High-sensitivity detection and identification of counterfeit components in public power systems via EMI frequency KIVIAT tubes.

Abstract

The counterfeit status of a target utility device is detected by the following steps: selecting a set of frequencies reflecting the load dynamics or other information of a reference utility device during a power test sequence; acquiring a target electromagnetic interference (EMI) signal emitted by the target utility device during the power test sequence; creating a sequence of target kiviat maps based on the amplitude of the target EMI signal at each frequency in the set of observed frequencies during the power test sequence to form a target kiviat tube EMI fingerprint; comparing the target kiviat tube EMI fingerprint with a reference kiviat tube EMI fingerprint of the reference utility device during the power test sequence to determine whether the target utility device and the reference utility device belong to the same type; and generating a signal indicating the counterfeit status based at least in part on the result of the comparison.

Description

Background Technology

[0001] It is estimated that counterfeit electronic components in the international supply chain cause $200 billion in losses annually across all industries that use electronics, including information technology, healthcare, military, gaming, transportation, and utilities. Counterfeit systems often look so realistic that service engineers cannot distinguish them from genuine systems with a simple visual inspection. However, counterfeit systems typically contain scrap components from obsolete systems, cheaply manufactured components, or older components from recycled vintage systems that have been repackaged to resemble genuine systems.

[0002] Such systems are then integrated into the supply chain through brokerage channels. When counterfeit systems are shipped to customers, they often fail upon arrival or within a short time, resulting in significant warranty losses, shortened mean time between failures (MTBF), and customer dissatisfaction. In some cases, counterfeit systems even include "spy chips" or "modified chips," which can grant unauthorized access to or control of the counterfeit system, posing a significant risk to infrastructure. In the utility sector, the use of counterfeit electronic components is not only costly but also presents major security concerns. Failures in utility components can lead to life-threatening situations such as power outages and fires.

[0003] The North American Electric Reliability Corporation (NERC, the North American utility regulator) and the Federal Energy Reliability Council (FERC) have issued Supply Chain Risk Management Regulation (No. CIP-013-1) to mitigate risks and ensure the reliable operation of high-capacity electrical systems. This regulation requires all utilities in North America to implement technologies to detect counterfeit components used in all power system assets used in generation facilities, Supervisory Control and Data Acquisition (SCADA) subsystems, and distribution network assets by July 2020. Summary of the Invention

[0004] In one embodiment, a method for detecting a counterfeit state of a target utility device includes: selecting a set of frequencies reflecting the load dynamics of a reference utility device during a power test sequence; obtaining a target electromagnetic interference (EMI) signal emitted by the target utility device during the power test sequence; creating a sequence of target kiviat maps based on the amplitude of the target EMI signal at each frequency in the set of observed frequencies during the power test sequence to form a target kiviat tube EMI fingerprint; comparing the target kiviat tube EMI fingerprint with a reference kiviat tube EMI fingerprint of the reference utility device during the power test sequence to determine whether the target utility device and the reference utility device belong to the same type; and generating a signal indicating a counterfeit state based at least in part on the result of the comparison.

[0005] In one embodiment, a method for detecting counterfeit state of a target utility device includes generating an estimate of the amplitude at each frequency in a set of frequencies by a state estimation model trained on a reference kiviat tube EMI fingerprint, wherein the target kiviat map is the estimated kiviat map.

[0006] In one embodiment, a method for detecting the counterfeit status of a target utility device includes a comparison that further includes normalizing each axis of each target kiviat plot relative to a unit circle, which is a value plotted on that axis of a corresponding reference kiviat plot at the same observation point in the EMI fingerprint of a reference kiviat tube.

[0007] In one embodiment, a method for detecting the counterfeit status of a target utility device includes a comparison that further comprises generating an error metric from the annular residual between the target kiviat map and the unit circle on an axis normalized to represent the same observation in the reference kiviat tube EMI fingerprint as a unit circle, wherein the error metric is the cumulative area of ​​the annular residual between the unit circle and the target kiviat map over all observations.

[0008] In one embodiment, a method for detecting the counterfeit status of a target utility device includes a comparison that further comprises generating an error metric from the annular residual between the target kiviat image and the reference kiviat image at the same observation in the EMI fingerprint of the reference kiviat tube, wherein the error metric is the cumulative area of ​​the annular residual between the reference kiviat image and the target kiviat image across all observations.

[0009] In one embodiment, a method for detecting the counterfeit status of a target utility device, wherein a reference utility device is a genuine utility device of a specific type, further includes generating a signal to indicate that the target utility device is identified as genuine in response to determining that the target utility device and the reference utility device are (i) of the same type, and (ii) of different types, generating a signal to indicate that the target utility device is a suspected counterfeit.

[0010] In one embodiment, a method for detecting the counterfeit status of a target utility device further includes transmitting a target kiviat EMI fingerprint to a counterfeit analysis system in response to a signal that the target utility device is a suspected counterfeit.

[0011] In one embodiment, a method for detecting the counterfeit status of a target utility device further includes transmitting supply chain information about the target device to a counterfeit analysis system.

[0012] In one embodiment, a method for detecting the counterfeit status of a target utility device further includes receiving, in response to a signal that the target utility device is a suspected counterfeit, additional information about the suspected counterfeit configuration from a counterfeit analysis system, wherein the additional information includes one or more of the following: (i) confirmation that the suspected counterfeit is a known type of counterfeit; (ii) pervasiveness information describing the prevalence of utility devices with suspected counterfeit configurations; (iii) source information describing the origin of utility devices with suspected counterfeit configurations; and (iv) supply chain information describing how the target device can enter the supply chain.

[0013] In one embodiment, a method for detecting the counterfeit status of a target utility device, wherein a reference utility device is a known counterfeit utility device of a specific type, further includes, in response to determining that the target utility device and the reference utility device are (i) of the same type, generating a signal to indicate that the target utility device is identified as a counterfeit device of the specific type, and (ii) are not of the same type, generating a signal to indicate that the target utility device is not a counterfeit device of the specific type.

[0014] In one embodiment, a method for detecting the counterfeit status of a target utility device further includes retrieving a reference kiviat tube EMI fingerprint from a database for the reference utility device.

[0015] In one embodiment, a method for detecting the counterfeit status of a target utility appliance further includes displaying information based at least in part on signals using a graphical user interface.

[0016] In one embodiment, a non-transitory computer-readable medium storing computer-executable instructions, which, when executed by at least one processor of a computer, cause the computer to: select a set of frequencies reflecting the load dynamics of a reference utility device during a power test sequence; obtain a target electromagnetic interference (EMI) signal emitted by a target utility device during the power test sequence; create a sequence of target kiviat maps based on the amplitude of the target EMI signal at each frequency in the set of observed frequencies during the power test sequence to form a target kiviat tube EMI fingerprint; compare the target kiviat tube EMI fingerprint with a reference kiviat tube EMI fingerprint of the reference utility device during the power test sequence to determine whether the target utility device and the reference utility device belong to the same type; and generate a signal indicating a counterfeit status based at least in part on the result of the comparison.

[0017] In one embodiment, the non-transient computer-readable medium, wherein the instructions for causing a computer to create a sequence of target kiviat graphs further include instructions for causing the computer to generate estimates of amplitude at each frequency of the set of frequencies by means of a state estimation model trained on a reference kiviat tube EMI fingerprint, wherein the target kiviat graph is the estimated kiviat graph.

[0018] In one embodiment, a computing system includes: a processor; a memory operatively connected to the processor; a radio transceiver operatively connected to the processor and the memory; and a non-transitory computer-readable medium operatively connected to the processor and the memory and storing computer-executable instructions, which, when executed by at least the processor, cause the computing system to: select a set of frequencies reflecting the load dynamics of a reference utility device during a power test sequence; acquire, via the radio transceiver, a target electromagnetic interference (EMI) signal emitted by a target utility device during the power test sequence; create a sequence of target kiviat maps based on the amplitude of the target EMI signal at each frequency in the set of observed frequencies during the power test sequence to form a target kiviat tube EMI fingerprint; compare the target kiviat tube EMI fingerprint with a reference kiviat tube EMI fingerprint of the reference utility device during the power test sequence to determine whether the target utility device and the reference utility device belong to the same type; and generate a signal indicating a counterfeit status based at least in part on the result of the comparison. Attached Figure Description

[0019] Various systems, methods, and other embodiments of this disclosure are illustrated in conjunction with the accompanying drawings, which form a part of this specification. It will be appreciated that the element boundaries (e.g., boxes, groups of boxes, or other shapes) illustrated in the figures represent one embodiment of a boundary. In some embodiments, one element may be implemented as multiple elements, or multiple elements may be implemented as one element. In some embodiments, an element shown as an inner component of another element may be implemented as an outer component, and vice versa. Furthermore, elements may not be drawn to scale.

[0020] Figure 1 The illustration shows an example of an EMI fingerprint scanner for a utility device, associated with high-sensitivity detection and identification of counterfeit components in a utility power system using EMI frequency kiviat tubes.

[0021] Figure 2 The illustration depicts one embodiment of an environment in which an EMI fingerprint counterfeit scanner is operated, which is associated with the high-sensitivity detection and identification of counterfeit components in a utility power system using EMI frequency kiviat tubes.

[0022] Figure 3 An embodiment of a method associated with highly sensitive detection and identification of counterfeit components in a utility power system using EMI frequency kiviat tubes is illustrated.

[0023] Figure 4 The illustration shows an example of a graphical user interface associated with using an EMI frequency Kiviat tube to confirm to the user that the target utility equipment is genuine.

[0024] Figure 5 The illustration shows an example of a graphical user interface associated with warning users about counterfeit components in target utility equipment using an EMI frequency kiviat tube.

[0025] Figure 6 An embodiment of a method associated with a display for highly sensitive detection and identification of counterfeit components in a utility power system using EMI frequency kiviat tubes is illustrated.

[0026] Figure 7 The illustrations depict embodiments of computing systems configured with the example systems, methods, and / or special equipment disclosed herein. Detailed Implementation

[0027] This paper describes a system and method for providing highly sensitive detection and identification of counterfeit components in utility power systems by applying electromagnetic interference (EMI) frequency Kiviat tubes.

[0028] EMI signals are generated by power utility equipment (such as transformers, generators, inverters, meters, or other power grid systems) during operation. These EMI signals are generally considered noise, but they can also carry information that can be used to generate a unique EMI fingerprint for the utility equipment. For example, the EMI emitted by a target utility equipment with an unknown configuration of components can be scanned to generate a target EMI fingerprint for the target utility equipment. The generated target EMI fingerprint can be compared with a reference EMI fingerprint of a reference utility equipment with a known configuration to confirm that the target utility equipment is a known brand, model, and configuration, or to indicate that the target utility equipment is not a known brand, model, and configuration, and therefore may contain one or more potentially counterfeit components, or may be suspected of being entirely counterfeit.

