Inductive coil yield testing method based on real-time resistance monitoring

Abstract

The application provides a yield test method for inductance coil based on real-time resistance monitoring, relates to the technical field of automatic measurement of electrical characteristics, and acquires real-time direct-current resistance, probe pressing acceleration peak characteristics and coil structure characteristics bound by production batches by a production line test control device; compares vibration amplitude with a steady-state contact threshold to extract a steady-state direct-current resistance reference, and converts the dynamic quality factor evaluation value by combining the coil structure characteristics; performs aggregative statistical verification based on continuous evaluation values, extracts a translation compensation value to adaptively update a yield qualification interval; and finally, in response to the dynamic evaluation value not satisfying the updated qualification interval, performs physical shunting for abnormal workstations. The application effectively overcomes the limitations caused by probe mechanical rebound and cross-batch material resistance drift, and improves the stability of automatic detection of production lines under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of automated measurement technology of electrical characteristics, specifically to a method for testing the yield of inductor coils based on real-time resistance monitoring. Background Technology

[0002] With the continuous improvement of automation levels in modern intelligent manufacturing, electronic component production lines are developing towards higher speeds and continuous operation. In the quality control system of high-precision components such as inductors, real-time online measurement based on electrical characteristics (such as DC resistance) has become a key data foundation for building an automated assessment architecture for fault prediction and health management. How to accurately extract noise from massive amounts of continuously sampled electrical parameters and implement dynamic reliability judgment in complex and dynamic factory environments is an important direction for the evolution of high-fidelity testing in industrial settings. In continuous production scenarios, existing testing solutions still have the following limitations when facing complex electromechanical couplings and inherent material tolerances:

[0003] The mechanical bouncing and vibration of the probe holder is inevitable when the test probe is pressed down, which causes the test circuit to capture dirty data of falsely high transient contact resistance, thereby triggering false interception;

[0004] Secondly, different batches of raw materials often exhibit basic physical resistance drift within the allowable range of normal processes. Fixed static yield judgment thresholds can easily lead to large-scale erroneous rejections when materials are changed batches, causing frequent line stoppages for parameter adjustments. For example, Chinese patent document CN113740607B provides a resistance testing method and device. This solution is reliable in static testing of the display panel bonding area, but it relies on a multi-step adjustment mechanism based on temperature simulation. When faced with transient electrical changes caused by mechanical vibration in the production line and cross-batch physical drift, it is difficult to achieve dynamic noise reduction and adaptive evolution of the judgment criteria in terms of timing. Summary of the Invention

[0005] The purpose of this invention is to provide a yield testing method for inductor coils based on real-time resistance monitoring. Its core lies in the structured cross-domain fusion of the micromechanical feedback of rigid actuators with the multi-dimensional material process characteristics of the upper layer. By introducing a steady-state gating mechanism and dynamic quality factor conversion logic, it has a composite decision-making capability that adaptively shields high-frequency mechanical jitter and flexibly tracks the evolution of material characteristics, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The inductor coil yield testing method based on real-time resistance monitoring is executed by the production line testing control equipment, and the specific steps include:

[0008] Step S1: Obtain the real-time DC resistance data periodically reported by the current test node, the peak value of the probe pressing acceleration of the probe fixture corresponding to the test node, and the coil structure characteristic parameters bound to the current production batch;

[0009] Step S2: Compare the peak characteristic of the probe's downward acceleration with a preset steady-state contact threshold, and extract the real-time DC resistance data that falls within the steady-state contact threshold as a steady-state DC resistance reference; perform quality factor conversion processing on the steady-state DC resistance reference and the coil structure characteristic parameters to generate a dynamic quality factor evaluation value.

[0010] Based on the multiple dynamic quality factor evaluation values ​​continuously generated within the current production batch, perform data aggregation statistical verification, extract batch yield benchmark shift compensation values, and use the batch yield benchmark shift compensation values ​​to perform shift update operations on the pre-configured yield qualification judgment interval.

[0011] Step S3: In response to the logic determination that the dynamic quality factor evaluation value of the current cycle does not meet the preset qualified threshold condition in the updated yield qualified judgment interval, the test node of the current cycle is marked as an abnormal test station, and a physical isolation control instruction for the production line execution end is generated. The physical isolation control instruction is configured to drive the production line execution end to perform a physical shunt action on the inductor coil at the abnormal test station.

[0012] Preferably, before logically comparing the peak feature of the probe pressure acceleration with a preset steady-state contact threshold, the method further includes: extracting the timing vibration waveform of the test node during the DC resistance sampling period; performing waveform extreme value extraction processing on the timing vibration waveform to extract the maximum pressure transient vibration peak value characterizing the probe clamp, and using the maximum pressure transient vibration peak value as the peak feature of the probe pressure acceleration.

[0013] Preferably, the coil structure feature parameters bound to the current production batch are obtained, specifically including: obtaining the batch identification signaling triggered by the production line material scanning equipment during batch switching;

[0014] Based on the batch identification signaling, a joint search is performed in the preset process specification configuration library to extract the standard wire diameter data and parallel strand number information bound to the batch identification signaling, and the standard wire diameter data and the parallel strand number information are packaged and merged into the coil structure feature parameters.

[0015] Preferably, a quality factor conversion process is performed on the steady-state DC resistance reference and the coil structural characteristic parameters to generate a dynamic quality factor evaluation value, specifically including:

[0016] Based on the standard wire diameter data and the parallel strand number information in the coil structure feature parameters, multi-dimensional feature joint spatial addressing is performed in the preset standard resistance value process comparison table to extract the standard DC resistance theoretical value corresponding to the current production batch composite specification.

[0017] Calculate the differential offset between the steady-state DC resistance reference and the theoretical value of the standard DC resistance; perform deviation proportional amplification calculation based on the differential offset and the preset quality conversion weighting coefficient to generate the dynamic quality factor evaluation value characterizing the current reliability and health status of the inductor coil.

[0018] Preferably, based on multiple dynamic quality factor evaluation values ​​continuously generated within a production batch, data aggregation statistical verification is performed to extract batch yield benchmark shift compensation values. Specifically, this includes: within the initial processing cycle of a production batch, the percentage of contact failure alarms that are intercepted and rejected because they do not fall within the steady-state contact threshold is counted.

[0019] In response to the fact that the proportion of the number of poor contact alarms does not exceed the preset maximum allowable abnormality statistical limit, multiple dynamic quality factor evaluation values ​​that fall within the steady-state contact threshold are extracted to construct an effective evaluation value cache set;

[0020] Calculate the data distribution dispersion and the average numerical offset for the evaluation value sequence within the valid evaluation value cache set; in response to the logical determination that the data distribution dispersion meets the preset high clustering condition, extract the average numerical offset as the batch yield benchmark shift compensation value.

[0021] Preferably, generating physical isolation control instructions for the production line execution end specifically includes: based on the test station equipment code corresponding to the abnormal test station, writing the action delay time parameter for controlling the rejection mechanism and the defective product discharge channel number into the physical isolation control instructions;

[0022] After the physical isolation control command is issued, it is configured to drive the production line execution end to guide the inductor coil at the abnormal test station into the defective recycling bin corresponding to the defective waste discharge channel number when the production line conveyor belt turns at the time corresponding to the action delay time parameter.

[0023] Preferably, based on the peak characteristics of the probe pressing acceleration and the dynamic quality factor evaluation value, the method further includes a device mechanical wear early warning step: synchronously counting the frequency of contact retry triggered due to failure to fall into the steady-state contact threshold within a preset time window, and the total benchmark drift amount of the yield qualification judgment interval being shifted and updated due to the batch yield benchmark shift compensation value;

[0024] In response to a logical determination that the frequency of contact retry shows a monotonically increasing trend and the total baseline drift exceeds a preset equipment wear safety limit threshold, an electromechanical joint composite early warning command is generated. The electromechanical joint composite early warning command is configured to trigger the production line maintenance system to generate a predictive maintenance work order for upstream transmission components.

[0025] Preferably, based on the differential offset and a preset quality conversion weighting coefficient, a deviation proportional amplification calculation is performed to generate the dynamic quality factor evaluation value characterizing the current reliability and health status of the inductor coil, specifically including:

[0026] Acquire ambient temperature fluctuation parameters collected by temperature sensing hardware deployed around the current production line test node;

[0027] The ambient temperature fluctuation parameter is matched with the standard wire diameter data in the coil structure characteristic parameter to extract the dynamic temperature compensation multiplier corresponding to the current test environment.

[0028] The preset quality conversion weight coefficient is replaced by the dynamic temperature compensation multiplier to perform temperature drift calibration amplification calculation on the differential offset, thereby generating the dynamic quality factor evaluation value.

[0029] Preferably, calculating the data distribution dispersion and the average numerical offset of the evaluation value sequence within the valid evaluation value cache set specifically includes:

[0030] Perform resistance interval density statistical aggregation processing on the evaluation value sequence within the valid evaluation value cache set to map the evaluation value sequence to a one-dimensional resistance probability distribution map;

[0031] Extract the dominant frequency clustering peak containing the largest amount of data from the one-dimensional resistance probability distribution spectrum, and extract the frequency distribution width index of the dominant frequency clustering peak as the data distribution dispersion.

