality of measured resistances between a plurality of at least two lead wires; the determining the resistance associated with each of the sensors further includes determining a change in the plurality of measured resistances; and the determining the resistance associated with each of the sensors further includes determining a change in the resistance associated with each of the sensors based on the change in the plurality of measured resistances; the determining the resistance is based on a linear model of a change in resistances; the determining the resistance is based on a machine learning model. In various forms, the machine learning model includes a match model that associates a resistance of each sensor with different measurement combinations; and one or more of the plurality of sensors comprises a resistive temperature device.

[0009] In another form, a system includes: a sensing unit comprising at least two lead wires coupled to a grid of coupled sensors; one or more processors; and a computer-readable storage medium storing instructions which, when executed by the one or more processors, cause the one or more processors to: receive sensor data from the at least two lead wires coupled to the grid of coupled sensors; determine a resistance associated with each of the sensors based on the sensor data from each of the at least two lead wires; determine a temperature at each of the sensors basedAttorney Docket No.: 0100S-000393-WO-POA on the respective resistance; and generate temperature data of the thermal component based on the temperature at each of the sensors.

[0010] In variations of the system, which may be implemented individually or in any combination: the one or more processors determines the resistance associated with each of the sensors by determining a plurality of measured resistances between a plurality of two lead wires; the one or more processors determines the resistance associated with each of the sensors by determining a change in the plurality of measured resistances; the one or more processors determines the resistance associated with each of the sensors by determining a change in the resistance associated with each of the sensors based on the change in the plurality of measured resistances; the one or more processors determines the resistance based on a linear model of a change in resistances; the one or more processors determines the resistance based on a machine learning model; the machine learning model includes a match model that associates a resistance of each sensor with different measurement combinations; and one or more of the sensors comprises a resistive temperature device.

[0011] In another form, a computer-readable storage device storing instructions which, when executed by one or more processors, cause the one or more processors to: receive sensor data from at least two lead wires coupled to a grid of coupled sensors; determine a resistance associated with each of the sensors based on the sensor data from each of the at least two lead wires; determine a temperature at each of the sensors based on the respective resistance; and generate temperature data based on the temperature at each of the sensors.

[0012] In variations of the computer-readable storage device, which may be implemented individually or in any combination: the instructions cause the one or more processors to determine the resistance associated with each of the sensors by determining a plurality of measured resistances between a plurality of two lead wires; the instructions cause the one or more processors to determine the resistance associated with each of the sensors by determining a change in the plurality of measured resistances and determine the change in the resistance associated with each of the sensors based on the change in the plurality of measured resistances; the instructions cause the one or more processors to determine the resistance associated with each of the sensors based on a linear model of a change in resistances; and theAttorney Docket No.: 0100S-000393-WO-POA instructions cause the one or more processors to determine the resistance associated with each of the sensors based on a machine learning model.

[0013] Further areas of applicability will become apparent from the description provided herein. It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order that the disclosure may be well understood, there will now be described various forms thereof, given by way of example, reference being made to the accompanying drawings, in which:

[0015] FIG. 1 is a functional block diagram illustrating an exemplary thermal performance measurement system associated with a thermal component of a thermal system in accordance with various forms of the present disclosure;

[0016] FIG. 2 is a functional block diagram illustrating an exemplary sensing unit of the thermal performance measurement system in accordance with various forms of the present disclosure;

[0017] FIG. 3 is a dataflow diagram illustrating an exemplary control module of the thermal performance measurement system in accordance with various forms of the present disclosure;

[0018] FIG. 4 is a flowchart illustrating an exemplary process that may be performed by the control module of the thermal performance measurement system in accordance with various forms of the present disclosure; and

[0019] FIG. 5 is a flowchart illustrating an exemplary process that may be performed by the control module of the thermal performance measurement system in accordance with various forms of the present disclosure.

[0020] The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.DETAILED DESCRIPTION

[0021] The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features.