[0029] This counterfeit detection technology is "passive" because it does not involve disassembling the power system electronics of the target utility equipment to perform internal inspections, such as visual or photographic checks. It's important to note that counterfeit detection technologies involving disassembly are inefficient and often create subsequent problems in the inspected utility equipment, even if they don't detect any counterfeit components. In contrast, this passive technology makes it possible to periodically inspect power system equipment in the supply chain, at ports of entry, or when power system customers receive the system during initial setup preparation and power-on self-test (POST) operations. Therefore, this new technology helps ensure that counterfeit components, "spy chips," or "modified chips" are not installed in power system electronics between component manufacturing and the "assembly plant," or during transport between the assembly plant and the utility system. Furthermore, this new technology does not require hardware modifications to the utility power system, thus ensuring backward compatibility with conventional power systems commonly used by utilities.

[0030] In one embodiment, a specific form of EMI fingerprint (i.e., EMI kiviat tube fingerprint (EMI-KT fingerprint)) can be used to enhance the sensitivity of detecting and identifying counterfeit components or counterfeit utility equipment. In one embodiment, a kiviat tube is a series of kiviat plots (also known as spider plots, star plots, or radar plots) of data at time intervals (such as evenly spaced time intervals). A kiviat plot is a multi-vector line graph used to represent multivariate data in two dimensions, where the values ​​of the data are represented on axes starting from the same point. Kiviat plots are well-suited for illustrating outliers and commonalities among datasets. In one embodiment, an EMI-KT fingerprint includes the data required to describe each kiviat plot that constitutes a kiviat tube. For example, an EMI-KT fingerprint may include a time series of kiviat plots with N axes. Each axis represents the signal strength at one of N frequencies. The N frequencies are N frequencies determined to best reflect the dynamics of the target device. Therefore, these N frequencies best convey information about the target device's response to a dynamic test sequence.

[0031] It is important to note that while in one embodiment, the kiviat tubes of the reference and / or target EMI-KT fingerprints can be generated and visually displayed on a graphical user interface (GUI) for user reference, the more significant feature of representing EMI fingerprints as kiviat tubes is the enhanced ability of EMI counterfeit detection systems to distinguish genuine utility equipment from counterfeit equipment. Forming the EMI fingerprint as a “dynamic rolling tube” of a kiviat map along the time axis provides a rich and detailed fingerprint that is particularly sensitive to differences between the reference and target fingerprints. This is at least in part due to the nature of the kiviat map. In the kiviat map, the area contained grows proportionally to the square of the linear measurement, thus highlighting even minute differences in signal strength between observations at a particular time as areas between the reference and target maps. Integrating these areas over time produces a “residual volume” in the observation of the EMI-KT fingerprint, thereby increasing sensitivity to deviations beyond that of other EMI fingerprinting techniques. Residual volume—the time integral of the residual area between the reference (golden system) and the target (test unit) signal strength kiviat plots at the top N most informative frequencies—forms a novel prognostic metric over a series of time observations. In one embodiment, the cumulative cylinder error metric (or CCEM, described in further detail elsewhere herein) is a form of residual volume. Residual volume (and its variants, such as CCEM) can be used to contribute significantly improved difference detection (sensitivity) to system prediction algorithms used for deterministic and repeatable identification between counterfeit and genuine utility systems and components.

[0032] In one embodiment, the reference EMI-KT fingerprint for the brand, model, and configuration (or “type”) of the utility device is taken from a reference utility device. The reference utility device for brand, model, and configuration is a device identified as a genuine example of the brand, model, and configuration of the utility device (which may be referred to as a “Gold System” or “GS”). In one embodiment, it is further confirmed that the reference utility device operates optimally, or at least that it operates within acceptable parameters. In one embodiment, the target utility device (which may be referred to as a “Unit Under Test” or “UUT”) undergoes a pattern recognition process that compares the target EMI-KT fingerprint obtained from the target utility device with the reference EMI-KT fingerprint using a configuration similarity metric called the Cumulative Cylindrical Error Metric (CCEM).

[0033] Although we describe the invention in the context of power utility equipment, the general principles and techniques of the invention can be applied to any electronic system that includes at least one electronic component.

[0034] —Example EMI fingerprint counterfeit detector—

[0035] Figure 1 The illustration depicts an embodiment of an EMI fingerprint scanner 100 and an example target utility device 105 associated with high-sensitivity detection and identification of counterfeit components in a utility power system using EMI frequency kiviat tubes. The EMI fingerprint scanner 100 includes an antenna (or other EMI signal sensor) 115 connected to a radio transceiver 120, such as a software-defined radio transceiver, an AM / FM radio transceiver, or other radio transceivers configured to connect to the EMI fingerprint scanner 100. The EMI fingerprint scanner 100 also includes local data storage 150 connected to the radio transceiver 120. The EMI fingerprint scanner 100 also includes high-sensitivity EMI kiviat tube counterfeit component detection and identification logic 155. The EMI fingerprint scanner 100 also includes a network interface 160 and a display 165.

[0036] Note that when power is supplied to utility device 105, utility device 105 generates an EMI signal 110. Utility device 105 may generate a first-type similar signal when the device is at high power, fully loaded, or otherwise powered, and may generate a second-type similar signal when the device is at low power, idle, or otherwise powered off. Further note that after power to utility device 105 is cut off (power-off state), utility device 105 may continue to generate EMI signal 110 for at least a period of time. The EMI signal is generated by one or more internal components of utility device 105, which may include, but are not limited to, controllers, switches, motors, inductor / transformer windings, capacitors, sensors, and other components. In some cases, the EMI signal may be generated by interaction between multiple components. In one embodiment, antenna 115 is configured to sense the EMI signal 110 and apply the EMI signal to a radio transceiver 120 coupled to antenna 115. Depending on the configuration of antenna 115 and radio transceiver 120, EMI signals are sensed over a wide frequency spectrum, for example, from approximately 500 kHz to approximately 4 GHz. Other ranges may also be suitable, and the frequency range available to the EMI fingerprint counterfeit scanner 100 can be dominated by the combination of antenna 115 and radio transceiver 120.

[0037] In one embodiment, antenna 115 may include: a dipole antenna, a Yagi-Uda antenna, a loop antenna, an electrically short antenna (e.g., an open-end wire with a length less than a quarter wavelength), a fractal antenna, a parabolic antenna, a microstrip antenna, a quadrilateral antenna, a random wire antenna (e.g., an open-end wire with a length greater than one wavelength), a Beveridge antenna, a helical antenna, a phased array antenna, and any other type of antenna now known or developed in the future. In a simple and inexpensive embodiment, antenna 115 may be an insulated wire in which a fixed length of insulation is stripped. In one embodiment, the type and length of the antenna can be selected to achieve optimal discrimination sensitivity and robustness.

[0038] Antenna 115 can be positioned either close to or far from the target utility device 105. A smaller distance between the target utility device 105 and antenna 115 is preferred to achieve better sensitivity in antenna 115 and thus a higher signal-to-noise ratio (SNR) in the EMI fingerprint scanner 100. In addition to distance, the sensitivity of antenna 115 can also be affected by its orientation relative to the target utility device 105.

[0039] In one embodiment, antenna 115 is positioned at a predetermined distance and orientation relative to target utility device 105 during scanning. This predetermined distance and orientation may be the same distance and orientation used for detecting reference EMI signals from a reference utility device of the same brand and model as target utility device 105. Consistency in antenna placement relative to the scanned utility device increases the ability of the EMI fingerprint counterfeit scanner 100 to match and distinguish between target EMI fingerprints and reference EMI fingerprints.

[0040] In one embodiment, antenna 115 may be fixed to EMI fingerprint scanner 100. In one embodiment, antenna 115 may be in a fixed position (distance and orientation) relative to target utility device 105 during scanning of target utility device 105 by EMI fingerprint scanner 100. For example, antenna 115 may be placed close to target utility device 105 and remain stationary during scanning. Antenna 115 may be fixed to or within the housing of target utility device 105. Antenna 115 may be mechanically fixed (e.g., with bolts, screws, or clips), magnetically fixed, or fixed with an adhesive (e.g., sensor wax). In one embodiment, multiple antennas and / or radio transceivers (not shown) may be positioned at different locations and orientations relative to target utility device 105 during scanning, and measurements obtained from multiple antennas and / or radios may be combined. In one embodiment, antenna 115 may move to multiple different locations and orientations relative to target utility device 105 during scanning. These various antenna positions and configurations, as well as other implementations of positions and configurations, can be selected as desired to improve the signal-to-noise ratio (SNR) of the detected EMI signals of the entire target utility 105, or to highlight EMI signals emitted by specific components of the target utility 105.

[0041] In one embodiment, the radio transceiver 120 is configured to convert received EMI signals from analog signals to digital signals and record the power amplitude and frequency of the signals at defined time intervals. In one embodiment, the radio transceiver 120 may store the recorded signals as a data structure in a local data storage device 150, or provide them directly to the high-sensitivity EMI kiviat tube counterfeit component detection and identification logic 155 for analysis.

[0042] In one embodiment, the local data storage device 150 is a local data repository for a mobile device or computer. In one embodiment, the high-sensitivity EMI kiviat tube counterfeit component detection and identification logic 155 is a processor of the mobile device or computer specifically configured with instructions to perform one or more of the functions of the system described herein. For example, the instructions may be stored in the local data storage device 150 and retrieved by the processor as needed to execute logic 155.

[0043] —Example environment for EMI fingerprinting—

[0044] Figure 2An embodiment of environment 200 is illustrated, in which an EMI fingerprint counterfeit scanner 100 is associated with the high-sensitivity detection and identification of counterfeit components in a utility power system using EMI frequency kiviat tubes.

[0045] In one embodiment, the EMI fingerprint counterfeit scanner 100 is a mobile device 205 or computer 210 coupled to a software-defined radio transceiver 120 and an antenna 115. In one embodiment, a network interface 160 is configured to enable the EMI fingerprint counterfeit scanner 100 to interact with one or more remote computers via a communication network 215. In one embodiment, the EMI fingerprint counterfeit scanner 100 can send requests to and receive responses from a web server, such as a web interface server 220. For example, such communication can take the form of a Remote State Transfer (REST) ​​request using JavaScript Object Notation (JSON) as the data exchange format, or in another example, a Simple Object Access Protocol (SOAP) request traveling to and from an XML server.

[0046] In one embodiment, web interface server 220 is configured to enable EMI fingerprint counterfeit scanner 100 to access resources provided by cloud application infrastructure 225. In addition to web interface server 220, cloud application infrastructure 225 also includes server-side high-sensitivity EMI kiviat tube counterfeit component detection and identification logic (“server-side logic”) 230 and one or more data storage devices 235. Web interface server 220, server-side logic 230, and data storage devices 235 are interconnected via local network 240. In one embodiment, server-side logic 230 is one or more computing devices specifically configured with instructions to perform one or more of the functions of the system described herein.