[0032] The difference parameter between the statistical center of the main frequency cluster peak and the preset zero deviation benchmark is calculated, and the difference parameter is extracted as the numerical average offset.

[0033] Preferably, after performing resistance interval density statistical aggregation processing on the evaluation value sequence within the valid evaluation value cache set, the method further includes a baseline update interception step based on abnormal distribution states:

[0034] In response to the extraction of at least two discrete independent peaks in the one-dimensional resistance probability distribution spectrum, both of which exceed the preset effective baseline, the logic determines that the current production batch exhibits a heterogeneous resistance bimodal distribution characteristic.

[0035] In response to the determination of the existence of the heterogeneous resistance bimodal distribution feature, an interception action is triggered to block the extraction and translation update operation of the batch yield benchmark translation compensation value, and a mixed batch source locking control signal is generated for the upstream winding execution end. The mixed batch source locking control signal is configured to trigger the production line main control system to urgently interrupt the wiring action of the associated winding equipment and drive the conveyor belt to perform physical isolation of the entire pallet of the current batch.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] This invention constructs a data access defense by extracting the peak characteristics of the probe clamp's downward acceleration and comparing them with a preset steady-state contact threshold. This mechanism effectively intercepts falsely high contact resistance data caused by probe impacts, ensuring that only the real-time DC resistance within the mechanical convergence window is extracted as the steady-state reference. This eliminates false positives caused by mechanical bouncing at the source and enhances the anti-interference robustness of electrical data sampling.

[0038] This invention eliminates impedance differences in the basic material design dimension by performing quality factor conversion between the steady-state DC resistance benchmark and the coil structural characteristic parameters. Furthermore, it utilizes continuously generated dynamic quality factor evaluation values ​​for data aggregation statistical verification, adaptively extracting and performing batch yield benchmark shift compensation. This effectively overcomes the static threshold failure problem caused by normal tolerance drift across batches of raw materials, eliminates the resistance of frequent manual line stoppages for parameter adjustment, and ensures stability in continuous production.

[0039] When the tested entity fails to meet the qualification threshold, this invention binds the equipment code of the test station with the action delay time parameter and the waste discharge channel number. This mechanism successfully maps the transient electrical characteristic imbalance at the front end to the firing sequence of the actuator on the long-distance production line, achieving precise spatiotemporal decoupling and alignment between the test space-time and the split physical space, and eliminating control signaling crosstalk and defective product mixing under multi-channel parallel flow.

[0040] This invention simultaneously counts the frequency of contact retry and the total baseline drift within a time window, constructing a dual-gate control defense line extending from product interception to electromechanical asset management. When mechanical fatigue is detected to be monotonically increasing and electrical drift exceeds the safety limit, a combined electromechanical early warning is triggered. This can automatically generate maintenance work orders for transmission components before a large number of physical components are scrapped, realizing an intelligent upgrade of production line operation and maintenance from passive shutdown to predictive health management. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the physical application environment and technical route of the inductor coil yield testing method based on real-time resistance monitoring of the present invention. Figure 2This is a data curve from a numerical verification experiment conducted by the present invention to investigate the cross-domain multidimensional electromechanical degradation characteristics and the dual-gated composite early warning boundary. Figure 3 This is a schematic diagram illustrating the principle of invalid data interception and feature dimensionality reduction conversion based on electromechanical steady-state feature engagement in this invention. Figure 4 This is a schematic diagram of the scenario-based physical current shunting isolation and PHM closed-loop control based on the one-dimensional resistance probability distribution map update of the present invention; Figure 5 This is a flowchart of the multi-source feature synchronization and steady-state cleaning dimensionality reduction conversion of the present invention; Figure 6 This is a flowchart of the yield determination interval flexible translation and cross-domain electromechanical equipment status monitoring of the present invention; Figure 7 This is a flowchart of the boundary determination and cross-temporal physical isolation closed loop of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various elements, but unless otherwise stated, these elements are not limited by these terms. These terms are used only to distinguish one element from another.

[0044] Example 1:

[0045] Please see Figures 1 to 7 The present invention provides a technical solution:

[0046] A method for testing the yield of inductor coils based on real-time resistance monitoring, executed by a production line test control device, includes the following steps: In this embodiment, the production line test control device is the sole executor of all control commands. Specifically, this production line test control device is an industrial field-programmable logic controller (PLC) or dedicated industrial PC with integrated edge computing capabilities. Internally, it is equipped with a microprocessor for logic deduction and a non-volatile storage medium storing the test control program. This device establishes real-time hard-wired electrical connections downstream to Kelvin probe modules, MEMS accelerometers, and temperature sensing hardware deployed at the test station, as well as to the shunt rejection cylinder valve island at the end of the production line and the upstream winding machine servo control board, via an industrial fieldbus (such as EtherCAT / PROFINET) and high-speed I / O interfaces. The production line test control device is physically connected to each sensing hardware and execution end via a bus to continuously transmit physical sampling features to the upper-level system and output mechanically isolated control signals to the lower-level hardware channels.

[0047] This embodiment implements a benchmark calibration and experience dictionary construction mechanism in an offline environment. Specifically, the calibration logic for the built-in two-dimensional experience lookup table of wire diameter-temperature-compensation multiplier is as follows: In a constant temperature and humidity high and low voltage test chamber, a full temperature gradient scan test covering the temperature sensing range is performed on all standard wire diameter specifications of good enameled copper wire involved in the current process; the resistance drift slope of each wire diameter at different temperature nodes is extracted, normalized and smoothed, and then solidified into the constant compensation multiplier in the above two-dimensional experience lookup table, which serves as the benchmark for online dynamic temperature compensation. At the same time, for the pre-configured yield qualification judgment range, its initial baseline is obtained in the first piece sampling stage of the same batch by performing high-frequency impedance sampling on the first few (first 10) standard good inductors that have passed manual optical inspection, extracting their average ohmic value, and relaxing the absolute tolerance range, which serves as the initial physical anchor point for adaptive evolution.

[0048] Set step S1, specifically execute the initial pre-sensing and multi-source feature acquisition steps: acquire the real-time DC resistance data periodically reported by the current test node, the peak characteristics of the probe pressure acceleration of the probe fixture corresponding to the test node, and the coil structure feature parameters bound to the current production batch.

[0049] In front-end mechanical sensing, the probe holder in this embodiment not only serves as a fixed base for high-frequency electrical probes but is also configured as a rigid mechanical conduction medium in the test physical circuit. To ensure the confidence level of the peak characteristics of the probe's downward acceleration, the probe holder is preferably integrally formed from a low-damping rigid metal material. When the pneumatic module drives the probe to press down and physically impacts the inductor pin, the probe holder can avoid the high-frequency oscillation attenuation caused by flexible assembly. It transmits the transient reaction energy generated at the probe tip as a mechanical wave along its high-rigidity sidewall with high fidelity and low latency, thereby providing a physical measurement benchmark for the micro-mechanical vibration sensing element tightly attached to its sidewall. This ensures the engineering feasibility of the front-end mechanical dirty data gating and cleaning mechanism from the source. In this embodiment, the probe holder also serves as the core carrier of the execution-end physical isolation and reset retry control logic. When the logic center determines that the current real-time DC resistance data has not fallen into the steady-state contact threshold, it sends a lift-up reset command to the bus. In essence, this command drives the servo cylinder connected above the probe fixture to perform spatial displacement, thereby breaking the current abnormal electrical coupling state and re-establishing the press-fit address.

[0050] The system acquires batch identification signaling triggered by the production line material scanning equipment deployed at the test station's inlet during batch pallet switching. Based on this signaling, it performs a joint search in a pre-set process specification configuration library via the workshop industrial bus to extract standard wire diameter data and parallel strand count information bound to the batch identification signaling. This data is then reconstructed into structured multi-dimensional feature vectors and packaged into coil structure feature parameters. During cold start initialization of the test equipment or the cleanup action at the end of a single batch, this parameter configuration section remains in a silent, zero-data state by default, physically blocking all invalid addressing queries without matching batch identification signaling activation. Control flow triggers a search and release only when the industrial bus listens for and parses the batch identification signaling emitted by the production line material scanning equipment, and the reconstructed coil structure feature parameters are transitioned to an active state and persistently reside in the non-volatile storage medium of the main controller.

[0051] The characteristic parameters of the coil structure are denoted as It represents the inherent physical design attributes and conductor cross-section specifications of the inductor coils in the current production batch. In this embodiment, it is manifested as follows: when the target product is selected on-site, this parameter group is issued as a fixed constraint constant [e.g., standard wire diameter 0.8mm, single strand], thereby providing a precise design parameter carrier for subsequent impedance theory verification.

[0052] A high-precision Kelvin probe assembly deployed at the current test node is used to acquire periodically reported real-time DC resistance data. This real-time DC resistance data is then recorded as... It is a physical quantity that characterizes the transient ohmic resistance of the inductor under test during the probe pressing detection cycle. In this preferred embodiment, its numerical boundary is set to 1mΩ to 500mΩ. This range is set to fully cover the physical impedance distribution range of the power inductors on the production line and the sudden high resistance state that is prone to occur due to poor soldering.