[0022] With reference now to FIG. 1 , a thermal performance measurement system is shown generally at 100. The thermal performanceAttorney Docket No.: 0100S-000393-WO-POA measurement system 100 measures observable conditions of a thermal component of a thermal system such as, for example, a heater component 102 and provides thermal performance data 121 based thereon. In various forms, the thermal performance measurement system 100 provides thermal performance data including temperature data predicted at multiple locations of the heater component 102. In various forms, the thermal performance measurement system 100 predicts the temperature data relating to the multiple locations of the heater component 102 based on one or more computer models as disclosed herein. For exemplary purposes, the disclosure will be discussed in the context of the heater component 102. As can be appreciated, the thermal performance measurement system 100 is configured to provide thermal performance data 121 associated with multiple locations of any thermal component and is not limited to the heater component 102.

[0023] As shown in FIG. 1 , an example heater component 102 may include a heater plate (not shown) that includes at least one resistive element that is configured to generate heat. The resistive element can be in the form of a wire (e.g., straight, coiled, wound, etc.) or other geometrical shape (e.g., sheets, films, etc.) embedded within or upon a surface of, for example, the plate. As can be appreciated, the heater component 102 may be any number of other types of constructions, such as by way of example, a tubular heater, a cartridge heater, a layered heater, open / exposed element, or any type of heater construction that includes a resistive material that functions to provide heat to a heating target or environment according to specific application requirements.

[0024] In various forms, the heater component 102 and the thermal performance measurement system 100 are incorporated into industrial equipment (e.g., heat exchangers, semiconductor equipment, injection molding equipment, chemical processing, exhaust emissions), scientific equipment, motor vehicles, and powerplants, among many others, to provide improved temperature and / or temperature information of such equipment / applications. As can be appreciated, these particular examples are non-limiting and are illustrated and described herein for the sake of demonstrating exemplary forms of the present disclosure. Accordingly, it should be understood that the thermal performance measurement system 100 may be configured to provide temperature information associated with a resistive element(s) or other functional element of any type of application or system while remaining within the scope of the present disclosure.Attorney Docket No.: 0100S-000393-WO-POA

[0025] In various forms, the thermal performance measurement system 100 generally includes a sensing unit 106 configured to measure observable conditions of the heater component 102, and to communicate measured data 107 to a control module 108. In various forms, the sensing unit 106 includes multiple (n) sensors configured at multiple points of the heater component 102 coupled between intermediate wires according to a pattern such as a grid pattern having, (described in greater detail below), for example, rows and columns. The sensors can be, for example, resistive temperature devices (RTDs). As will be discussed in more detail, the pattern is arranged in such a way that the number of lead wires to the sensors is less than 2*n. The temperature at each sensor point in the pattern is determined based on a predicted resistance at each sensor according to the systems and processes disclosed herein. As can be appreciated, predicting the resistances and thus, the temperatures in this manner reduces the number of lead wires for measuring the data.

[0026] For example, as shown in FIG. 2, an exemplary sensing unit 106 includes a grid 200 that couples a plurality of sensors (s1-s84) according to a grid pattern having rows and columns. While the grid 200 is shown as a rectangle, in various forms, the grid 200 may be implemented as a rectangle, a square, a circle, an oval, u-shaped, polygonal, or other shape or configuration, in either two dimensional or three dimensional space. While eighty four sensors (s1-s84) are shown, it is appreciated that a grid having various numbers of sensors can be implemented in various forms. As can be appreciated, the plurality of sensors (s1-S84) can be implemented according to patterns other than a grid in various embodiments as the disclosure is not limited to the present grid example.

[0027] As shown in FIG. 2, the sensing unit 106 further includes lead wires (L1-L8) coupled to the grid 200. As can be appreciated, more or less lead wires can be implemented in various embodiments. Each of the lead wires (L1-L8) provides measurement data indicating the observable condition of the lead wire (e.g., resistance of L1-L8) and the grid 200 coupled thereto during operation of the sensing unit 106.

[0028] With reference back to FIG. 1 , the control module 108 receives the measured data 107 from the sensing unit 106. In various forms, the control module 108 includes an input device 110, an output device 112, at least one processor 114 and memory 116 and, optionally, a communication device 118. As can be appreciated,Attorney Docket No.: 0100S-000393-WO-POA the control module 108 and / or one or more of the components thereof may be integrated with or local to the sensing unit 106 and / or may be implemented separate from or remote from the sensing unit 106 in various forms (e.g., in as part of a cloud or network environment).