[0047] In one embodiment, the analysis of the target EMI-KT fingerprint is performed by the EMI fingerprint counterfeit scanner 100. In another embodiment, the analysis of the target EMI-KT fingerprint is performed by server-side logic 230 in response to a request from the EMI fingerprint counterfeit scanner 100, and the results are returned to the EMI fingerprint counterfeit scanner 100 for display to the user.

[0048] In one embodiment, cloud application infrastructure 225 is operated as a counterfeit analysis system by at least a portion of server-side logic 230 (counterfeit analysis logic 233). In one embodiment, cloud application infrastructure 225 is a multi-tenant system for storing and processing information associated with EMI fingerprint counterfeit detection and identification of one or more operating companies that are tenants of the cloud application infrastructure. The counterfeit analysis system is configured to analyze information associated with EMI-KT fingerprints of suspected and confirmed counterfeit utility equipment provided by one or more tenants of the counterfeit analysis system using various analytical tools. The information being analyzed may be submitted by tenants of the system, manufacturers of the utility equipment, or law enforcement entities. Analysis may be performed within information provided by a single tenant or across information provided by multiple tenants. Analysis may include identifying how common or prevalent utility equipment with suspected or confirmed counterfeit configurations are. Analysis may provide further segmentation of this prevalence information, such as indicating the prevalence of suspected or confirmed counterfeit configurations: in a specific geographic area or location, in a specific tenant's system or device, and in other locations in the detection or supply chain at a specific port of entry. The analysis can also identify commonalities among supply chain information of multiple instances of detected counterfeit utility equipment to identify potential sources of the counterfeit system. Generally, analyses that help identify the source of counterfeit utility equipment or help identify previously undetected counterfeit utility equipment installed in the utility system can be included in counterfeit analysis logic 233.

[0049] In one embodiment, environment 200 further includes a test sequence generator 245. The test sequence generator 245 operates to control the power passing through one or more components in the target utility equipment 105. In operation, the test sequence generator 245 can create a square wave with the power amplitude passing through the components of the target utility equipment 105. Instructions for the test sequence (including the amplitude and duration of the square wave) can be pre-programmed in the test sequence generator 245 and / or controlled by the EMI fingerprint scanner 100.

[0050] In one embodiment, for some types of utility equipment, a suitable square wave can be generated by switching the power supply delivered to the target utility equipment 105 between high-power and low-power supply states to place the target utility equipment 105 into a "power-on" operating state (high-power supply) and a "power-off" operating state (low-power supply). In one embodiment, the power-on state can be a full-power supply state. In one embodiment, the power-on state can be a supply state relatively higher than the low-power supply state, and the power-off state is a supply state relatively lower than the high-power supply state. In one embodiment, the power-off state can be a supply state where the power supply is completely cut off—a "no power" supply state. In one embodiment, the power-off state can be an "idle" supply state, wherein the power supplied to the target utility equipment 105 is the minimum power required to maintain the target utility equipment 105 operating at the lowest possible power level. In this configuration, a test sequence generator 245 is placed in series between the power supply 250 and the target utility equipment 105 and controls the delivery of power from the power supply 250 to the target utility equipment 105. The test sequence generator is configured to provide high-power and low-power test sequences to the target utility equipment 105 according to instructions for test sequences used to power on and off the target utility equipment 105.

[0051] In another embodiment, for some types of utility equipment, a suitable square wave can be generated by switching the load on the target utility equipment 105 between high-power and low-power draw states to place the target utility equipment 105 into a powered-on operating state (high-power draw) and a powered-off operating state (low-power draw). In one embodiment, the powered-on state can be a full-power draw state. In one embodiment, the powered-on state can be a power draw state relatively higher than the low-power draw state, while the powered-off state is a power draw state relatively lower than the high-power draw state. In one embodiment, the powered-off state can be a power draw state with the load completely disconnected—a “no-power” power draw state. In one embodiment, the powered-off state can be an “idle” power draw state, wherein the power drawn from the target utility equipment 105 is the minimum power required to maintain the target utility equipment 105 operating at the lowest possible power level. In this alternative configuration, a test sequence generator 245 is placed in series between the target utility equipment 105 and ground 255 and controls the power load drawn from the target utility equipment 105. The test sequence generator is configured to provide high-power and low-power power extraction (load) test sequences to the target utility equipment 105 according to instructions for test sequences used to power on and off the target utility equipment 105.

[0052] In one embodiment, the power-on and power-off test sequence includes multiple power-on or power-off cycles. In one embodiment, the test sequence cycles between approximately equal portions of: (i) providing a high power supply or load to power on the target device, and (ii) providing a low power supply or load to power off the target device. In one embodiment, a test sequence having a period of 30 seconds to the target device under a high power supply or load (power on) and a period of 30 seconds to the target device under a low power supply or load (power off) may be appropriate. In other embodiments, other test sequences may be appropriate, such as those with shorter or longer power-on / power-off periods, or those with unequal power-on / power-off periods. In one embodiment, the test sequence generator is configured to automatically control the power or load to alternately power on and off the target device in a repeatable test sequence.

[0053] In one embodiment, where the test sequence generator 245 controls the power supply to the utility equipment, the test sequence generator 245 provides high and low power from the power supply 250 to an input (such as one of the primary terminals of a transformer) to energize and de-energize the utility equipment separately in a test sequence. For example, power to the utility equipment is alternately supplied and interrupted in a repetitive cycle. In another embodiment, where the test sequence generator 245 controls the power load drawn from the utility equipment, the test sequence generator 245 provides high and low power loads respectively at an output (such as one of the secondary terminals of a transformer) to energize and de-energize the utility equipment separately in a test sequence. For example, the load on the utility equipment is alternately applied and stopped in a repetitive cycle.

[0054] In one embodiment, the test sequence generator can be configured by instructions to generate waveforms other than square waves. The waveforms can include gradual transitions between power-off and power-on states, as well as transitions to and from different partially powered states.

[0055] In one embodiment, a test sequence generator 245 can be added to a buffer for initial power-on self-test (POST). Utility assets are typically unpacked, then powered on first in a POST test before being installed into a production system. Therefore, performing an EMI-KT fingerprint forgery scan is a convenient time to perform when setting up the target utility for POST testing. In one embodiment, a test sequence is applied to the target utility when it is set up in the buffer for testing.

[0056] —Example configuration for discovery and counterfeit detection methods—

[0057] In one embodiment, one or more steps of the method described herein may be performed by a processor of one or more computing devices (such as reference numerals). Figure 7 The processor 710 shown and described performs (i) accessing memory (such as reference Memory) Figure 7 (ii) The memory 715 and / or other computing device components shown and described) and are configured with logic to enable the system to perform steps of the method (such as reference) Figure 7 The utility asset allocation discovery and counterfeit detection logic 730 shown and described herein. For example, the steps of a processor accessing and reading from or writing to memory to perform the computer-implemented methods described herein. These steps may include (i) retrieving any necessary information, (ii) calculating, determining, generating, classifying, or otherwise creating any data, and (iii) storing any data calculated, determined, generated, classified, or otherwise created. References to storage devices or storage indicate memory or storage units / disks stored as computing devices (such as references). Figure 7 The data structures in the memory 715 or storage device / disk 735 or remote computer 765 of the computing device 705 shown and described.

[0058] In one embodiment, subsequent steps of the method may begin in response to parsing a received signal or retrieving stored data indicating that the previous step has been performed at least to the extent necessary for the subsequent steps to begin. Generally, a received signal or retrieved stored data indicates the completion of a previous step. Each step of the method may include multiple sub-steps, which may or may not be described herein.

[0059] In one embodiment, the steps of the method described herein are performed by an EMI fingerprint counterfeit scanner 100 (as referenced). Figure 1 and Figure 2 The method described herein is performed as shown and described. In one embodiment, the EMI fingerprint counterfeit scanner 100 is a dedicated computing device (such as computing device 700) configured with high-sensitivity EMI Kiviat tube counterfeit component detection and identification logic. In one embodiment, the steps of the method described herein are performed by the EMI fingerprint counterfeit scanner 100 in conjunction with a remote system (such as cloud application infrastructure 225) configured with server-side high-sensitivity EMI Kiviat tube counterfeit component detection and identification logic 230 and / or counterfeit analysis logic 233. In one embodiment, the steps of the method are performed by a dedicated computing system having at least one processor and configured to perform the described steps.

[0060] Figure 3An embodiment of method 300 is illustrated, which is associated with the high-sensitivity detection and identification of counterfeit components in a utility power system using EMI frequency kiviat tubes. Method 300 is a method for detecting the counterfeit state of a target utility device (e.g., the state of being genuine, or the state of containing at least one counterfeit component).

[0061] Method 300 can be initiated based on various triggers, such as receiving a signal over a network or parsing stored data indicating that: (i) the user (or administrator) of the EMI fingerprint scanner 100 has initiated method 300, for example by providing a signal indicating that a scan should be initiated using the EMI fingerprint scanner 100; (ii) method 300 is scheduled to be initiated at a defined time or time interval; (iii) the target utility equipment is in place and ready to be scanned; or (iv) some other trigger indicating that method 300 should begin. In response to determining that the received parsed signal or retrieved stored data indicates that method 300 should begin, method 300 is initiated at start block 305. Processing continues to process block 310.

[0062] At process block 310, the system selects a set of frequencies that reflect the load dynamics of a reference utility device undergoing a power test sequence. In one embodiment, the set of frequencies is predetermined and stored in a database of EMI-KT fingerprints, such as a database stored in local data storage device 150 or data storage device 235. In one embodiment, the selection of specific frequencies in the set can be performed through preprocessing to generate a reference EMI-KT fingerprint for a genuine (confirmed authentic, "gold system") reference device undergoing the test sequence, and the resulting fingerprint (including the set of frequencies) can be stored in the database. In one embodiment, the set of frequencies is selected by requesting the reference EMI-KT fingerprint from the database and resolving the reference EMI-KT fingerprint in response to receiving it from the database to identify the set of frequencies. In one embodiment, the determined set of frequencies is selected as part of the process for creating the EMI-KT fingerprint for the reference device.

[0063] In one embodiment, a reference EMI-KT fingerprint is created. The system collects reference EMI signals emitted by the reference utility as it undergoes a power test sequence. The reference EMI signals are received by an antenna (such as antenna 115) and processed by a radio transceiver (such as radio transceiver 120). The collected reference EMI signals are stored in a local data storage device 150 or a data repository 235 for further processing. The system transforms the reference EMI signals from the time domain to the frequency domain, for example by performing a Fast Fourier Transform (FFT) or other suitable transform on the collected reference EMI signals. The system divides or partitions the frequency range associated with the collected reference EMI signals into multiple “intervals” and represents each discrete interval with a representative frequency value. For example, the entire frequency range sensed by antenna 115 and radio transceiver 120 (such as a range from approximately 500 kHz to approximately 4 GHz) can be divided into, for example, 100 intervals. In one embodiment, these frequency intervals and associated representative frequency values ​​are equally spaced. In one example, the representative frequency value is the frequency value in the middle of the range of the interval, equidistant from the upper and lower frequency limits of the interval. In one embodiment, the intervals and the representative frequencies are stored in a local data storage device 150 or a data repository 235. The system then selects intervals from those intervals that have representative frequencies reflecting the load dynamics on a reference utility device caused by the power test sequence, to form a set of frequencies from the representative frequencies of those intervals.