[0053] Before logically comparing the peak characteristics of the probe's downward acceleration with a preset steady-state contact threshold, the timing vibration waveform of the test node during the DC resistance sampling period is extracted. In a specific engineering implementation, this is a miniature mechanical vibration sensing element tightly attached to the side wall of the probe holder, specifically a MEMS accelerometer. The control center extracts the mechanical feedback sequence of the probe's downward pressure in real time through the MEMS accelerometer rigidly connected to the probe's pneumatic module.

[0054] Waveform extremum extraction processing is performed on the time-series vibration waveform. Specifically, a sliding time window statistical algorithm is configured through the scheduling queue of the main control center. In this embodiment, the time span of the evaluation window is limited to a mechanical convergence period of 20 to 50 milliseconds, which is used to perform continuous slicing of the time-series waveform stream. In the data processing stream, the time mean of the unloaded background mechanical waveform when the probe is not pressed within 100 milliseconds before the current sliding slice is extracted to calculate the static baseline characterizing the constant noise floor of the conveyor belt. Then, the static baseline is algebraically subtracted from each discrete sampling point of the current slice sequence to purify the net dynamic impulse fluctuation flow caused only by the physical impact of the pneumatic probe. In response to the input of the net dynamic impulse fluctuation flow, the waveform high point that produces the maximum discrete variation absolute value is located and locked, and then the maximum downward pressure transient vibration peak value characterizing the recoil intensity of the probe clamp is extracted. The maximum downward pressure transient vibration peak value is used as the probe downward acceleration peak feature for subsequent gating judgment.

[0055] The peak value of the maximum downward transient vibration is denoted as It characterizes the extreme value of the mechanical rebound intensity generated when a rigid test probe impacts the copper pin of an inductor. In this preferred embodiment, its range is set to 1.5G to 4G, where G is the standard gravitational acceleration. This range is set to prevent abnormal physical impacts caused by overload due to mechanical buffer failure caused by metal fatigue of the probe spring.

[0056] Step S2 involves executing the electrical characteristic cleaning and dimensional calibration steps: specifically, obtaining the basic micro-vibration extreme values ​​of the probe fixture under no-load conditions without test material; and simultaneously reading the pre-configured electromechanical tolerance amplification factor. The initial calibration mechanism for this electromechanical tolerance amplification factor is as follows: extracting multiple sets of pneumatic probe assemblies with different aging degrees and performing high-frequency pressure fatigue tests; based on the statistical upper limit of the critical recoil potential energy that does not induce electrical disconnection from the aforementioned historical test sample pool, and introducing an engineering safety margin, the electromechanical tolerance amplification factor is derived through mapping. The above initial calibration characteristics are used to derive the steady-state contact threshold using the following proportional mapping formula: ;in, The steady-state contact threshold is the upper limit of physical and mechanical residual tolerance that allows the probe to establish an effective electrical connection with the coil pin; in this embodiment, it is preferably set to 0.5G. The extreme value of the microseismic event is used to characterize the low-frequency background vibration amplitude transmitted by the factory foundation and the conveyor belt motor. In this embodiment, the preferred value is 0.15G. The electromechanical tolerance amplification factor, as a safety engineering factor for the pneumatic probe buffer assembly to contain mechanical recoil, is based on the above offline fatigue test data distribution, and its preferred range converges within a spatial mapping span of 3 to 3.5 times.

[0057] Further, it performs cleaning based on electrical features and mapping to the spatiotemporal coordinate system; specifically, it continuously performs logical gating comparison between the obtained peak features of probe downward acceleration and the preset steady-state contact threshold.

[0058] In response to the logic determination that the peak value of the probe's downward acceleration currently being tracked in real time has failed to fall within the safe boundary of the steady-state contact threshold within the preset physical stability time limit, the gating trigger immediately blocks the current electrical data sampling process and sends a reset retry command to the pneumatic module, driving the test end cylinder to lift the probe and re-execute the downward alignment action. At the same time, the frequency of contact retry is incremented in the operation log.

[0059] In response to the logical determination that the peak value of the probe's downward acceleration, currently being tracked in real time, exhibits a continuous monotonically decreasing trend and falls within the safe boundary of the steady-state contact threshold, indicating that the probe's physical bouncing oscillation has converged to the crimping blind zone, the gating trigger immediately transitions to the release state and directly triggers the effective time-domain alignment mechanism. Specifically, the time-domain alignment mechanism involves calling the data synchronization unit instruction to perform absolute timestamp matching on the cyclically rolling input real-time DC resistance data queue, using the start and end time nodes of the gating release state as anchor points. This mechanism logically extracts and isolates an electrical data slice sequence that is completely consistent with the start and end coordinates of the mechanical steady-state time-domain window. The multiple real-time DC resistance data consecutively contained within this pure slice sequence are then subjected to mean aggregation and summation mapping to remove the disordered high-frequency dynamic oscillation environment, thereby solidifying and outputting a unique steady-state DC resistance reference that characterizes the crimping impedance of the tested inductor.

[0060] This embodiment addresses the issue that abnormal characteristics of high-power or miniature inductors are masked by absolute values ​​due to differences in their fundamental specifications. It performs a quality factor conversion process on the previously extracted steady-state DC resistance reference based on coil structural characteristic parameters to eliminate the physical design impedance base of the product, thereby generating a dynamic quality factor evaluation value. The specific logic of this process is as follows: using the currently active coil structural characteristic parameter (e.g., [standard wire diameter 0.8mm, single strand]) as the unique data addressing key, it enters a preset standard resistance value process comparison table. Based on the current characteristic parameter, it performs multi-dimensional feature mapping association and spatial comparison addressing; upon successful matching, it extracts the nominal impedance absolute value bound to the current production batch under ideal process and standard room temperature conditions, as the theoretical value of the standard DC resistance.

[0061] The theoretical value of standard DC resistance is denoted as It represents the absolute value of the nominal impedance of the current batch of coils under ideal process and standard room temperature. The control center pre-allocates and maintains a static physical feature association mapping structure containing multiple discrete wire diameter specifications, strand number combinations and nominal impedance mapping relationships, which is a standard resistance value process reference table. When the currently effective coil structure feature parameter is extracted, the data scheduling unit uses the standard wire diameter size data and parallel strand number information in the feature parameter as the combined feature primary key, and performs multi-dimensional feature joint spatial addressing and physical mapping feature extraction in the reference table; after successful addressing, it extracts the comprehensive theoretical ohm value corresponding to the basic impedance of a single wire diameter and the parallel current shunting effect of multiple strands (for example, when the primary key is a 0.8mm wire diameter and two strands are twisted in parallel, it adaptively extracts the equivalent value of 7.5mΩ due to parallel current shunting), and pushes it into the resident data queue, which serves as the absolute static physical reference frame for subsequent calculation of the system's relative deviation.

[0062] The microprocessor extracts a steady-state DC resistance reference containing dynamic variation characteristics as the minuend, and simultaneously extracts the theoretical value of the standard DC resistance as the subtrahend. It then performs a direct algebraic subtraction mapping on both under the same ohmic dimensions, outputting a differential offset that uniquely characterizes the ohmic difference between the two and its evolving drift direction, denoted as . It represents the pure band-direction ohmic deviation vector of the tested coil caused solely by the current physical tension tolerance of the processing (such as the positive high resistance deviation caused by the stretching and thinning of the copper wire) or potential defects (such as the negative low resistance deviation caused by local varnish film damage and short circuit).

[0063] This embodiment further implements a heterogeneous parameter dimension alignment mechanism to eliminate the nonlinear resistance expansion interference caused by the non-constant temperature environment of the workshop on the ultra-fine enameled copper wire. It acquires the ambient temperature fluctuation parameters collected by temperature sensing hardware (such as PT100 thermistors) deployed around the current production line test node. Specifically, an industrial communication gateway normalizes the temperature sensing hardware (such as thermistors) deployed around the test bench and physically exposed to the workshop atmosphere into standard data acquisition units. Based on a preset low-frequency time interval (such as every 2 seconds), the gateway continuously reads the analog level fluctuation signal output by the sensing hardware and linearly translates and formats it into a structured absolute Celsius floating-point sequence stream through its built-in analog-to-digital converter (ADC). The ambient temperature fluctuation parameter is denoted as... It characterizes the real-time temperature drift characteristics induced by the heating cycle of electrically driven heavy equipment or alternating airflow changes within the physical space of the production line test. In this embodiment, the ambient temperature fluctuation parameter is set to a sensing interception limit of -15℃ to +35℃.

[0064] To further address this temperature boundary, an asymmetric circuit breaker mechanism is implemented: if the transient ambient temperature reported by the temperature sensing hardware deployed at the test node exceeds the sensing interception limit of -15℃ to +35℃, the logic determines that the current thermodynamic compensation algorithm has entered an unreliable range; the normalized automatic flow test of the current batch of products is immediately interrupted, and an environmental temperature control failure lockout command is triggered to the production line alarm center. This command suspends the downstream execution cylinder and illuminates the fault indicator light to call for manual intervention, fundamentally preventing erroneous interception and misjudgment of waste materials caused by distortion.