[0029] In various forms, the input device 110 is configured to receive the measured data 107 from the sensing unit 106 and / or other devices. Similarly, the output device 112 is configured to provide thermal performance data 121 to one or more temperature evaluation devices, such as, but not limited to a notification device, a temperature control device, and / or an analysis device, among others. In various forms, the communication device 118 is configured to receive data and / or communicate data including the thermal performance data 121 to a remote system or other entity for further evaluation by way of a wired or wireless communication protocol.

[0030] In various forms, the processor 114 can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the processor 114, a semiconductor based microprocessor (in the form of a microchip or chip set), a macro processor, any combination thereof, or generally any device for executing instructions. Although only one processor 114 is shown, various form of the thermal performance measurement system 100 can include any number of processors 114 that communicate over any suitable communication medium or a combination of communication mediums and that cooperate to process the sensor signals, perform logic, calculations, methods, processes and / or algorithms, and generate control signals to the output device 112 or the communication device 118 and / or to automatically control features of the heater component 102 or other components (not shown) of the thermal system.

[0031] The memory 116 may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the processor 114 is powered down. The memory 116 may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some ofAttorney Docket No.: 0100S-000393-WO-POA which represent executable instructions, used by the processor 114 in controlling the operations of the thermal performance measurement system 100.

[0032] In various forms, the memory 116 includes a data storage device 120 that stores predefined data, measured data, and predicted data associated with the heater component 102 and / or the resistive element and used by the thermal performance measurement system 100 to provide the thermal performance data 121. In various forms, the memory 116 further stores programming instructions, which may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions associated with the methods and processes described herein. The instructions, when executed by the processor 114, perform logic, calculations, methods and / or algorithms of predefined methods or processes. In various forms, one or more of the programming instructions are embodied in and are configured to implement the methods, processes, and systems described herein for receiving the measured data 107, determining the temperature at multiple locations of the heater component 102, and providing the thermal performance data 121 , as will be described further below. As such, the thermal performance measurement system 100 is configured to provide thermal performance data 121 , including temperature data at multiple locations of the heater component 102 as opposed to temperature information localized at a single location.

[0033] Referring now to FIG. 3, and with continued reference to FIG. 1 , a dataflow diagram illustrates various forms of elements of the control module 108 of the thermal performance measurement system 100 in accordance with the present disclosure. That is, suitable software and / or hardware components of the control module 108 (e.g., the processor 114 and the memory 116) are utilized to provide the temperature information of, for example, the heater component 102 or other thermal component. In various forms, inputs to the control module 108 may be received from the input device 110, received from other modules (not shown) associated with the heater component 102, received from the communication device 118, and / or determined / modeled by other sub-modules (not shown) within the control module 108.

[0034] In various forms, the instructions of the thermal performance measurement system 100 may be organized by function or system into sub-modules and / or datastores of the control module 108. For example, as shown in FIG. 3, the control module 108 includes a data measurement module 300, a sensor point resistance determination module 302, and a temperature data determination moduleAttorney Docket No.: 0100S-000393-WO-POA304. As can be appreciated, in various other forms the sub-modules shown may be combined or further partitioned to similarly provide the thermal performance data 121.

[0035] In various forms, the data measurement module 300 receives as input sensor data 306 including the measured data 107 generated by the sensing unit 106 in response to operation of the sensing unit 106. The data measurement module 300 preprocesses the sensor data 306 to provide measured resistance data (R1-Rx), referred to as measured resistance data 308.

[0036] The data measurement module 300 determines each resistance R1 , R2, R3, ... Rx as the measured resistance between two lead wires. For example, given the example sensing unit 106 of FIG. 2 having eighty-four sensors (s1-s84) and eight lead wires (L1-L8), at least eighty-four resistances (R1-R84) are measured using various combinations of at least two lead wires of the eight lead wires (L1-L8) (e.g., R1 (L1 , L2), R2 (L2, L3), ... Rx(L1-L2, L3)). In various forms, the eighty-four different combinations are determined based on an adjacency of the lead wires and / or the proximity of the sensors (s1-s84) to the lead wires (L1-L8). As can be appreciated, more combinations of two lead wires and / or of more than two lead wires can be used in various forms to provide more accurate results.