[0064] In one embodiment, the set of frequencies can be the N frequencies—the “top” N frequencies—that best represent the most significant dynamics caused by the power test sequence among all reference frequencies. For example, the system can select a subset of N representative frequency values ​​associated with the strongest power spectral density peak. Signals with the highest signal-to-noise ratio typically have the highest peak on the power spectral density (PSD) plot. In one embodiment, a transformation such as a Fast Fourier Transform (FFT) is performed on the amplitude-time series of each representative frequency. The representative frequencies are then ranked according to the order of the transformation results, thus ranking them according to the peak height. The N representative frequencies with the N highest peaks are selected. The system then sets these N frequencies as a set of frequencies based on power spectral density frequency analysis.

[0065] Note that while any number of N frequencies can be chosen to reflect load dynamics, in one embodiment, the first 9 frequencies (N=9) are chosen because 9 frequencies allow for a wide range of representative frequencies while maintaining a relatively small number of vertices used to create the kiviat graph. As the number of frequencies chosen increases, the return on information diminishes, while the complexity of the kiviat graph of the frequency data and the computational load involved in the operations on them increase. N=9 frequencies work well in practice. N=20 intervals are also satisfactory and work well in practice, but result in a visually dense kiviat graph when displayed in a graphical user interface.

[0066] In one example of generating a reference EMI-KT fingerprint, power amplitude values ​​at N frequencies at regular time intervals (observations) over the duration of the test sequence are recorded, for example, as a series of tuples (t, value_F1, ..., value_F...). N For example, as an array structure in a database. In one embodiment, a sequence of tuples forms a reference EMI-KT fingerprint. In one embodiment, a sequence of graphs of tuples on a kiviat graph having axes for each of N frequencies forms a reference EMI-KT fingerprint. In one embodiment, the axes of the kiviat graph in the EMI-KT fingerprint are measured in decibels (dB).

[0067] In one embodiment, the N representative frequencies with the highest power amplitudes are selected in ascending order of frequency value, such that the lowest representative frequency is selected as frequency F1, the next lowest representative frequency is selected as frequency F2, and so on, until the highest representative frequency is selected as frequency F. N In one embodiment, instead, the top N representative frequencies of power amplitude are selected in descending order of frequency value. Note that in either case, the power amplitude indicates which representative frequency will be selected, while the frequency value indicates the order in which the selected frequencies are assigned. This is to maintain visual clarity in the visualization of the kiviat plot—the axes of the kiviat plot can be labeled with their respective frequencies, and arranging them in ascending or descending order is visually meaningful.

[0068] Once the system has thus completed selecting a set of frequencies that reflect the load dynamics of the reference utility equipment during the power test sequence, the processing at process block 310 is then complete, and the processing continues to process block 315.

[0069] At process block 315, the system obtains the target electromagnetic interference (EMI) signal emitted by the target utility equipment during a power test sequence. In one embodiment, the power test sequence is performed on the target utility equipment to cause the target utility equipment (such as device 130) to emit an EMI signal (such as EMI signal 110). In one embodiment, the power test sequence performed on the target equipment at process block 315 is the same as the power test sequence initially performed on the reference equipment to generate a reference EMI-KT fingerprint. In other words, the same test sequence is used to generate both the reference EMI-KT fingerprint and the target EMI-KT fingerprint.

[0070] The target EMI signal is received by an antenna (such as antenna 115) and processed by a radio transceiver (such as radio transceiver 120). The collected target EMI signal is stored in a local data storage device 150 or a data repository 235 for further processing. In one embodiment, the signal is stored as a tuple (t, f, p) of time, frequency, and power amplitude values. In one embodiment, the signal is stored in a planar file dataset having columns for frequency and rows for observation (time), as well as the power amplitude value at each row and column entry. In one embodiment, the system converts the target EMI signal from the time domain to the frequency domain, for example by performing a Fast Fourier Transform (FFT) or other suitable transform on the collected target EMI signal. In one embodiment, the observation rate may be one observation per second, but higher and lower rates may be selected based on the transition speed in the test sequence. In one embodiment, EMI signals across the entire frequency range sensed by antenna 115 and radio transceiver 120 (such as a range from approximately 500 kHz to approximately 4 GHz) are stored. In one embodiment, EMI signals received only for N frequencies in a set reflecting the load dynamics of a reference utility device are stored. Therefore, in one embodiment, the EMI “noise” signal emitted from the utility equipment is processed into digitized multivariate time series data.

[0071] In one embodiment, the collection of the target EMI signal is performed as part of the acquisition step. In another embodiment, the collection of the target EMI signal from the target utility equipment is performed prior to the acquisition step and stored in, for example, a local data storage device 150 or a data repository 235. To acquire the target EMI signal, the stored target EMI signal is then requested and retrieved from the local data storage device 150 or the data repository 235.

[0072] Once the system has thus completed obtaining the target EMI signal emitted by the target utility equipment during the power test sequence, the processing at process block 315 is then complete, and the processing continues to process block 320.

[0073] At process block 320, the system creates a sequence of target kiviat maps based on the amplitude of the target EMI signal at each frequency in the set of observed frequencies during the power test sequence, to form a target kiviat tube EMI fingerprint (EMI-KT fingerprint). In one embodiment, to generate the sequence of target kiviat maps, raw (observed) power amplitude values ​​for N frequencies of the target EMI signal are recorded at regular time intervals (observations) during the test sequence. In one embodiment, the time intervals applied to the target EMI signal should be aligned or synchronized with the time intervals applied to the reference EMI signal to ensure synchronization between the reference waveform generated by the test sequence and the target waveform.

[0074] Similar to the generation of the EMI-KT fingerprint mentioned above, the unprocessed (observed) power amplitude value at each frequency in a set of N frequencies can be recorded as a series of tuples (t, value_F1, ..., value_F). N For example, as an array structure in a database (such as one that can be maintained in a local data storage device 150 or a data repository 235). In one embodiment, a sequence of tuples forms a target EMI-KT fingerprint. In one embodiment, a sequence of tuples on a kiviat graph having axes for each of N frequencies forms a target EMI-KT fingerprint. The fingerprint is stored, for example, in a local data storage device 150 or a data repository 235 for further processing.

[0075] In one embodiment, the creation step described in reference process block 320 further includes generating amplitude estimates at each of the N frequencies in a set of frequencies by training a state estimation model on the EMI fingerprint of the reference kiviat tube. The resulting target kiviat map is an estimated kiviat map, not a map of unprocessed (observed) power amplitude values. In one embodiment, the creation step includes generating a state estimation model of the “real” behavior of the utility equipment (such as an MSET model, an MSET 2 model, or other models generated by a nonparametric pattern recognition algorithm) for example by training an MSET model with a reference kiviat map of the reference kiviat tube. While using MSET for pattern recognition purposes is advantageous, the disclosed embodiments generally use any nonlinear, nonparametric (NLNP) regression, including neural networks, support vector machines (SVMs), autocorrelation kernel regression (AAKR), and even simple linear regression (LR). In one embodiment, the MSET model is further trained with additional amplitude-time series information for frequencies other than those belonging to the selected set of frequencies, such as other frequencies in the interval to which the selected frequencies also belong. For each observation, the raw (observed) target power amplitude value at each of the N frequencies is fed into the trained MSET model to generate an estimated target power amplitude value for each of those N frequencies. The estimated target amplitude value for each frequency is stored as an amplitude value in the target kviat plot for that observation, replacing the raw (observed) target amplitude value. Therefore, the resulting kviat plot is a series of kviat plots of MSET estimates for the power amplitude at each of the N frequencies, rather than a series of kviat plots of the raw (observed) power amplitude at each of the N frequencies. This raw-to-estimate replacement process has a smoothing effect, used to eliminate noise from the target EMI-KT fingerprint.

[0076] Once the system has thus completed the sequence of creating a target kiviat map from the amplitude of the target EMI signal at each frequency in the set of frequencies observed during the power test sequence to form the EMI fingerprint of the target kiviat tube, process block 320 is then complete, and processing continues to process block 325.

[0077] At process block 325, the system compares the EMI fingerprint of the target kiviat tube with the EMI fingerprint of the reference kiviat tube used to undergo the power test sequence to determine whether the target utility and the reference utility are of the same type.

[0078] In one embodiment, the reference kiviat tube EMI fingerprint for a reference utility device is retrieved from a database (e.g., it may be maintained in a local data storage device 150 or a data repository 235). In one embodiment, the database is maintained remotely by, for example, cloud application infrastructure 225 according to server-side logic 230, and a REST request for the transfer of the reference EMI-KT fingerprint is constituted by the EMI fingerprint counterfeit scanner 100 and sent to the web interface server 220 via network interface 160. In another embodiment, the database is maintained locally according to logic 155 in the local data storage device 150, and a request to retrieve the reference EMI-KT fingerprint is constituted by the EMI fingerprint counterfeit scanner 100.

[0079] In one embodiment, the target kiviat map values ​​and reference kiviat map values ​​at each observation on the target and reference kiviat tubes are plotted on the same kiviat map. In one embodiment, a magnitude of the area between the target and reference maps at each observation is calculated and added to the cumulative sum of this magnitude at each observation (i.e., the area is integrated along the time (observation) axis). The cumulative sum across all time observations is evaluated to determine if it meets a threshold test. Thus, this comparison can generate an error metric from the annular residuals between the target and reference kiviat maps at the same observations in the EMI fingerprint of the reference kiviat tube, where the error metric is the cumulative area of ​​the annular residuals between the reference and target kiviat maps across all observations. Generally, since consistency between the target and reference maps indicates that the target and reference devices perform similarly in response to the same test sequence, an error metric (cumulative area) value below the threshold will indicate that the target and reference devices belong to the same type. Similarly, since the difference between the target map and the reference map indicates that the target device and the reference device behave differently in response to the same test sequence, an error metric (cumulative area) value above the threshold will indicate that the target device and the reference device are different types.

[0080] Therefore, in one embodiment, for each observation in the kiviat tube, the residual area between the EMI signal strength kiviat plots at optimally selected representative frequencies for the reference (gold system) and the target (cell under test) is integrated over time to produce the residual volume of the kiviat tube. The residual volume for the target EMI-KT fingerprint can be used as a prognostic metric to determine whether the target EMI-KT fingerprint represents a genuine or questionable utility device by comparison with a threshold.

[0081] In one embodiment, the threshold is a pass-fail quantity value generated by comparing a reference EMI-KT fingerprint with a target EMI-KT fingerprint taken from one or more other utility devices with the same configuration as the reference utility device, or with an EMI-KT fingerprint repeatedly obtained from the same reference device. The threshold is set so that the error metric for each of these comparisons is included within the quantity value required to satisfy the threshold test. In one embodiment, the maximum error metric value among multiple fingerprint comparisons can be set as the threshold, where error metric values ​​exceeding the threshold do not satisfy the threshold test. In one embodiment, a small additional margin (such as 5% or 10% of that maximum error metric) is added to the maximum error metric, and the sum of the results can be set as the threshold.