[0065] Furthermore, the PT100 thermally sensitive devices deployed at the front end of the test nodes continuously report real-time environmental thermodynamic characteristics to the main control center. The physical metal microlattice corresponding to the wire diameter specification will naturally expand and increase impedance when encountering a thermodynamic rise. The scheduling queue directly reads the external level analog signal through the gateway and performs two-dimensional empirical lookup table spatial addressing. It uses the extracted dimensionless scaling factor to perform algebraic cancellation on the absolute value of the differential ohms of the tested physical coil. The physical suppression process is mapped by the following matrix parameters.

[0066] Table 1: Heterogeneous parameter alignment and dynamic dimensionality reduction mapping data under thermodynamic fluctuations in the workshop environment

[0067]

[0068] As shown in Table 1, when the controlled production line is in a standard thermodynamic steady state at 20℃, the real-time resistance acquired by the probe module deviates slightly from the ideal nominal impedance. This difference of +0.05mΩ is extracted in ohms and directly mapped to a relative deviation scale of 0.0066 through a reference constant of 1. As the workshop temperature gradually increases to 25℃ and 28℃, the thermal expansion effect of the copper conductor causes the resistance data acquired by the physical sampling end to continuously rise. The control logic extracts dynamic temperature compensation multipliers of 0.90 and 0.85 from a table, suppressing the artificially high differential offset vector before the division operator is executed, thus keeping the final output dynamic quality factor evaluation value within a safe low range and avoiding triggering false positives. When the thermal hardware detects that the local environment momentarily exceeds the 35℃ safety threshold (set to 36℃) due to a ventilation failure, the state machine determines that the current acquisition space has deeply deviated from the linear envelope range of the static empirical dictionary. The control center directly refuses to output the dynamic quality factor assessment value, and at the same time sends an environmental temperature control failure lockout command upstream, relying on the hard isolation mechanism to eliminate the risk of batch misjudgment caused by the extrapolation distortion of the dynamic temperature compensation multiplier.

[0069] Furthermore, the ambient temperature fluctuation parameter is matched with the standard wire diameter data in the coil structure characteristic parameters using a temperature resistance coefficient matching process. The specific logic of this matching process is as follows: the microprocessor retrieves a two-dimensional empirical lookup table of wire diameter-temperature-compensation multipliers pre-programmed into a non-volatile storage medium. The formatted ambient temperature fluctuation parameter is converted into a row-oriented addressing index, and the current batch of standard wire diameter data is loaded as a column-oriented addressing index. The microprocessor calls the two-dimensional empirical lookup table of wire diameter-temperature-compensation multipliers and performs cross-space addressing within the grid space based on the constructed bidirectional index. If the real-time temperature input variable does not fall on a pre-calibrated grid reference node, the interpolation algorithm module will automatically activate, extracting the scalars of adjacent discrete grid elements that tightly enclose the temperature point. Based on the absolute difference ratio weight between adjacent temperature reference points, a first-order linear interpolation derivation is performed, ultimately converging and outputting a unique and smooth dimensionless scalar. This dimensionless scalar is then extracted as the dynamic temperature compensation multiplier corresponding to the current test environment, denoted as... As a preconditioning parameter for state compensation, it represents a dimensionless scaling factor at the algebraic level specifically used to proportionally offset the artificial high impedance caused by the natural physical thermal expansion of copper. This preferred embodiment assigns a cutoff protection boundary to copper based on its properties, and its range is constrained to the range of 0.85 to 1.15.

[0070] The control center has a preset quality conversion weighting coefficient. This preset weighting coefficient is a constant mapping gain constant used for linearly amplifying differential offsets in a temperature-controlled workshop or when external thermodynamic feature sensing is not triggered. The initial reference scalar value of this coefficient is preferably configured to be 1. When the environmental temperature drift compensation route is activated, the control logic replaces the preset quality conversion weighting coefficient with a dynamic temperature compensation multiplier to perform temperature drift calibration amplification calculations on differential offsets in the Ohmic dimension. Its internal logic manifests as reference normalization and feature calibration mapping. The specific action decomposition is as follows:

[0071] The microprocessor invokes the data processing module to perform physical temperature drift calibration and relative deviation ratio conversion on the differential offsets in the Ohmic dimension based on the following structured mapping rules: The parameter constraints in the above rules are as follows: The dynamic quality factor evaluation value characterizing the dimensionality reduction output is preferably in the range of 0 to 1 in this embodiment, and is used to provide a homogeneous calculation target for the subsequent construction of a dimensionless one-dimensional resistance probability distribution map. Characterized by the differential offset at the molecule end, it has an absolute value of the deviation in absolute ohmic physical units. Characterizes the theoretical value of the standard DC resistance at the denominator. The currently active dynamic temperature compensation multiplier represents a dimensionless scaling factor specifically used to offset the natural physical thermal expansion of copper in an equal proportion. Its value range is constrained to 0.85 to 1.15, with a preferred initial value of 1 in the default blocking state. If the multiplier value exceeds the upper limit of the range, the resistance data will be over-compressed, masking the true coil tension defects. If its value is below the lower limit of the range, it cannot effectively eliminate the spurious increase in natural expansion induced by workshop heating, resulting in severe distortion of the dynamic reference. The specific steps of this mapping algorithm are: extracting the differential offset with absolute ohmic physical units and performing a real-number multiplication amplification operation with the currently active dynamic temperature compensation multiplier. By introducing this mapping rule and offsetting factor, the natural expansion deviation induced by workshop heating is directly and smoothly eliminated in the pure physical ohmic dimension. Subsequently, algebraic division is performed using the theoretical value of standard DC resistance with the same ohmic dimension to remove the feature masking effect caused by the significant difference in the base resistance of the materials, ultimately solidifying and outputting a purely numerical dynamic quality factor evaluation value.

[0072] Step S2 also includes performing a flexible baseline evolution step based on abnormal distribution state to prevent contamination: performing data aggregation statistical verification based on multiple dynamic quality factor evaluation values ​​continuously generated within the production batch, and extracting batch yield baseline shift compensation values. In this embodiment, to prevent continuous abnormal evaluation features from deeply contaminating the subsequent statistical sample pool, a feature admission pre-interception mechanism is constructed: specifically, before performing the flexible baseline evolution, the basic pressure connection confidence of the pneumatic actuator module is established. A fixed monitoring time slice is initiated within the initial processing cycle of the production batch, extracting the total number of downward pressure detection commands issued for the current test node, and simultaneously counting the frequency of contact failure alarms that are blocked and rejected because they do not fall within the steady-state contact threshold, thereby calculating the proportion of contact failure alarms. The specific logic is: activating a dual-path parallel statistical module within this monitoring time slice. The first monitoring link continuously accumulates the total number of down-pressure detection commands intercepted from the main control gateway to the probe module at the denominator end of the data stream. The second monitoring link simultaneously intercepts the frequency of contact failure alarms caused by physical mechanical bounces failing to fall within the steady-state contact threshold, resulting in gating blockage and the emission of independent abnormal pulses. After the time slice expires and closes, the data processing module extracts the above dual data sources, performs a ratio mapping operation by dividing the contact failure alarm frequency by the total number of down-pressure detection commands, and outputs the percentage of contact failure alarms that characterizes the current hardware contact health.

[0073] The maximum allowable upper limit for abnormal statistics is denoted as This represents the lower limit of the basic effective crimping capability of the tested pneumatic actuator module. In this preferred embodiment, it is expressed as a percentage of 3%. This setting prevents the risk of data evolution failure due to the continuous intake of invalid high-resistance data caused by the loosening of clamp fastening screws, which could deeply contaminate the subsequent statistical sample pool.

[0074] In response to the confirmation that the proportion of alarms related to poor contact does not exceed the maximum allowable anomaly statistical limit, the data write block on the buffer is lifted. The data scheduling module sequentially extracts all dynamic quality factor evaluation values ​​that have been approved by steady-state gating and have undergone complete dimensionality reduction transformation, and stores them sequentially in the configured first-in-first-out (FIFO) data queue to load and construct a valid evaluation value cache set. As a temporary storage container for providing continuous and homogeneous data streams to the backend probabilistic statistical algorithm, this valid evaluation value cache set is configured with boundary reset and lifecycle attributes: whenever the bus throws a batch switching control signal, or the queue overflows and triggers an interrupt, the data scheduling module performs a zeroing and overwriting operation on the historical residual data column in the valid evaluation value cache set, physically preventing heterogeneous feature stream contamination across batches or work orders.

[0075] The data distribution dispersion and average numerical offset are calculated for the evaluation value sequence within the valid evaluation value cache set. Specifically, dynamic space management of the data flow is implemented: based on a preset sliding time window distribution statistical algorithm, a first-in-first-out (FIFO) cache queue with a fixed step period and capacity constraints is configured. During the time-series evolution, whenever a new period's evaluation value is detected entering the front of the window queue, a pop-out action is synchronously triggered for the oldest historical evaluation value at the tail of the queue to maintain dynamic convergence of the overall window data. Based on the preset sliding time window distribution statistical algorithm, resistance interval density statistical aggregation processing is performed on the evaluation value sequence within the valid evaluation value cache set to project and map it onto a one-dimensional resistance probability distribution map in the feature space. The specific execution logic of this statistical aggregation includes an address space initialization step: allocating an address set in the storage space consisting of multiple discrete resistance statistical containers arranged in a linear, continuous, fixed-step span.