[0037] In various forms, the sensor point resistance determination module 302 receives as input the measured resistance data 308. The sensor point resistance determination module 302 computes a resistance value (n) for each of the (n) sensor points (e.g., ri-rn). For example, given the example sensing unit 106 of FIG. 2 having eighty-four sensors (s1 -s84) and eight lead wires (L1 -L8), a set of eighty-four resistance values are determined:using the set of measured resistance values:

[0040] In various forms, the sensor point resistance determination module 302 computes, for each measurement in [ / ?], a change in the resistance dR based on a difference in the measured resistance R and an initial resistance Roto produce the set of changes in measured resistances:Attorney Docket No.: 0100S-000393-WO-POA

[0041] [dB] = [B] - [B]o. (3)

[0042] The sensor point resistance determination module 302 uses the computed changes [dB] to compute the changes in resistances of each the sensors:

[0043] [dr] = [B][dB] / n (4)

[0044] where n is determined by [dR]. For example, initially [dR] may be too large, n may be 10 or even larger. When [dR] becomes smaller n can be smaller, for example, up to the value 1. [B] has been determined as the inverse relationship of [A]:

[0045] [B] = [4]"1, (5) whereand A has been determined from making a single change in r, such as to r2and:B is then determined by solving for all values in A:

[0049] The sensor point resistance determination module 302 then computes the set of resistances [r] of the sensor points based on the determined change in resistances [dr] (e.g., iteratively until the relationship between r and R is linear) using:

[0050] [r] = [r]0+ [dr], (9) where [r]0represents initial resistance estimates, resistance estimates from a last measurement, or resistance estimates from a last iteration. The sensor point resistance determination module 302 then provides the computed resistances for each of the sensor points as resistance data 310.

[0051] In various alternative forms or in various additional forms, the sensor point resistance determination module 302 predicts a resistance value (r) for each sensor point based on a second model that uses a neural network or other machine learning model that is trained to predict the resistances (r1 -rn) at each sensor point (s1-sn) from the measurements R1-Rx. In various forms, the second modelAttorney Docket No.: 0100S-000393-WO-POA includes a multiple linear regression model, an artificial neural network model, support vector machine model, a random forest model, or a combination thereof. In various forms, the model may be trained in a supervised or unsupervised manner. For example, a match model that associates the resistance of each sensor with different measurement combinations is defined. The match model is then used to create datasets for training the machine learning model.

[0052] In various forms, the sensor point resistance determination module 302 retrieves a predefined model from a model datastore and processes the measurements [ft] with the retrieved model to provide the predicted resistances [r]. The sensor point resistance determination module 302 may selectively update the retrieved model based on the processing. Upon completion of the processing, the sensor point resistance determination module 302 then provides the resistances for each of the sensor points as sensor point resistance data 312.

[0053] In various forms, the temperature data determination module 304 receives as input the sensor point resistance data 310, and / or the sensor point resistance data 312. When both the sensor point resistance data 310 and the sensor point resistance data 312 are received, the received data 310, 312 is compared and selectively used to compute final resistance data (e.g., the data is averaged, the best of the two is selected, etc.). When only the sensor point resistance data 310 or the sensor point resistance data 312 is provided, the provided resistance data 310 or 312 is used as the final resistance data.

[0054] The temperature data determination module 304 determines a temperature at each sensor point (T1-Tn) based on the final resistance data (r1-rn). For example, for each predicted resistance, the temperature data determination module 304 determines a corresponding temperature based on a temperature equation and a coefficient of resistance or based on an interpolated lookup table that stores the relationships between resistance and temperature. The temperature data determination module 304 provides the thermal performance data 121 based on the computed temperatures (T1-Tn) for each of the sensor points.