[0082] In one embodiment, the comparison further includes normalizing each axis of each target kiviat plot relative to a unit circle, which is the value plotted on that axis in the corresponding reference kiviat plot at the same observation point in the EMI fingerprint of the reference kiviat tube. Therefore, each of the N frequency axes of the target kiviat plot is adjusted such that the unit circle passes through each of the N power amplitude values ​​of the reference kiviat plot for the same time. For example, in a 3-frequency target plot, where the power amplitude of the reference plot for the same time is 20 dB at frequency 1, 30 dB at frequency 2, and 10 dB at frequency 3, the axes of the target plot are adjusted or normalized such that each of these reference power amplitude values ​​lies on the unit circle, and the amplitude values ​​of the target plot are plotted on these normalized axes.

[0083] In one embodiment, a normalized target kiviat map for each observation is compared to the unit circle. In another embodiment, a magnitude of the area between the target kiviat map and the unit circle on the normalized axis is calculated and added to the cumulative sum of this magnitude for each observation (i.e., the area is integrated along the time (observation) axis). This area integrated across each observation (time point) during the duration of the test sequence can be referred to as the cumulative cylindrical error measure (CCEM). Therefore, the CCEM is a measure of the difference between the EMI-KT fingerprint of the reference (gold system) and the EMI-KT fingerprint of the target (test unit). The CCEM is evaluated to determine whether it meets a threshold test. As mentioned above, consistency between the target map and the unit circle indicates that the target device and the reference device behave similarly in response to the same test sequence, and a CCEM value below the threshold indicates that the target device and the reference device belong to the same type. And as mentioned above, difference between the target map and the unit circle indicates that the target device and the reference device behave differently in response to the same test sequence, and a CCEM value above the threshold indicates that the target device and the reference device belong to different types.

[0084] Therefore, the comparison also includes generating an error metric from the annular residual between the target Kiviat image and the unit circle on the axis of the reference Kiviat image, which is normalized to represent the same observation in the reference Kiviat EMI fingerprint as a unit circle. The error metric is the cumulative area of ​​the annular residual between the unit circle and the target Kiviat image across all observations. Thus, a cumulative cylindrical error metric is generated for all observations across both the target and reference Kiviat EMI fingerprints.

[0085] The use of the CCEM metric enables the detection of anomalies and true components in utility equipment operating at all different power levels by normalizing decibel values ​​at N reference frequencies at each observation. Therefore, the threshold does not need to be defined based on the configuration of the reference utility equipment, but can be based on deviations from the unit circle. A general threshold test for CCEM can then be applied to all utility equipment, regardless of the power level. In one embodiment, a threshold of approximately 10 works well in practice. In another embodiment, a threshold between 10 and 30 works well in practice.

[0086] In one embodiment, this comparison of the target kiviat tube's EMI fingerprint with that of a reference kiviat tube can be performed locally, for example on an EMI fingerprint counterfeit scanner 100 that implements high-sensitivity EMI kiviat tube counterfeit component detection and identification logic 155. In another embodiment, this comparison can be performed remotely in response to a request (such as a REST request) and by the EMI fingerprint counterfeit scanner 100 transmitting the target kiviat tube to a remote system (such as cloud application infrastructure 225 that implements server-side high-sensitivity EMI kiviat tube counterfeit component detection and identification logic 230).

[0087] Once the system has completed comparing the EMI fingerprint of the target kiviat tube with the EMI fingerprint of the reference kiviat tube of the reference utility device that has undergone a power test sequence to determine whether the target utility device and the reference utility device belong to the same type, the processing at process block 325 is complete, and the processing continues to process block 330.

[0088] At process block 330, the system generates a signal indicating a counterfeit status, at least in part, based on the comparison result. In one embodiment, the system determines the reference type of a reference EMI-KT fingerprint. Note that method 300 can be used to identify a target utility device as either genuine or suspected counterfeit, or it can be used to identify the target utility device as either a confirmed known type of counterfeit or an unknown type of device by comparing the target device with reference EMI-KT fingerprints for known configurations of certified genuine utility devices or for counterfeit devices, respectively. Therefore, the reference type of the reference EMI-KT fingerprint can be either a "genuine device" or a "known counterfeit" reference EMI-KT fingerprint. The system then composes a signal indicating the result of a threshold test for an error metric within the context of the reference type of the reference EMI-KT fingerprint.

[0089] For example, if the reference utility is a genuine utility of a specific type, in response to determining (through a threshold test) that the target utility and the reference utility are (i) of the same type (threshold test satisfied), the system can generate a signal indicating that the target utility is genuine; and (ii) are not of the same type (threshold test satisfied), the system can generate a signal indicating that the target utility is a suspected counterfeit. Alternatively, for example, if the reference utility is a known counterfeit utility of a specific type, in response to determining (through a threshold test) that the target utility and the reference utility are (i) of the same type (threshold test satisfied), the system can generate a signal indicating that the target utility is a counterfeit of the specific type; and (ii) are not of the same type (threshold test not satisfied), the system can generate a signal indicating that the target utility is not a counterfeit of the specific type.

[0090] In one embodiment, the counterfeit status can be displayed as a visual alert presented on a graphical user interface, such as a reference. Figure 4 The evidence of authenticity shown and described in 465, and references Figure 5 Counterfeit alert 565 is shown and described.

[0091] In one embodiment, the system simply generates a signal indicating the result of a threshold test that measures the error. The signal is then stored in a local storage device or transmitted to a display device or a remote system for future use.

[0092] Once the system has thus completed generating a signal indicating the counterfeit status based at least in part on the comparison results, the processing at process block 330 is then complete, and the processing continues to end block 335, where process 300 ends.

[0093] —Counterfeit Analysis System—

[0094] In one embodiment, the result indication of the counterfeit status from method 300 can be further enhanced by a counterfeit analysis system, such as cloud application infrastructure 225 controlled by counterfeit analysis logic 233. For example, in response to a signal that the target utility device is a suspected counterfeit, the system can transmit the target kiviat EMI fingerprint along with the signal that the utility device is a suspected counterfeit to the counterfeit analysis system. For example, the EMI fingerprint counterfeit scanner 100 can compose a request, such as a REST request with an indication that the target kiviat EMI fingerprint is a suspected counterfeit, and transmit the request along with the target kiviat EMI fingerprint from network interface 160 to web interface server 220. In response to receiving the request, counterfeit analysis logic 233 will further process the target kiviat EMI fingerprint and accompanying information.

[0095] In one embodiment, a request transmitted to the counterfeit analysis system may include additional supply chain information about the target device. The supply chain information may include any information available to the system regarding how the target utility device is constructed and how it arrived at the test location. This supply chain information, along with the target EMI-KT fingerprint, may be transmitted to the counterfeit analysis system for storage (in data repository 235), analysis (by counterfeit analysis logic 233), and future reference in scans of other target utility devices (as a reference EMI-KT fingerprint in the database).

[0096] The analysis of the target EMI-KT fingerprint (and supply chain information, if any) by the counterfeit analysis logic 233 can generate additional information that may be useful to one or more users of the system. For example, in response to a signal that the target utility equipment is a suspected counterfeit, and after transmitting a request to the counterfeit analysis system, the system can receive additional information from the counterfeit analysis system regarding the suspected counterfeit configuration, such as one or more of the following: (i) confirmation that the suspected counterfeit is a known type of counterfeit; (ii) generality information describing the prevalence of utility equipment with the suspected counterfeit configuration; (iii) source information describing the origin of the utility equipment with the suspected counterfeit configuration; and / or (iv) supply chain information describing how the target equipment can enter the supply chain. Item (i) can be based on performing method 300 to compare the suspected counterfeit target EMI-KT fingerprint with one or more known counterfeit reference EMI-KT fingerprints until a match is found, or the known counterfeit reference EMI-KT fingerprints available to the counterfeit analysis system are exhausted. Items (ii)-(iv) can be based on analysis of information provided solely by a single tenant of the counterfeit analysis system, or on analysis of information provided across multiple tenants. In one embodiment, in response to receiving additional information, the EMI fingerprint counterfeit scanner 100 (or another computing device (not shown) connected to the cloud application infrastructure 225) can display some or all of the additional information, such as references, on a graphical user interface. Figure 4 and Figure 5 Those shown and described.

[0097] —Graphical user interface for displaying results—

[0098] In one embodiment, the system further displays information, at least in part, based on the signal using a graphical user interface. The displayed information may also be based on a reference EMI-KT fingerprint and a target EMI-KT fingerprint.

[0099] Figure 4 The illustration depicts one embodiment of a graphical user interface (GUI) 400 associated with using an EMI frequency kiviat tube to verify to a user that a target utility device is genuine. In one embodiment, the GUI 400 shows kiviat tube analysis where the target device (unit under test) has all genuine components and the target EMI-KT fingerprint exhibits only a slight deviation from a perfect circle.

[0100] GUI 400 includes a 3D visualization of the example reference (gold system) Kiviat tube 405 and a 3D visualization of the example target (cell under test) Kiviat tube 410. GUI 400 also includes instantaneous 2D Kiviat multivariate prognostic health profile visualization, which includes a 2D visualization of an example reference Kiviat plot 415 (reference (gold sample) Kiviat EMI-realism prediction) from the example reference Kiviat tube 405, and a 2D visualization of an example target Kiviat plot 420 (target (cell under test) Kiviat EMI-realism prediction) from the example target Kiviat tube 410. The example Kiviat plots of the example Kiviat tubes are configured to represent power amplitudes at discrete observations along the time axis of the example Kiviat tube at N = 9 frequencies F1, F2, F3, F4, F5, F6, F7, F8, and F9. The position of example reference Kiviat figure 415 within example reference Kiviat tube 405 is shown by the bold reference Kiviat figure 425 at observation t=5. The position of example target Kiviat figure 420 within example target Kiviat tube 410 is shown by the bold target Kiviat figure 430 at observation t=5. The power amplitude value at observation t=5 for each frequency axis along example reference Kiviat figure 415 is shown by the vertex of reference region 435 at the intersection with the axis of example reference Kiviat figure 415. The power amplitude value at observation t=5 for each frequency axis along example target Kiviat figure 420 is shown by the vertex of target region 440 at the intersection with the axis of example reference Kiviat figure 420.