[0076] The main data mapping loop is activated, sequentially extracting the evaluation value sequence parameters residing in the valid evaluation value cache set. For each extracted feature parameter, the statistical algorithm module executes a spatial routing addressing mechanism based on interval boundary constraints. Specifically, it directly extracts the currently extracted feature parameter and calls the microprocessor to perform discrete dimensionality reduction mapping based on the absolute span. The unique corresponding target discrete resistance statistical container index address is calculated using the following mapping operator:

[0077] ;

[0078] The Index represents the physical index address of the one-dimensional array of the target discrete resistance statistical container after dimensionality reduction, which is represented as a non-negative integer address offset mapped within the storage space. It represents the dynamic quality factor assessment value currently extracted from the first-in-first-out cache queue. The lower bound absolute reference point characterizes the pre-configured one-dimensional feature distribution space. In this embodiment, the reference value is preferably 0. The width of the continuous data span covered by the statistical container for a single discrete resistance value is characterized. In this embodiment, its preferred value is 0.01. In actual deployment, it is configured within the range of [0.005, 0.05] based on the main controller capacity and the required impedance determination accuracy. (Symbol) The logical operation representing rounding down.

[0079] Furthermore, if the calculated index exceeds the pre-allocated one-dimensional array boundary, the control logic immediately triggers boundary interception, routing the feature parameter to the edge exception containers at both ends to ensure no data sample is lost. If the calculated index falls within a valid system-allocated address range, it is directly used as the physical pointer for payload routing, assigning the current feature parameter to the target discrete resistance statistical container uniquely corresponding to that index, and directly performing an increment-by-one accumulation mapping operation on the statistical frequency variable inside the target statistical container. After all sequence elements within the set time-domain slice have been distributed, the structured one-dimensional container array containing different discrete frequency heights is directly projected and reconstructed into a one-dimensional resistance probability distribution map characterizing the spatial density aggregation state of the entire batch.

[0080] The one-dimensional resistance probability distribution spectrum is denoted as As a structured data graph model reflecting the uniformity of the intrinsic quality of the current batch of inductor products, it characterizes the physical background state of the product after continuous sample accumulation. This one-dimensional polymorphic feature set is delivered in a one-way closed loop from the front-end cache queue to the back-end abnormal peak identification module. After the graph is generated, a baseline update interception fallback action based on the abnormal distribution state is performed. In this embodiment, in the process of checking the peak shape of the probability distribution and performing abnormal mixed batch fallback interception, the control unit calls the one-dimensional discrete array scanning logic. Along the impedance growth coordinate axis of the one-dimensional resistance probability distribution graph, adjacent resistance statistical containers are extracted sequentially and their frequency change gradients are calculated; when the state machine detects a feature node where the change gradient instantly flips from positive growth to negative decay, it immediately locks the container coordinates corresponding to the node in the physical storage area and marks it as a candidate peak vertex. The absolute cumulative frequency of the candidate peaks is compared algebraically to verify whether it exceeds the preset effective baseline. In this embodiment, in order to verify the physical independence between multiple peaks, the microprocessor searches laterally between any two adjacent qualified candidate peaks for the valley shape represented by the local frequency minimum and logically determines whether the local frequency minimum satisfies the preset independent cutoff depth condition.

[0081] In response to the extraction of at least two discrete independent peaks in the one-dimensional resistance probability distribution spectrum, both with amplitudes exceeding a preset effective baseline (at least two candidate peak vertices satisfying all the above multidimensional constraint indices exist in the current feature space), the logic determines that the current production batch exhibits a heterogeneous bimodal resistance distribution characteristic. The independent cutoff depth condition is used to characterize the degree of physical isolation between the two probability density clusters; in this embodiment, this condition is preferably configured as follows: the absolute value of the frequency of the trough minimum is less than 50% of the amplitude of the lower of the two adjacent candidate peak vertices; when facing mixed batches of enameled wire with different viscosities, this proportional constant is dynamically reset within the range of [30%, 70%].

[0082] In this embodiment, the data processing module extracts the total data capacity carried within the current valid evaluation value cache set as the base order of magnitude and performs algebraic multiplication with a pre-configured dynamic judgment ratio coefficient (e.g., set to 12% to 16%). The final product generated is established as the preset valid baseline. This ensures that any identified abnormal peaks are supported by sufficient physical sample density, thereby using the absolute order of magnitude of the sample size to filter and shield sporadic isolated noise induced by transient electromagnetic pulses in the workshop or individual probe jitter, ensuring a high confidence level for the entire line shutdown intervention.

[0083] Preset effective baseline (denoted as) This parameter, serving as the threshold of the underlying threshold filter in the bimodal discrimination architecture, essentially characterizes and confirms that the current data density bulge deviating from the main distribution axis is a substantial production line accident induced by the mixing of large batches of heterogeneous materials of different specifications. The baseline value is limited to a value dynamically calculated by extracting the total data capacity carried within the effective evaluation value cache set and multiplying it by a preset proportional coefficient of 12% to 16%. In actual operation verification, if the value of this proportional coefficient is lower than the set lower threshold, it will lead to the degradation of the distribution gating, causing the system to be overly sensitive to occasional local high-resistance contacts or slight environmental electromagnetic pseudo-fluctuations, thereby frequently triggering false bimodal locking and erroneous line stop actions; if the value of this proportional coefficient exceeds the upper limit of the range, it will cause the abnormal distribution discrimination defense line to become sluggish, easily losing the ability to capture the substantial bimodal characteristics caused by the mixing of small batches of heterogeneous materials, resulting in the failure to intercept mixed batches of products flowing into the downstream packaging process.

[0084] When the above-mentioned bimodal state is present, it statistically indicates with high confidence that an abnormal batch of material mixed together has occurred on-site due to the mixing of materials of different specifications or human rewinding. When this condition is met, an interception action is triggered: the extraction and translation update operation of the batch yield benchmark translation compensation value is blocked, and a mixed batch source locking control signal is generated for the upstream winding execution end; the mixed batch source locking control signal is configured to trigger the production line main control system to cut off the enable circuit of the associated winding machine servo motor to urgently interrupt the winding action of the associated winding equipment, and at the same time drive the push-pull cylinder on the conveyor belt to perform physical isolation of the entire batch carrying tray.

[0085] If the preceding bimodal heterogeneous state blocking is not triggered, the microprocessor continues to perform peak extremum search in the one-dimensional resistance probability distribution spectrum to lock the main frequency clustering peak covering the largest absolute cumulative frequency. Starting from the extremely high frequency peak of this main peak, the control logic probe synchronously performs a downward traversal operation along the descending envelope edges on both sides of the waveform; when the scanning probe detects that the data frequency reaches and falls below the preset attenuation ratio (such as using the full width at half maximum (FWHM) rule, falling to 50% of the amplitude of the extremely high peak), it immediately locks the positions at both ends to obtain the coordinates of the left and right feature cutoff points; further, it calls the algebraic subtraction action to calculate the range of the statistical container spanned by the coordinates of these two feature cutoff points. This algebraic range is directly mapped to the frequency distribution width index of the main frequency clustering peak, and this frequency distribution width index is extracted as the data distribution dispersion.

[0086] Frequency distribution width is marked as This characterizes the discrete width boundary that diverges outward from the overall physical impedance of mainstream homogeneous coil products currently within the compliance tolerance range. This embodiment defines a bandwidth tolerance threshold boundary: its hash span is limited by a judgment rule to reject crossing 4 to 7 consecutive discrete statistical container physical distances. By introducing this span tolerance boundary based on the integer architecture level, an interception defense is directly established on the distributed addressing span of consecutive memory address blocks, avoiding the failure of boundary tolerance judgment caused by the misalignment of floating-point statistical dimensions.

[0087] After extracting the statistical center values ​​of the main frequency cluster peaks, a preset zero-deviation benchmark represented by a set of hard-coded constants is pre-loaded into the read-only data segment, denoted as... This constant characterizes, within the entire control closed loop, the absolute coordinate reference origin position that the tested inductor product should fall into after normalization calculation under ideal conditions such as theoretically optimal process, absolutely tension-free deformation and elongation, and a constant standard laboratory environment. The preset zero-deviation reference is constantly anchored at a node with an absolute scalar value of 1 or a relative offset of 0%. Based on the statistical center value of the extracted dominant frequency cluster peak, the algebraic difference between it and the preset zero-deviation reference is calculated, and this difference parameter is denoted as... It represents the algebraic drift vector magnitude of the overall process statistical background color of the currently circulating homogeneous inductor coil batch, which is far from the absolute perfect state (for example, the calculation shows that the current batch distribution background color presents a positive offset scalar of +1.2%).

[0088] The difference parameter is then extracted as a numerical average offset. In response to the logical determination that the data distribution dispersion meets the preset high clustering condition, specifically when it is confirmed that the frequency span scalar represented by the data distribution dispersion is less than the aforementioned broadband tolerance threshold boundary of 4 to 7 consecutive discrete statistical container physical intervals, the extracted numerical average offset is extracted as a batch yield benchmark translation compensation value. The batch yield benchmark translation compensation value is then used to perform a translation update operation on the pre-configured yield qualification judgment interval in the control center, specifically by performing algebraic offset compensation on the upper and lower boundaries of the judgment domain.