[0055] Referring now to FIG. 4 and with continued reference to FIGS. 1- 3, a flowchart illustrates a process 400 that can be performed by, for example, the modules 300, 302, 304 of the control module 108 of the thermal performance measurement system 100 in accordance with the present disclosure. As can be appreciated in light of the disclosure, the order of operation within the process is notAttorney Docket No.: 0100S-000393-WO-POA limited to the sequential execution as illustrated in FIG, 4 but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. In various forms, the process 400 can be scheduled to run independently or in a defined order based on one or more predetermined events associated with the heater component 102.

[0056] In one example, the process 400 may begin at 405. The sensor data 206 is received at 410. The sensor data 206 is pre-processed at 420 to determine the measured resistances (R1-Rx).

[0057] The measured resistances R1-Rx are then used to compute the resistance data r1-rn at 430 using, for example, the first linear model. Alternatively, or additionally, the measured resistances R1-Rx are used to predict the resistance data r1-rn at 440 using, for example, the second machine learning model.

[0058] If predicted resistances are provided from both models, optionally the predicted resistances are compared and the final resistances are determined at 450. The temperature data T1 -Tn is then computed based on the final resistances r1- rn at 460. The thermal performance data 121 including the temperature data T1-Tn is provided for evaluation, control, notification, etc. at 470. Thereafter, the process 400 may end at 480.

[0059] With reference now to FIG. 5, a process of computing the resistances as recited in step 430 of FIG. 4 is shown. In one example, the process 500 may begin at 505. The change in the resistances [dB] based on a difference in the measured resistances [ / ?] and initial resistances [r]0is computed at 510. Thereafter, it is determined whether the change in resistances is not less than a threshold (e.g., T4) at 520, the process iterates at 530 and 540. For example, the change in resistance is computed from [B] and the change in the resistances [dB] at 530 and the sensor resistances are computed from the initial sensor resistances [r]0and the change in sensor resistances [dB] at 540.

[0060] Once the change in resistances is less than the threshold at 520, the sensor resistance is set to the initial resistance at 550. Thereafter, the process 500 may end at 560.

[0061] In accordance with the disclosure, novel methods and systems are provided for predicting the thermal performance of a thermal component at multiple locations using a grid configuration of sensors and the processes disclosed herein.Attorney Docket No.: 0100S-000393-WO-POA

[0062] Unless otherwise expressly indicated herein, all numerical values indicating mechanical / thermal properties, compositional percentages, dimensions and / or tolerances, or other characteristics are to be understood as modified by the word “about” or "approximately" in describing the scope of the present disclosure. This modification is desired for various reasons including industrial practice, material, manufacturing, and assembly tolerances, and testing capability.

[0063] As used herein, the phrase at least one of A, B, and C should be construed to mean a logical (A OR B OR C), using a non-exclusive logical OR, and should not be construed to mean “at least one of A, at least one of B, and at least one of C.”

[0064] In this application, the term “controller” and / or “module” may refer to, be part of, or include: an Application Specific Integrated Circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system-on- chip.

[0065] The term memory is a subset of the term computer-readable medium. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave); the term computer-readable medium may therefore be considered tangible and non-transitory. Non-limiting examples of a non-transitory, tangible computer-readable medium are nonvolatile memory circuits (such as a flash memory circuit, an erasable programmable read-only memory circuit, or a mask readonly circuit), volatile memory circuits (such as a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (such as an analog or digital magnetic tape or a hard disk drive), and optical storage media (such as a CD, a DVD, or a Blu-ray Disc).

[0066] The apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general-purpose computer to execute one or more particular functions embodied in computer programs. The functional blocks, flowchart components, and other elementsAttorney Docket No.: 0100S-000393-WO-POA described above serve as software specifications, which can be translated into the computer programs by the routine work of a skilled technician or programmer. The computations can be done in local computer(s) or computer(s) in a network or in a cloud.

[0067] The description of the disclosure is merely exemplary in nature and, thus, variations that do not depart from the substance of the disclosure are intended to be within the scope of the disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the disclosure.