[0101] In one embodiment, the GUI 400 also includes an anomaly detector kiviat graph 445. The anomaly detector kiviat graph illustrates a normalized unit circle 450, shown by dashed lines, and a normalized target profile 455 of the target region 440, shown by solid lines. As discussed in reference process block 325 above, the axes of the anomaly detector kiviat graph 445 are adjusted or normalized such that the power amplitude value (the vertex of the reference region 435) along each axis of the example reference kiviat graph 415 falls at the intersection of the unit circle 450 and the corresponding axis. Therefore, the target region 440 is normalized to the unit circle 450 in the anomaly detector kiviat graph 445, as shown by the normalized target profile 455. Note that the normalized target profile 455 and the unit circle 450 are highly consistent, indicating that the target device and the reference device perform similarly across all frequencies at the same time observations in the test sequence. The area (in magnitude or absolute value) of the annular residual between the unit circle for each observation and the normalized target profile for each observation is calculated. The area of ​​the annular residual is integrated from the initial observation (t=0) to the current observation (in the example shown in the figure, the current observation is t=5 at 425, 430) to determine the cumulative cylindrical error metric (CCEM) 460 at the current observation. For a pair of reference kiviat tubes and a target kiviat tube with M total observations, the CCEM will cumulatively increase as the observations progress from observation t=0 to observation t=M. The final CCEM at t=M is ​​the area of ​​the annular residual integrated over all M observations. In one embodiment, while the CCEM continues to satisfy a threshold test, indicating that the example reference kiviat tube 405 and the example target kiviat tube 410 are for a target device with a similar configuration, the GUI 400 receives a signal to display a proof of authenticity 465. In one embodiment, the continued display of the proof of authenticity 465 at observation t=M by the final CCEM indicates that the target device has been proven to be genuine, authentic, or otherwise free of counterfeit components. In one embodiment, the proof of authenticity 465 is not presented on the GUI 400 until observation t=M is ​​reached. In one embodiment, the authenticity verification 465 may take the form of a large green icon indicating that the target utility device is “genuine,” “authentic,” “verified,” or other language indicating that the target EMI-KT fingerprint for the target utility device matches a reference EMI-KT fingerprint for a genuine item.

[0102] In one embodiment, the GUI 400 is configured to display a reference kiviat plot and a target kiviat plot in an animated sequence at each observation position along the kiviat tube in response to a user command to display an animated sequence. For a pair of reference kiviat tubes and a target kiviat tube for M total observations, the GUI 400 sequentially displays each pair of reference kiviat plots and target kiviat plots as the observation progresses from observation t=0 to observation t=M. Since each pair of reference kiviat plots and target kiviat plots is displayed sequentially, the corresponding positions of the kiviat plots within the reference kiviat tube and target kiviat tube are highlighted, as shown in reference bold kiviat plots 425 and 430. This highlighting can be achieved, for example, by changing the color, changing the transparency, changing the size or shape, changing the border thickness, or changing the border line. In one embodiment, the GUI 400 is configured to step forward or backward through the observation in response to a user command to step forward or backward. In response to a corresponding forward or backward step command, GUI 400 (i) displays a subsequent or preceding pair of reference kiviat plots and a target kiviat plot in the health contour visualization, and (ii) removes the highlight from the current observation in the example kiviat tube and highlights the location of the subsequent or preceding pair of reference kiviat plots and the target kiviat plot. In one embodiment, GUI 400 is configured to jump directly to that observation in response to a user command that selects a selected observation within the example kiviat tube. In response to the jump command, the pair of reference kiviat plots and the target kiviat plot at the selected observation is displayed, and the highlight in the kiviat tube shifts to the selected pair. In one embodiment, user input may include keystroke or mouse click input.

[0103] Figure 5 The illustration depicts one embodiment of a graphical user interface 500 associated with warning a user about counterfeit components in a target utility device using an EMI frequency kiviat tube. In one embodiment, the GUI 500 displays kiviat tube analysis where the target device (unit under test) has at least one counterfeit component. Here, the target EMI-KT fingerprint exhibits a significant deviation from a perfect circle.

[0104] GUI 500 includes a 3D visualization of the example reference (gold system) kiviat tube 405 and a 3D visualization of the example suspicious target (test unit) kiviat tube 510 for potential counterfeit target devices. GUI 500 also includes transient 2D kiviat multivariate prognostic health profile visualization, which includes a 2D visualization of the example reference kiviat plot 415 (reference (gold sample) kiviat EMI-true prediction) from the example reference kiviat tube 405, and a 2D visualization of the example suspicious target kiviat plot 520 (target (test unit) kiviat EMI-true prediction) from the example suspicious target kiviat tube 510. The position of the example suspicious target kiviat plot 520 within the example suspicious target kiviat tube 510 is shown by the bold target kiviat plot 530 at observation t=5. The power amplitude values ​​observed at t=5 along each frequency axis of the example suspected target kiviat figure 520 are shown by the vertex of the target region 440 at the intersection with the axis of the example reference kiviat figure 520.

[0105] In one embodiment, the GUI 500 also includes an anomaly detector kiviat plot 545. The anomaly detector kiviat plot illustrates a normalized unit circle 450, shown by dashed lines, and a normalized suspicious target profile 555 of the target region 540, shown by solid lines. As discussed above with reference to the anomaly detector kiviat plot 445 and processing block 325, the suspicious target region 540 is normalized relative to the unit circle 450 in the anomaly detector kiviat plot 545, as shown by the normalized target profile 555. Note that the normalized suspicious target profile 555 and the unit circle 450 are divergent, thus indicating that the target device and the reference device behave differently at the same time observation in the test sequence. For each observation, the annular residual area between the unit circle and the normalized suspicious target profile is integrated from the initial observation (t=0) to the current observation (t=5 as shown) to determine the cumulative cylindrical error metric (CCEM) 560 at the current observation. For a pair of reference kiviat tubes and a target kiviat tube with M total observations, the CCEM will cumulatively increase as the observations progress from observation t=0 to observation t=M. The final CCEM at t=M is ​​the area of ​​the annular residual integrated over all M observations. In one embodiment, once the CCEM fails to meet a threshold test, thus indicating that the example reference kiviat tube 405 and the example suspected target kiviat tube 510 are used for a target device with a different configuration, the GUI 500 receives a signal to display a counterfeit alarm 565. In one embodiment, displaying the counterfeit alarm 565 at any time before the final CCEM at observation t=M indicates that the target device is a potentially counterfeit device suspected of having one or more counterfeit components. In one embodiment, the counterfeit alarm 565 is not presented on the GUI 500 before observation t=M is ​​reached. In one embodiment, the counterfeit alert 565 may take the form of a large red icon, which may be octagonal in shape to suggest a stop sign, indicating that the target utility device is “counterfeit,” “suspicious,” “not verified,” or other language indicating that the target EMI-KT fingerprint used for the target utility device does not match the reference EMI-KT fingerprint used for the genuine product.

[0106] Therefore, a display device (such as display 160) can be configured to present an indication that the target utility device (i) is genuine in response to a signal indicating a counterfeit status of a genuine product, and (ii) is a suspected counterfeit in response to a signal indicating a counterfeit status of a suspected counterfeit product.

[0107] —Example methods shown—

[0108] Figure 6An embodiment of a method 600 associated with a display for highly sensitive detection and identification of counterfeit components in a utility power system using EMI frequency kiviat tubes is illustrated. Method 600 is a method for displaying the counterfeit status of a target utility appliance (e.g., the status of being genuine, or the status of containing at least one counterfeit component).

[0109] Method 600 can be initiated based on various triggers, such as receiving a signal over a network or parsing stored data indicating that: (i) the user (or administrator) of the EMI fingerprint scanner 100 has initiated method 600, for example by providing a signal indicating that a scan should be initiated using the EMI fingerprint scanner 100; (ii) method 600 is scheduled to be initiated at a defined time or time interval; (iii) the target utility equipment is in place and ready to be scanned; or (iv) some other trigger indicating that method 600 should begin. In response to determining that the received parsing signal or retrieved stored data indicates that method 600 should begin, method 600 is initiated at start block 605. Processing continues to process block 610.

[0110] At process block 610, the system selects nine optimal frequencies for the reference device (gold sample) in ascending order. In one embodiment, the selection of the nine optimal frequencies is performed by the system, such as the reference... Figure 3 The process is shown and described in process block 310. The processing at process block 610 is then completed, and processing continues to process block 615. Alternatively, processing may continue to process block 620.

[0111] At process block 615, the system generates a reference device (gold sample) kiviat tube. In one embodiment, the generation of the reference device kiviat tube is performed by the system shown and described in reference process block 310, or in a manner similar to that shown and described in reference process blocks 315 and 320 for the target EMI-KT fingerprint. The processing at process block 615 then completes, and processing continues to decision block 625.

[0112] At process block 620, the system selects the appropriate frequency for the target device (cell under test) in ascending order. In one embodiment, the system selects the same frequency as the one selected for the reference device at process block 610. In another embodiment, the system parses a reference (gold sample) kiviat tube (reference EMI-KT fingerprint) to extract the frequency. The processing at process block 620 is then complete, and processing continues to process block 630.

[0113] At process block 630, the system generates the target device (cell under test) kiviat. In one embodiment, the generation of the target device kiviat is performed by the system in a manner similar to that shown and described in reference process block 310 for the reference EMI-KT fingerprint, or in a manner shown and described in reference process blocks 315 and 320. The processing at process block 630 is then complete, and processing continues to decision block 625.

[0114] At decision block 625, the system determines whether the current observation is less than or equal to the total number of observations used for the kiviat tube. In one embodiment, a loop for comparing and displaying the target and reference EMI-KT fingerprints is initiated. The current observation is initialized to the value of the first observation in the kiviat tube for the test sequence (applicable to both the reference and target kiviat tubes). The initial value is typically 0 or 1, but other values ​​may be appropriate. The total number of observations in the kiviat tube (applicable to both the reference and target kiviat tubes) is identified, for example, by parsing either kiviat tube to extract the total number of observations. If the current observation is less than or equal to the total number of observations (true), then the processing at decision block 625 is complete and the process proceeds to process block 635 if (i) the reference device (gold sample) kiviat map used for this observation has not yet been generated, and (ii) if the reference device kiviat map used for this observation has already been generated, then proceeds directly to process block 640. If the current observation is greater than the total number of observations (false), then the processing at decision box 625 is complete and processing continues to end box 645.

[0115] At process block 635, the system generates a reference device (gold system) kiviat plot. In one embodiment, the system retrieves a tuple from a data structure in memory describing the reference kiviat plot used for the current observation. The system parses the tuple to extract the power amplitude values ​​for each frequency in the plot. The system generates a kiviat plot displaying the extracted power amplitude values ​​on their respective axes. The system stores the generated kiviat plot in memory and / or generates instructions to cause display 165 to show the generated kiviat plot in a GUI such as GUI 400. In one embodiment, if the reference device kiviat plot has been previously generated and stored in memory for immediate retrieval, then process block 635 can be bypassed. The processing at process block 635 is then complete, and processing continues to process block 640.

[0116] At process block 640, the system generates a kiviat plot of the target device (unit under test). In one embodiment, the system retrieves a tuple from a data structure in memory that describes the currently observed target kiviat plot. The system parses the tuple to extract the power amplitude values ​​for each frequency in the plot. The system generates a kiviat plot that displays the extracted power amplitude values ​​on their respective axes. The system stores the generated kiviat plot in memory and / or generates instructions to cause display 165 to display the generated kiviat plot in a GUI such as GUI 400. The processing at process block 640 is then complete, and processing continues to process block 650.