[0089] Step S3 is set up to execute the anomaly detection and physical isolation diversion control steps based on spatiotemporal absolute alignment: After completing the flexible adaptive evolution of the preceding test benchmark, the dynamic quality factor evaluation value of the current cycle is extracted and compared with the updated yield pass / fail judgment interval boundary value. The preset pass / fail threshold condition is denoted as... This characterizes the minimum reliability threshold that allows the physical health condition of the tested inductor coil (such as microlattice integrity and inter-turn insulation level) to flow into the downstream finished product packaging process. In this preferred embodiment, its boundary is set to deviate from the dynamic reference center value by ±3.5% to ±5%. This boundary is set to prevent defective devices with potential for poor soldering due to relaxed tolerance from flowing into the downstream application environment and causing electrical function failure. In response to the logic determining that the dynamic quality factor evaluation value of the current cycle does not meet the preset qualified threshold condition in the updated yield qualified judgment interval, the current product is confirmed as a physical defective product. The test node of the current cycle is marked as an abnormal test station in the control logic status table, and a physical isolation control instruction for the production line execution end is generated.

[0090] In this embodiment, to address the issue of a significant distance difference between the front-end high-frequency electrical test probe and the rear-end mechanical rejection cylinder in the physical space of the production line, a dynamic delay compensation mechanism is implemented. Based on the equipment code of the test station corresponding to the abnormal test station, the action delay time parameter for precisely controlling the firing timing of the rejection mechanism and the defective product discharge channel number are written into the data protocol frame of the physical isolation control command. After the physical isolation control command is issued, it is configured to drive the countdown timer unit inside the production line execution end to operate; in response to the countdown reaching zero and triggering the production line conveyor belt flow time corresponding to the action delay time parameter, the inductor coil at the abnormal test station is guided to detach from the main conveyor track and enter the defective product recycling box corresponding to the defective product discharge channel number, thereby completing the closed-loop isolation of the physical defective product.

[0091] The equipment code for the test station is recorded as follows: As a bound addressing constant, it represents the unique underlying MAC or IP addressing identity assigned to the specific electrical test bench that triggers the defect determination in the workshop fieldbus network (such as EtherCAT industrial LAN). During the control system initialization phase, this code is statically registered in the mapping routing table of the main control gateway and is in a persistent mounted state. When the test communication link captures a defect anomaly, this multi-dimensional topological feature is extracted and used to accurately locate and uniquely bind the corresponding physical rejection cylinder and action valve island on the corresponding downstream transfer track on a long-distance production line, across time and space physical intervals, to prevent crosstalk and mistransmission of isolation signaling under multi-channel parallel testing conditions.

[0092] The control unit extracts the absolute physical space span constant pre-stored in the parameter configuration area, and simultaneously extracts the encoder pulse data deployed at the servo motor end through the industrial bus, and converts it into the real-time kinematic linear velocity parameter of the current conveyor belt. The data processing module in this embodiment derives the delayed triggering timing based on the following spatiotemporal mapping logic calculation: ;

[0093] in The action delay time parameter characterizes the calculated output, which means the absolute spatiotemporal span required for the abnormal tested inductor coil to physically travel with the conveyor belt to the diversion execution point; in this preferred example, it converges within a dynamic range of 800 milliseconds to 2500 milliseconds. The constant representing the absolute physical space span is specifically manifested as the fixed mechanical straight-line distance from the probe pressing center point to the central axis of the end physical diversion baffle, serving as a static spatial scale benchmark. The real-time kinematic linear velocity parameter is characterized and used as the denominator for real-time kinematic dimensionality reduction. This embodiment addresses the conveyor belt acceleration / deceleration or mechanical jamming caused by transient load fluctuations in the main motor of the production line by further implementing a dynamic spatiotemporal integral compensation fault-tolerance mechanism in the shunt countdown service. Specifically, after the front-end pneumatic probe is raised and the initial countdown process is activated, the main control equipment's data processing module extracts real-time encoder pulses from the servo motor side at high frequency according to time slices and executes closed-loop residual timing dynamic calibration logic. ;in The dynamic residual delay triggering parameters are calculated in real time and continuously overwritten within the control cycle. It represents the instantaneous linear velocity within the historical discrete sampling period t, which is decoded from the encoder pulse. The discrete sampling time step characterizes the state data intercepted by the microprocessor. In this embodiment, it is preferably 10 milliseconds. To match industrial buses with different baud rates, it is configured within the time domain boundary of [5 milliseconds, 20 milliseconds]. The latest discrete time node representing the execution of the current closed-loop dynamic calibration logic serves as the upper limit of the time-domain integral defining the accumulated spatial displacement of the physical conveyor belt. Characterizes the latest instantaneous linear velocity features entering the current evaluation node. This represents the cumulative physical displacement.

[0094] Furthermore, during continuous physical processes involving a large number of inductor coils in the test equipment, the servo drive system is prone to slight drops in belt speed due to upstream feed stacking or transient mechanical jamming of the transmission bearings. An absolute encoder deployed at the motor end intercepts this mechanical jitter at high frequency and transmits real-time linear velocity slice data to the main control computing unit via the EtherCAT industrial bus. To overcome the spatial firing misalignment caused by static clock open-loop control under such physical stall conditions, high-frequency residual physical displacement alignment logic is activated. The specific parameter evolution is recorded in the following chart.

[0095] Table 2: Dynamic Spatiotemporal Integral Approximation Fault-Tolerant Control Data Based on Servo Encoder Feedback

[0096]

[0097] As shown in Table 2, the discrete sampling time step The constant configuration is 10 milliseconds. At the initial stripping point of t=0, the probe just detaches from the measured inductor. At this moment, the belt is fully loaded with its rated linear velocity output. Based on the current steady-state flow rate of 500 mm / s, the remaining delay firing sequence calculated backwards is 1990 milliseconds. When the system clock advances to cycles t=1 and t=2, the control hardware detects transient mechanical stagnation in the support tray via the bus, causing the kinematic linear velocity to drop sharply to 400 mm / s. Within this deceleration range, the microprocessor extracts the sum of historical discrete displacements in real time and removes it from the constant physical space boundary. Then, it re-executes the division and dimensionality reduction mapping using the latest instantaneous velocity after the drop. The control center overwrites the issued action delay command, which is instantly extended to 2477.5 milliseconds. With the stagnation eliminated (t=3), the drive speed returns to the baseline, and the countdown period is adaptively shortened again based on the remaining absolute spatial difference. Utilizing the aforementioned high-frequency dynamic spatial residual approximation mechanism, the firing sequence of the pneumatic rejection valve island is locked on the physical displacement travel axis, intercepting the abnormal flow of waste recycling bins into good products caused by the deceleration of the production line.

[0098] In this embodiment, the defective product waste disposal channel is numbered as follows: As a discrete enumeration control identifier, it represents the specific recycling track entity address statically allocated at the hardware level based on the specific physical defect type (such as a high-resistance waste bin corresponding to microscopic strand breakage, and a low-resistance waste bin corresponding to inter-turn short circuit). The specific value assigned to this enumeration identifier is [such as a discrete PLC address code like 0x01 or 0x02]. This setting is intended to guide the on-site pneumatic valve island to perform targeted physical distribution, preventing the source-tracing blockage anomaly caused by mixing waste of all electrical failure modes in one place, leading to confusion in later yield review and analysis.

[0099] Further, a physical isolation control command containing spatiotemporal coordinate constraints is issued to the production line execution end. The control logic of this command is as follows: by issuing this control signal frame, the countdown unit inside the production line execution end is activated; this drives the production line execution end to respond to the system's diversion countdown service, precisely counting down to zero based on static action delay time parameters or dynamically overwritten residual delay trigger parameters, thereby reaching the corresponding production line conveyor belt flow moment, outputting a drive control signal to the pneumatic actuator, causing the pneumatic push rod to extend instantaneously to perform a physical intervention action. Based on this timing-aligned intervention action, the physical defective inductor coil at the abnormal testing station is guided to detach from the production line conveyor belt, and its route is distributed to a specific defective product recycling bin mapped to the defective product discharge channel number, thereby completing the abnormal product isolation closed loop across physical distances.

[0100] On the parallel timeline for intercepting individual product quality at the end, a mechanical and electrical condition degradation assessment mechanism based on multi-dimensional data cross-validation extracts the peak characteristics of probe pressure acceleration and dynamic quality factor assessment values ​​captured by the front end, and executes equipment mechanical wear early warning steps. Simultaneously, the frequency of contact retry triggered by the front-end mechanical gating detecting failure to fall within the steady-state contact threshold is statistically analyzed within a preset time window.

[0101] The preset time window is denoted as Its characterization is used to smooth out short-term random dust interference or human contact, and to monitor the mechanical fatigue evolution cycle of manufacturing equipment over a long period of time. In this preferred embodiment, its range is set to 120 minutes to 480 minutes of continuous operation. This window is set to prevent overly aggressive judgments of frequent false maintenance alarms due to being misled by transient unstable oscillations when the production line is just started up or when changing rolls.