Claims

Attorney Docket No.: 0100S-000393-WO-POACLAIMSWhat is claimed is:1 . A method for measuring a thermal performance of a thermal component, the method comprising: providing a plurality of sensors coupled between one or more intermediate wires; providing at least two lead wires coupled to the plurality of sensors; receiving, by a control module, sensor data from the at least two lead wires; determining, by the control module, a resistance associated with each of the plurality of sensors based on the sensor data from the at least two lead wires; determining, by the control module, a temperature at each of the plurality of sensors based on the respective resistance; and generating, by the control module, temperature data of the thermal component based on the temperature at each of the plurality of sensors.

2. The method of claim 1 , wherein the determining the resistance associated with each of the plurality of sensors comprises determining a plurality of measured resistances between a plurality of at least two lead wires.

3. The method of claim 2, wherein the determining the resistance associated with each of the plurality of sensors further comprises determining a change in the plurality of measured resistances.

4. The method of claim 3, wherein the determining the resistance associated with each of the plurality of sensors further comprises determining a change in the resistance associated with each of the plurality of sensors based on the change in the plurality of measured resistances.

5. The method of claim 1 , wherein the determining the resistance is based on a linear model of a change in resistances.Attorney Docket No.: 0100S-000393-WO-POA6. The method of claim 1 , wherein the determining the resistance is based on a machine learning model.

7. The method of claim 6, wherein the machine learning model includes a match model that associates a resistance of each plurality of sensor with different measurement combinations.

8. The method of claim 1 , wherein one or more of the plurality of sensors comprises a resistive temperature device.

9. A system for measuring a thermal performance of a thermal component, comprising: a sensing unit comprising at least two lead wires coupled to a grid of coupled sensors; one or more processors; and a computer-readable storage medium storing instructions which, when executed by the one or more processors, cause the one or more processors to: receive sensor data from the at least two lead wires coupled to the grid of coupled sensors; determine a resistance associated with each of the sensors based on the sensor data from each of the at least two lead wires; determine a temperature at each of the sensors based on the respective resistance; and generate temperature data of the thermal component based on the temperature at each of the sensors.Attorney Docket No.: 0100S-000393-WO-POA10. The system of claim 9, wherein the one or more processors determines the resistance associated with each of the sensors by determining a plurality of measured resistances between a plurality of at least two lead wires.11 . The system of claim 10, wherein the one or more processors determines the resistance associated with each of the sensors by determining a change in the plurality of measured resistances.

12. The system of claim 11 , wherein the one or more processors determines the resistance associated with each of the sensors by determining a change in the resistance associated with each of the sensors based on the change in the plurality of measured resistances.

13. The system of claim 9, wherein the one or more processors determines the resistance based on a linear model of a change in resistances.

14. The system of claim 9, wherein the one or more processors determines the resistance based on a machine learning model.

15. The system of claim 14, wherein the machine learning model includes a match model that associates a resistance of each sensor with different measurement combinations.

16. The system of claim 9, wherein one or more of the sensors comprises a resistive temperature device.

17. A computer-readable storage device storing instructions which, when executed by one or more processors, cause the one or more processors to: receive sensor data from at least two lead wires coupled to a grid ofAttorney Docket No.: 0100S-000393-WO-POA coupled sensors; determine a resistance associated with each of the sensors based on the sensor data from the at least two lead wires; determine a temperature at each of the sensors based on the respective resistance; and generate temperature data based on the temperature at each of the sensors.

18. The computer-readable storage device of claim 17, wherein the instructions cause the one or more processors to determine the resistance associated with each of the sensors by determining a plurality of measured resistances between a plurality of two lead wires.

19. The computer-readable storage device of claim 18, wherein the instructions cause the one or more processors to determine the resistance associated with each of the sensors by determining a change in the plurality of measured resistances and determine the change in the resistance associated with each of the sensors based on the change in the plurality of measured resistances.

20. The computer-readable storage device of claim 17, wherein the instructions cause the one or more processors to determine the resistance associated with each of the sensors based on a linear model of a change in resistances.

21. The computer-readable storage device of claim 17, wherein the instructions cause the one or more processors to determine the resistance associated with each of the sensors based on a machine learning model.

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