[0117] At process block 650, the system generates a kiviat plot with normalized circles for a reference device (gold sample). In one embodiment, the system retrieves a tuple from a data structure in memory describing the reference kiviat plot for the current observation. The system parses the tuple to extract the reference power amplitude value for each frequency in the plot. The system calculates an adjustment for the amplitude of each axis of the kiviat plot to allow the unit circle used for the current observation to intersect with each axis along the corresponding value at the extracted reference power amplitude value. The system plots the unit circle on the adjusted (normalized) kiviat plot. The system retrieves a tuple from a data structure in memory describing the target kiviat plot for the current observation. The system parses the tuple to extract the target power amplitude value for the plot at each frequency. The system plots the target power amplitude value on the adjusted (normalized) kiviat plot to form a normalized target kiviat plot for the current observation. The system stores the target kiviat graph with normalized unit circles in memory and / or generates instructions to cause display 165 to display the target kiviat graph with normalized unit circles in a GUI such as GUI 400. Then the processing at process block 650 is complete, and processing continues to process block 655.

[0118] At process block 655, the system calculates the area of ​​the annular residual between the target device (test unit) and the reference device (gold sample), integrating along the time axis. In one embodiment, the system calculates the magnitude (absolute value) of the area of ​​the annular residual between the unit circle used for the current observation and the normalized target kiviat plot used for the current observation. To integrate the area along the time axis, the system adds the calculated area used for the current observation to the cumulative sum of the calculated areas used for all previous observations to form the cumulative cylindrical error metric (CCEM) for the current observation. The system stores the CCEM in memory and / or generates instructions to cause display 165 to display the CCEM in a GUI such as GUI 400. The processing at process block 655 is then complete, and processing continues to decision block 660.

[0119] At process block 660, the system determines whether the cumulative cylindrical error metric (CCEM) is less than or equal to a threshold. In one embodiment, determining whether the CCEM used for the current observation satisfies a threshold test is performed as shown and described with reference to process block 325. The result of the threshold test may be stored in memory by the system and / or used by the system to generate a signal indicating the counterfeit status of the target device. If the CCEM is less than or equal to the threshold (true), then the processing at decision block 660 is complete, and the processing continues to process block 665. If the CCEM is greater than the threshold (false), then the processing at decision block 660 is complete, and the processing continues to process block 670.

[0120] At process block 665, the system displays an indication that the target device (unit under test) has been proven to be genuine on the graphical user interface. In one embodiment, the system generates an indication and performs the display of the indication, as shown and described with reference to process block 330. The processing at process block 665 is then complete, and processing continues to process block 675.

[0121] At process block 670, the system displays an alarm in the graphical user interface indicating that the target device (unit under test) may be counterfeited. In one embodiment, the system generates an instruction and executes the display of the instruction, as shown and described with reference to process block 330. The processing at process block 670 then completes, and processing continues to process block 675.

[0122] At process block 675, the system increments the observation count. In one embodiment, the system increments the current observation count by one and stores the incremented observation count in memory for later reference. Note that the observations can be evenly spaced and can be, but do not have to be, standard time increments (such as seconds, milliseconds, or other units). The processing at process block 675 then completes, and processing returns to decision block 625.

[0123] When the current observation count is less than or equal to the total observation count (decision box 625 is true), the process repeats from decision box 625 to process box 675 until the current observation count increments to exceed the total observation count (decision box 625 is false). At this point, the process continues to end box 645, where the process ends.

[0124] Each item generated by method 600 can be stored in memory for later retrieval and display in a GUI such as GUI 400. The generated items can be stored in a local data storage device 150 or remotely in a data repository 235. These items can be retrieved for display in response to input to the GUI, such as references. Figure 4 and Figure 5 As shown and described.

[0125] —Selected Advantages—

[0126] In one embodiment, the methods and systems described herein provide a highly efficient tool for detecting counterfeit utility equipment. When properly employed, the methods and systems described herein are capable of detecting the presence of counterfeit electronic components in utility equipment with nearly 100% accuracy. Additionally, in one embodiment, the tool is easily usable by non-expert personnel. This tool enables the autonomous detection and identification of definite counterfeits so that (i) personnel involved in utility acceptance testing of components in the supply chain, and (ii) personnel involved in inspecting shipping systems at ports of entry and other national and international borders, can quickly identify utility equipment containing internal counterfeit components or verify that utility equipment has all genuine components. These personnel are not required to be experts in EMI radiation, data science, machine learning, or counterfeit detection techniques to derive these advantages from using the systems and methods described herein.

[0127] In one embodiment, the methods and systems described herein also enable law enforcement to identify the exact brand, model, and / or implementation of counterfeit utility equipment with a unique EMI-KT fingerprint, allowing for the tracing of counterfeit utility equipment along the supply chain route to its source. The systems and methods described herein enable the accurate and undisputed identification of internal counterfeit components in utility equipment and the persistent and reliable identification of the accurate supplier-specific lineage of counterfeit products. The association of this data with the EMI-KT fingerprint allows data analytics to accurately (i) assess the prevalence of counterfeit equipment in the supply chain (the scale of a particular counterfeiting problem) and (ii) trace the lineage of counterfeit equipment and components through the supply chain to identify and cut off suppliers of counterfeit components.

[0128] The advantages described herein are achieved by the methods and systems described herein, for example, by the increased sensitivity provided by EMI-KT fingerprinting. The methods and systems described herein have not been performed by humans before, nor can they be performed by humans, and are therefore not a computerization of existing manually implemented processes.

[0129] —Cloud or Enterprise Implementation Examples—

[0130] In one embodiment, cloud application infrastructure 225 and / or other systems shown and described herein are computing / data processing systems, including a collection of applications or distributed applications for an enterprise organization. The application and data processing systems may be configured to operate with or implemented as cloud-based networked systems, Software-as-a-Service (SaaS) architectures, or other types of networked computing solutions. In one embodiment, the cloud computing system is a server-side system that provides one or more of the functions disclosed herein, and a number of users can access the system via a computer network through the EMI fingerprint scanner 100 or other client computing devices communicating with the cloud computing system (acting as a server).

[0131] —Computing Device Examples—

[0132] Figure 7 An example computing device 700 is illustrated, which is configured and / or programmed with one or more and / or equivalents of the example systems and methods described herein. The example computing device may be a computer 705, which includes a processor 710, a memory 715, and an input / output port 720 operably connected via a bus 725. In one example, computer 705 may include high-sensitivity EMI kiviat tube counterfeit component detection and identification logic 730, which is configured to use a similar... Figures 1 to 6 The logic and system illustrated use an EMI frequency kiviat transistor to facilitate highly sensitive detection and identification of counterfeit components in utility power systems. In various examples, logic 730 may be implemented in hardware, a non-transitory computer-readable medium with stored instructions, firmware, and / or a combination thereof. Although logic 730 is shown as a hardware component attached to bus 725, it should be appreciated that in other embodiments, logic 730 may be implemented in processor 710, stored in memory 715, or stored in disk 735.

[0133] In one embodiment, logic 730 or a computer is a component (e.g., structure: hardware, non-transitory computer-readable medium, firmware) for performing the described actions. In some embodiments, the computing device may be a server operating in a cloud computing system, a server configured in a Software as a Service (SaaS) architecture, a smartphone, a laptop computer, a tablet computing device, etc.

[0134] This component can be implemented, for example, as an ASIC programmed for highly sensitive detection and identification of counterfeit components in a utility power system using EMI frequency kiviat tubes. The component can also be implemented as stored computer-executable instructions, which are presented as data 740 to a computer 705, temporarily stored in memory 715, and then executed by a processor 710.

[0135] The Logic 730 can also provide components (e.g., hardware, non-transitory computer-readable media storing executable instructions, firmware) for performing high-sensitivity detection and identification of counterfeit components in utility power systems using EMI frequency kiviat tubes.

[0136] Generally describing an example configuration of computer 705, processor 710 can be a variety of different processors, including dual-microprocessor and other multiprocessor architectures. Memory 715 can include volatile memory and / or non-volatile memory. Non-volatile memory can include, for example, ROM, PROM, etc. Volatile memory can include, for example, RAM, SRAM, DRAM, etc.

[0137] Storage disk 735 can be operatively connected to computer 700 via, for example, an input / output (I / O) interface (e.g., a card, device) 745 and an input / output port 720. Disk 735 can be, for example, a disk drive, solid-state drive, floppy disk drive, tape drive, Zip drive, flash memory card, memory stick, etc. Furthermore, disk 735 can be a CD-ROM drive, CD-R drive, CD-RW drive, DVD ROM, etc. For example, memory 715 can store processes 750 and / or data 740. Disk 735 and / or memory 715 can store an operating system that controls and allocates resources of computer 705.

[0138] Computer 705 can interact with input / output (I / O) devices via I / O interface 745 and input / output port 720. Input / output devices may include, for example, a keyboard 780, microphone 784, pointing and selection device 782, camera 786, video card, monitor 770, scanner 788, printer 772, speaker 774, disk 735, network device 755, etc. Input / output port 720 may include, for example, a serial port, parallel port, and USB port. Input / output devices may include a software-defined radio transceiver 790 and an associated antenna 792.

[0139] Computer 705 can operate in a network environment and therefore can be connected to network device 755 via I / O interface 745 and / or I / O port 720. Through network device 755, computer 705 can interact with network 760. Through network 760, computer 705 can logically connect to remote computer 765. Networks that computer 705 can interact with include, but are not limited to, LANs, WANs, and other networks.

[0140] —Definitions and Other Examples—

[0141] In another embodiment, the described methods and / or their equivalents may be implemented using computer-executable instructions. Thus, in one embodiment, a non-transient computer-readable / storage medium is configured to have stored computer-executable instructions of an algorithm / executable application that, when executed by one or more machines, cause the machines (and / or associated components) to perform the methods. Example machines include, but are not limited to, processors, computers, servers operating in cloud computing systems, servers configured with a Software as a Service (SaaS) architecture, smartphones, and the like. In one embodiment, the computing device is implemented using one or more executable algorithms configured to perform any of the disclosed methods.

[0142] In one or more embodiments, the disclosed methods or their equivalents are performed by any of: computer hardware configured to perform the methods; or, computer instructions embodied in a module stored in a non-transient computer-readable medium, wherein the instructions are configured to execute an algorithm that is configured to perform the methods when executed by at least one processor of a computing device.

[0143] While the methods illustrated in the figures are shown and described as a series of boxes representing the algorithm for illustrative purposes, it should be understood that these methods are not restricted by the order of the boxes. Some boxes may appear in a different order than those shown and described, and / or may appear simultaneously with other boxes. Furthermore, example methods may be implemented using fewer boxes than are shown in all the figures. Boxes may be combined or divided into multiple actions / components. Additionally and / or alternative methods may employ additional actions not illustrated in the boxes.