[0102] The frequency of contact retry is denoted as This represents the accumulated number of mechanical failures where the end-of-line test probe clamp fails to converge due to excessive physical vibration during downward pressure, forcing the control module to control the cylinder to lift and re-press down for alignment. In this preferred embodiment, the abnormal judgment threshold for contact retry frequency is set to more than 15 times within a single time window. This frequency directly reflects the degree of local physical fatigue of the air circuit seal ring leakage or aging of the return spring in the test end execution cylinder, preventing the unreported abnormalities of infinitely tolerating normalized minor mechanical vibrations and thus masking substantial damage to pneumatic hardware. Simultaneously, the total benchmark drift amount is extracted and accumulated over a long period, calculated as the yield pass judgment interval is repeatedly shifted and updated due to the call to batch yield benchmark shift compensation values.

[0103] The total baseline drift is denoted as This characterizes the cumulative extreme value of absolute electrical offset that is continuously compensated and adjusted to one side during the long-term continuous flow of a single production batch in order to accommodate the slow variation of physical resistance caused by mechanical tension in the upstream enameled copper wire raw materials. This embodiment performs a two-dimensional cross-domain joint degradation judgment on the above two heterogeneous electromechanical loss indicators. The logic determines that the contact retry frequency shows a monotonically increasing trend on the statistical time axis; the specific judgment basis is: the control center extracts the contact retry frequency statistics within at least three consecutive adjacent preset time windows, performs a timing difference comparison operation, and confirms that the current timing characteristic satisfies the preset control logic of this monotonically increasing trend only when the statistical value of the later time window is greater than the statistical value of the previous time window; thus confirming that the end-machine mechanical testing environment is continuously deteriorating; and simultaneously determining that the total reference drift exceeds the preset equipment wear safety limit threshold.

[0104] The equipment wear safety limit threshold is denoted as It represents the absolute physical red line limit for tolerating mechanical degradation of the upstream core winding machine transmission components, which in turn leads to abnormal stretching and thinning of the enameled wire, causing a rise in substrate resistance. In this preferred embodiment, its boundary is set within the range of +8% to +10% of the cumulative deviation from the original design nominal resistance value. This boundary is set to prevent the yield acceptance criteria from being excessively broadened due to the tolerance deviation of the upstream materials, ultimately resulting in a failure condition where a large number of defective insulating varnish products are continuously flowed and penetrated due to physical loss of control of the winding tension.

[0105] When the combined conditions of a monotonically increasing frequency of logical contact retry and a total baseline drift exceeding a preset equipment wear safety limit threshold are met, it indicates that both end-point aerodynamic degradation and upstream material mechanical stretching anomalies have simultaneously occurred. Meeting these dual degradation conditions indicates that end-point aerodynamic degradation and upstream material mechanical stretching have formed a superimposed resonance. At this point, the microprocessor generates a combined electromechanical warning command encapsulated with the device's network physical address. In the distributed communication topology, this command traverses the local PLC controller and is transmitted upwards via the workshop bus network (such as industrial Ethernet) to the production line maintenance system (e.g., a specific Manufacturing Execution System (MES) or Enterprise Asset Management System (EAM)). Upon receiving this combined electromechanical warning command, the upper-level system directly parses the source of degradation based on the embedded address, automatically dispatches predictive maintenance work orders for transmission components such as tension bearings, and simultaneously sends control signals back to the lower level, driving the corresponding physical address node's field three-color audible and visual alarm device to switch its physical state. This extends the control loop from the single-point data collection end to the overall asset operation and maintenance and personnel scheduling of the workshop.

[0106] To further address the physical and mechanical fatigue degradation caused by continuous 24 / 7 operation of the production line, the main control PLC's non-volatile memory continuously records the mechanical convergence state of the execution module and the gradually changing displacement characteristics of the upstream wire drawing process over several shifts. By using the intersection of dual heterogeneous thresholds, it targets physical hardware degradation that could lead to widespread downtime. See details below:

[0107] Table 3: Cross-domain joint physical wear characteristic evolution and predictive maintenance triggering state data

[0108]

[0109] As shown in Table 3, in the initial two time windows, the cumulative inaccuracy of the pneumatic probe was low, and the resistance shift caused by the tension of the front-end winding machine was far below the broadband tolerance limit. When the observation period progressed to the third observation interval, the logs revealed 16 independent abnormal pulses where the probe cylinder failed to fall into the steady-state defense line during the downward pressure phase. Furthermore, comparison with historical queue characteristics confirmed a monotonous upward deterioration trend at the physical level, directly reflecting irreversible wear and leakage of the pneumatic damping ring. Since the current ohmic baseline drift induced by excessive mechanical stretching of the entire batch of coils only reached +7.5%, not yet breaching the 8% process tolerance barrier, the maintenance signaling remained covertly intercepted. Until the fourth observation interval, the number of failed end-point mechanical resets surged to 22, and the bus monitoring showed that the total amplitude of the flexible reference's unilateral correction reached +8.6%, indicating that the physical deviation tolerance of the upstream and downstream physical equipment had been exhausted. At this point, the main control gateway logic gate flips, and the joint maintenance command containing the MAC identifier of the physical fault node is transparently routed to the upper-layer MES system via the industrial Ethernet. At the same time, the corresponding point is driven to perform a hardware flashing of three-color sound and light, which preemptively cuts off the vicious accident chain of the wire diameter being continuously stretched to the point of breakage.

[0110] Figure 1 The industrial conveyor belt on the left forms the quality inspection line for inductor coil products. The electrical testing station in the middle integrates downward-pressed pneumatic test probes, with miniature mechanical vibration sensors tightly attached to the sidewalls of the probe holders to capture recoil intensity; see reference. Figure 1 Downstream on the right side, a pneumatic actuator assembly is independently mounted on one side of the conveyor belt, with its axis of motion perpendicular to the main conveyor belt channel at a 90-degree angle. On the opposite edge of the conveyor belt, a waste chute is connected, its extension direction perpendicular to the conveyor belt's transport direction, forming a collinear cross-side physical collection channel with the thrust axis of the opposite cylinder. This station also includes an ambient temperature sensor and an industrial control cabinet on one side. The multidimensional signals collected from the aforementioned physical scenario are mapped onto the technical roadmap in the lower right corner, sequentially executing "feature acquisition," "steady-state cleaning and dimensionality reduction," and "probability aggregation and baseline translation," before finally performing the "boundary judgment and physical diversion" step. When the control center of the industrial control cabinet determines that the resistance value of a measured inductor coil exceeds the limit, a physical isolation command is issued.

[0111] Figure 2The X-axis represents the long-term system observation time slice scale (minutes), and the Y-axis represents the probe contact retry frequency (times) and the upstream total baseline drift limit (%), respectively. Marker F1 actually represents the critical value of 15 for the abnormal judgment threshold of probe contact retry frequency; visual status marker D1 reflects the value of 8, the equipment wear safety limit judgment threshold derived from logic; marker W1 indicates the value of 720 for the cross-validation silent state observation node during monotonically deteriorating processes; and marker W2 corresponds to the exact spatiotemporal coordinate point of 960 for the formal transition and the issuance of the electromechanical joint composite early warning command. The monotonically increasing double-broken line trajectory in the figure visually maps the dual irreversible physical degradation process of gas path sealing leakage at the production line execution end and abnormal extension caused by uncontrolled tension in upstream raw materials. When the horizontal time axis advances to the slice interval of status marker W1, the physical characteristic curve representing probe reset failure has broken through the F1 interception line, but the heterogeneous characteristic curve mapping the deviation of the process substrate still converges within the tolerance lower boundary of D1. During this stage, the logic gating maintains a silent state based on the cross-validation defense constraint, thereby eliminating false shutdown interventions induced by occasional oscillations of a single module in the factory in the spatiotemporal dimension. As the cycle deepens and reaches the absolute coordinate point of W2, the accumulated drift vector increases synchronously and crosses the safety limit. The dual-domain deterioration indicators form a physical resonance, driving the state machine output to instantly unlock the high-order early warning route.

[0112] Figure 3 When the test probe is pressed against the inductor pin terminal, the miniature mechanical vibration sensor collects the peak value of the maximum transient vibration under pressure. Specifically, a logic verification and judgment mechanism is executed using preset steady-state contact rules. For example... Figure 3 As shown in the timing diagram, if the peak value of the maximum transient vibration under pressure falls within the preset steady-state contact threshold range to complete the steady-state contact logic determination, the real-time DC resistance data at the corresponding timestamp is extracted and used as a steady-state DC resistance reference representing the inductor coil's crimp impedance. A technical step is performed to perform a dimensionality reduction and mapping transformation on the steady-state DC resistance reference using a dynamic temperature compensation multiplier, removing the pseudo-ohmic increment caused by environmental heat, thereby generating a dynamic quality factor evaluation value that characterizes the current reliability and health status of the inductor coil.

[0113] Figure 4This demonstrates the adaptive boundary update and terminal physical diversion execution process. The upper left corner of the screen displays a real-time one-dimensional resistance probability distribution map derived from statistical reconstruction. Specifically, the average offset of the dominant frequency cluster peaks is extracted to generate a batch yield benchmark shift compensation value, which is then used to perform a shift update operation on the yield pass judgment interval. The diagram shows the process of dynamically and flexibly correcting the judgment interval as the group resistance distribution drifts. When the downstream judgment that the current cycle's evaluation value meets the out-of-bounds judgment condition, the inductor coil is marked as a defective entity, and an isolation control signal including timing compensation is generated across spatial distance and sent to the pneumatic isolation push rod. The push rod valve island is activated, the piston rod quickly pops out, and the isolation push rod terminal executes the push plate to physically guide the defective inductor coil away from the production line conveyor belt to the waste chute, completing the PHM closed-loop isolation from data statistics, flexible boundary decision-making to precise physical diversion.

[0114] The computational logic involved in this application can be constructed using machine learning algorithms such as regression analysis to build mathematical models, and implemented using computational tools such as Python and R. To eliminate the influence of physical dimensions, the input parameters of each formula can be dimensionlessly processed using techniques such as maximum-minimum normalization or Z-score standardization. Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products, and can be in the form of pure hardware, pure software, or a combination of hardware and software, and can be implemented on storage media (including but not limited to disks, optical disks, etc.) containing computer-usable program code.

[0115] This application is described with reference to relevant flowcharts and / or block diagrams. It should be understood that each flowchart / block and combination thereof in the flowcharts and / or block diagrams can be implemented by computer program instructions. These instructions can be stored in a computer-readable storage device or executed by a processor of a general-purpose computer, a special-purpose computer, or other programmable devices to achieve the functions specified in the figures. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope thereof. If such modifications and variations fall within the protection scope of the claims of this application and their equivalents, this application is also intended to cover such modifications and variations.

Claims

1. A method for testing the yield of inductor coils based on real-time resistance monitoring, characterized in that, Performed by the production line testing control equipment, the specific steps include: Step S1: Obtain the real-time DC resistance data periodically reported by the current test node, the peak value of the probe pressing acceleration of the probe fixture corresponding to the test node, and the coil structure characteristic parameters bound to the current production batch; Step S2: Compare the peak characteristic of the probe's downward acceleration with a preset steady-state contact threshold, and extract the real-time DC resistance data that falls within the steady-state contact threshold as a steady-state DC resistance reference; perform quality factor conversion processing on the steady-state DC resistance reference and the coil structure characteristic parameters to generate a dynamic quality factor evaluation value. Based on the multiple dynamic quality factor evaluation values ​​continuously generated within the current production batch, perform data aggregation statistical verification, extract batch yield benchmark shift compensation values, and use the batch yield benchmark shift compensation values ​​to perform shift update operations on the pre-configured yield qualification judgment interval. Step S3: In response to the logic determination that the dynamic quality factor evaluation value of the current cycle does not meet the preset qualified threshold condition in the updated yield qualified judgment interval, the test node of the current cycle is marked as an abnormal test station, and a physical isolation control instruction for the production line execution end is generated. The physical isolation control instruction is configured to drive the production line execution end to perform a physical shunting action on the inductor coil at the abnormal test station. Obtain the coil structure feature parameters bound to the current production batch, specifically including: obtaining the batch identification signaling triggered by the production line material scanning equipment during batch switching; Based on the batch identification signaling, a joint search is performed in the preset process specification configuration library to extract the standard wire diameter data and parallel strand number information bound to the batch identification signaling, and the standard wire diameter data and the parallel strand number information are packaged and merged into the coil structure feature parameters. The steady-state DC resistance reference and the coil structural characteristic parameters are converted using a quality factor to generate a dynamic quality factor evaluation value, specifically including: Based on the standard wire diameter data and the parallel strand number information in the coil structure feature parameters, multi-dimensional feature joint spatial addressing is performed in the preset standard resistance value process comparison table to extract the standard DC resistance theoretical value corresponding to the current production batch composite specification. Calculate the differential offset between the steady-state DC resistance reference and the theoretical value of the standard DC resistance; perform deviation proportional amplification calculation based on the differential offset and the preset quality conversion weighting coefficient to generate the dynamic quality factor evaluation value characterizing the current reliability and health status of the inductor coil.

2. The inductor coil yield testing method based on real-time resistance monitoring according to claim 1, characterized in that: Before logically comparing the peak feature of the probe pressure acceleration with a preset steady-state contact threshold, the method further includes: extracting the timing vibration waveform of the test node during the DC resistance sampling period; performing waveform extreme value extraction processing on the timing vibration waveform to extract the maximum pressure transient vibration peak value characterizing the probe clamp, and using the maximum pressure transient vibration peak value as the peak feature of the probe pressure acceleration.

3. The inductor coil yield testing method based on real-time resistance monitoring according to claim 1, characterized in that: Based on the multiple dynamic quality factor evaluation values ​​continuously generated within the production batch, perform data aggregation statistical verification and extract batch yield benchmark shift compensation values. Specifically, this includes: within the initial processing cycle of the production batch, statistically analyze the percentage of contact failure alarms that were intercepted and rejected because they did not fall within the steady-state contact threshold. In response to the fact that the proportion of the number of poor contact alarms does not exceed the preset maximum allowable abnormality statistical limit, multiple dynamic quality factor evaluation values ​​that fall within the steady-state contact threshold are extracted to construct an effective evaluation value cache set; Calculate the data distribution dispersion and the average numerical offset for the evaluation value sequence within the valid evaluation value cache set; in response to the logical determination that the data distribution dispersion meets the preset high clustering condition, extract the average numerical offset as the batch yield benchmark shift compensation value.

4. The inductor coil yield testing method based on real-time resistance monitoring according to claim 1, characterized in that: Generate physical isolation control instructions for the production line execution end, specifically including: based on the test station equipment code corresponding to the abnormal test station, write the action delay time parameter for controlling the rejection mechanism and the defective product discharge channel number into the physical isolation control instructions; After the physical isolation control command is issued, it is configured to drive the production line execution end to guide the inductor coil at the abnormal test station into the defective recycling bin corresponding to the defective waste discharge channel number when the production line conveyor belt turns at the time corresponding to the action delay time parameter.

5. The inductor coil yield testing method based on real-time resistance monitoring according to claim 1, characterized in that: Based on the peak characteristics of the probe pressing acceleration and the dynamic quality factor evaluation value, the method also includes a device mechanical wear early warning step: synchronously counting the frequency of contact retry triggered due to failure to fall into the steady-state contact threshold within a preset time window, and the total benchmark drift amount of the yield qualification judgment interval being shifted and updated due to the batch yield benchmark shift compensation value. In response to a logical determination that the frequency of contact retry shows a monotonically increasing trend and the total baseline drift exceeds a preset equipment wear safety limit threshold, an electromechanical joint composite early warning command is generated. The electromechanical joint composite early warning command is configured to trigger the production line maintenance system to generate a predictive maintenance work order for upstream transmission components.

6. The inductor coil yield testing method based on real-time resistance monitoring according to claim 1, characterized in that: Based on the differential offset and a preset quality conversion weighting coefficient, a deviation proportional amplification calculation is performed to generate the dynamic quality factor evaluation value characterizing the current reliability and health status of the inductor coil, specifically including: Acquire ambient temperature fluctuation parameters collected by temperature sensing hardware deployed around the current production line test node; The ambient temperature fluctuation parameter is matched with the standard wire diameter data in the coil structure characteristic parameter to extract the dynamic temperature compensation multiplier corresponding to the current test environment. The preset quality conversion weight coefficient is replaced by the dynamic temperature compensation multiplier to perform temperature drift calibration amplification calculation on the differential offset, thereby generating the dynamic quality factor evaluation value.

7. The inductor coil yield testing method based on real-time resistance monitoring according to claim 3, characterized in that: Calculate the data distribution dispersion and the average numerical offset of the evaluation value sequence within the valid evaluation value cache set, specifically including: Perform resistance interval density statistical aggregation processing on the evaluation value sequence within the valid evaluation value cache set to map the evaluation value sequence to a one-dimensional resistance probability distribution map; Extract the dominant frequency clustering peak containing the largest amount of data from the one-dimensional resistance probability distribution spectrum, and extract the frequency distribution width index of the dominant frequency clustering peak as the data distribution dispersion. The difference parameter between the statistical center of the main frequency cluster peak and the preset zero deviation benchmark is calculated, and the difference parameter is extracted as the numerical average offset.

8. The inductor coil yield testing method based on real-time resistance monitoring according to claim 7, characterized in that: After performing resistance interval density statistical aggregation processing on the evaluation value sequence within the valid evaluation value cache set, the method further includes a baseline update interception step based on abnormal distribution states: In response to the extraction of at least two discrete independent peaks in the one-dimensional resistance probability distribution spectrum, both of which exceed the preset effective baseline, the logic determines that the current production batch exhibits a heterogeneous resistance bimodal distribution characteristic. In response to the determination of the existence of the heterogeneous resistance bimodal distribution feature, an interception action is triggered to block the extraction and translation update operation of the batch yield benchmark translation compensation value, and a mixed batch source locking control signal is generated for the upstream winding execution end. The mixed batch source locking control signal is configured to trigger the production line main control system to urgently interrupt the wiring action of the associated winding equipment and drive the conveyor belt to perform physical isolation of the entire pallet of the current batch.

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