[0144] The following includes definitions of the selected terms used herein. Definitions include various examples and / or forms of components that fall within the scope of the term and can be used to implement it. Examples are not intended to be restrictive. Both singular and plural forms of the terms may be included within the definitions.

[0145] References to "an embodiment," "an embodiment," "an example," "an example," etc., indicate that one or more embodiments or examples as described may include a particular feature, structure, characteristic, property, element, or limitation, but not every embodiment or example must include that particular feature, structure, characteristic, property, element, or limitation. Furthermore, repeated use of the phrase "in one embodiment" does not necessarily refer to the same embodiment, but may refer to the same embodiment.

[0146] ASIC: Application-Specific Integrated Circuit.

[0147] CD: Optical disc.

[0148] CD-R: CD is recordable.

[0149] CD-RW: CDs are rewritable.

[0150] DVD: Digital multifunction disc and / or digital video disc.

[0151] LAN: Local Area Network.

[0152] RAM: Random Access Memory.

[0153] DRAM: Dynamic RAM.

[0154] SRAM: Synchronous RAM.

[0155] ROM: Read-only memory.

[0156] PROM: Programmable ROM.

[0157] EPROM: Erasable PROM.

[0158] EEPROM: Electrically Erasable Proto-ROM.

[0159] USB: Universal Serial Bus.

[0160] XML: Extensible Markup Language.

[0161] WAN: Wide Area Network.

[0162] As used herein, a “data structure” is an organization of data stored in memory, storage devices, or other computerized systems within a computing system. A data structure can be any of, for example, a data field, a data file, a data array, a data record, a database, a data table, a graph, a tree, a linked list, etc. A data structure can be formed from and contain many other data structures (e.g., a database contains many data records). Other examples of data structures are also possible according to other embodiments.

[0163] As used herein, "computer-readable medium" or "computer storage medium" means a non-transient medium that stores instructions and / or data configured to perform one or more of the disclosed functions when executed. In some embodiments, data may be used as instructions. Computer-readable media may take the form of, but is not limited to, non-volatile and volatile media. Non-volatile media may include, for example, optical discs, magnetic disks, etc. Volatile media may include, for example, semiconductor memory, dynamic memory, etc. Common forms of computer-readable media may include, but are not limited to, floppy disks, flexible disks, hard disks, magnetic tapes, other magnetic media, application-specific integrated circuits (ASICs), programmable logic devices, compact discs (CDs), other optical media, random access memory (RAM), read-only memory (ROM), memory chips or cards, memory sticks, solid-state storage devices (SSDs), flash drives, and other media in which computers, processors, or other electronic devices may operate. If each type of media is selected for implementation in one embodiment, it may include stored instructions of an algorithm configured to perform one or more of the disclosed and / or claimed functions.

[0164] As used herein, “logic” means a component implemented using computer or electrical hardware, a non-transient medium having instructions for executable application or program modules stored therein, and / or a combination thereof, to perform any function or action disclosed herein, and / or to cause a function or action from another logic, method, and / or system to be performed as disclosed herein. Equivalent logic may include firmware, a microprocessor programmed with an algorithm, discrete logic (e.g., an ASIC), at least one circuit, analog circuit, digital circuit, programmable logic device, memory device containing instructions for an algorithm, etc., any of which may be configured to perform one or more of the disclosed functions. In one embodiment, logic may include one or more gates, combinations of gates, or other circuit components capable of performing one or more of the disclosed functions. In the case of describing multiple logics, it is possible to combine multiple logics into one logic. Similarly, in the case of describing a single logic, it is possible to distribute that single logic among multiple logics. In one embodiment, one or more of these logics are corresponding structures associated with performing the disclosed and / or claimed functions. The choice of which type of logic to implement may be based on desired system conditions or specifications. For example, hardware implementation of the function would be chosen if higher speed is considered. If lower cost is a consideration, then stored instructions / executable applications will be chosen to implement the functionality.

[0165] An "operable connection," or a connection through which entities are "operably connected," is a connection capable of sending and / or receiving signals, physical communication, and / or logical communication. An operable connection may include physical interfaces, electrical interfaces, and / or data interfaces. An operable connection may include various combinations of interfaces and / or connections sufficient to allow for operable control. For example, two entities may be operably connected to transmit signals to each other directly or through one or more intermediate entities (e.g., processors, operating systems, logic, non-transient computer-readable media). Logical and / or physical communication channels can be used to create an operable connection.

[0166] As used herein, “user” includes, but is not limited to, one or more persons, computers or other devices, or a combination of these.

[0167] While the disclosed embodiments have been illustrated and described in considerable detail, they are not intended to limit the scope of the appended claims or in any way restrict them to such detail. It is certainly impossible to describe every contemplated combination of components or methods in order to describe all aspects of the subject matter. Therefore, this disclosure is not limited to the specific details or illustrative examples shown and described. Consequently, this disclosure is intended to cover changes, modifications, and variations that fall within the scope of the appended claims.

[0168] As to the extent to which the term “comprising” is used in the specific embodiments or claims, it is intended to be inclusive in a manner similar to that interpreted when the term “comprising” is used as a transitional word in the claims.

[0169] As far as the term “or” is used in the specific embodiments or claims (e.g., A or B), it is intended to mean “A or B or both.” When the applicant intends to indicate “only A or B but not both,” then the phrase “only A or B but not both” will be used. Therefore, the use of the term “or” herein is inclusive, not exclusive.

Claims

1. A method for detecting the counterfeit condition of target utility equipment, the method comprising: Apply a power test sequence to the target device; Multiple electromagnetic interference (EMI) signals emitted by the target utility equipment were obtained during the power test sequence; An EMI fingerprint of the target kiviat tube is created based on at least multiple obtained EMI signals. The EMI fingerprint of the target kiviat tube is compared with the EMI fingerprint of a reference kiviat tube from a reference utility device that has undergone a power test sequence to determine whether the target and reference utility devices belong to the same type. The comparison also includes generating an error metric from the ring residuals between the target and reference kiviat images at the same observations in the reference kiviat tube EMI fingerprint. This error metric is the cumulative area of ​​the ring residuals between the reference and target kiviat images across all observations. A signal is generated, at least in part, to indicate the counterfeit status, based on the fact that the error metric does not meet a threshold test.

2. The method of claim 1, wherein the creation further comprises: In response to a power test sequence, a set of EMI frequencies is selected, wherein the plurality of EMI signals are obtained from the set of EMI frequencies; An amplitude estimate at each frequency in the set of frequencies is generated by a state estimation model trained on the EMI fingerprint of a reference kiviat tube, where the target kiviat map is the estimated kiviat map.

3. The method of claim 1, wherein the comparison further comprises normalizing each axis of each target kiviat plot relative to a unit circle, the unit circle being a value plotted on that axis of the corresponding reference kiviat plot at the same observation point in the reference kiviat tube EMI fingerprint.

4. The method of claim 1, wherein the comparison further comprises generating an error metric from the annular residual between the target kiviat map and the unit circle on an axis normalized to represent the same observation in the reference kiviat tube EMI fingerprint as a unit circle, wherein the error metric is the cumulative area of ​​the annular residual between the unit circle and the target kiviat map over all observations.

5. The method of claim 1, wherein the reference utility is a genuine utility of a specific type, further comprising, in response to determining that the target utility and the reference utility are (i) of the same type, generating a signal to indicate that the target utility is identified as genuine, and (ii) are not of the same type, generating a signal to indicate that the target utility is a suspected counterfeit.

6. The method of claim 5, further comprising, in response to a signal that the target utility equipment is a suspected counterfeit, transmitting the target kiviat EMI fingerprint to a counterfeit analysis system.

7. The method of claim 6 further includes transmitting supply chain information about the target device to a counterfeit analysis system.

8. The method of claim 5, further comprising, in response to a signal that the target utility equipment is a suspected counterfeit, receiving additional information about the suspected counterfeit configuration from a counterfeit analysis system, wherein the additional information includes one or more of the following: (i) Confirmation that a suspected counterfeit is a known type of counterfeit. (ii) General information describing the prevalence of utility equipment with suspected counterfeit configurations. (iii) Describe the source information of utility equipment with suspected counterfeit configurations. (iv) Supply chain information describing how the target equipment can enter the supply chain.

9. The method of claim 1, wherein the reference utility is a known counterfeit utility of a specific type, further comprising, in response to determining that the target utility and the reference utility are (i) of the same type, generating a signal to indicate that the target utility is identified as a counterfeit utility of the specific type, and (ii) are not of the same type, generating a signal to indicate that the target utility is not a counterfeit utility of the specific type.

10. The method of claim 1, further comprising retrieving a reference kiviat tube EMI fingerprint from a database for reference to utility equipment.

11. The method of claim 1, further comprising displaying information using a graphical user interface based at least in part on signals.

12. A non-transitory computer-readable medium storing computer-executable instructions, which, when executed by at least one processor of a computer, cause the computer to: When undergoing a power test sequence, select a set of frequencies that reflect the load dynamics of the reference utility equipment; Target electromagnetic interference (EMI) signals emitted by the target utility equipment were obtained during a power test sequence. A sequence of target kiviat maps is created based on the amplitude of the target EMI signal at each frequency in a set of frequencies observed during a power test sequence to form a target kiviat tube EMI fingerprint. The EMI fingerprint of the target kiviat tube is compared with the EMI fingerprint of a reference kiviat tube from a reference utility device that has undergone a power test sequence to determine whether the target and reference utility devices belong to the same type. The comparison also includes generating an error metric from the ring residuals between the target and reference kiviat images at the same observations in the reference kiviat tube EMI fingerprint. This error metric is the cumulative area of ​​the ring residuals between the reference and target kiviat images across all observations. A signal is generated, at least in part, to indicate the counterfeit status, based on the fact that the error metric does not meet a threshold test.

13. The non-transitory computer-readable medium of claim 12, wherein the instructions for causing the computer to create the sequence of target kiviat graphs further include instructions for causing the computer to generate estimates of amplitude at each frequency of the set of frequencies by means of a state estimation model trained on the EMI fingerprint of a reference kiviat tube, wherein the target kiviat graph is the estimated kiviat graph.

14. A computing system, comprising: processor; Memory, operatively connected to the processor; A radio transceiver operatively connected to a processor and memory; A non-transitory computer-readable medium operatively connected to a processor and memory and storing computer-executable instructions that, when executed by at least the processor, enable the computing system to: When undergoing a power test sequence, select a set of frequencies that reflect the load dynamics of the reference utility equipment; Target electromagnetic interference (EMI) signals emitted by the target utility equipment are obtained by using a radio transceiver device while undergoing a power test sequence; A sequence of target kiviat maps is created based on the amplitude of the target EMI signal at each frequency in a set of frequencies observed during a power test sequence to form a target kiviat tube EMI fingerprint. The EMI fingerprint of the target kiviat tube is compared with the EMI fingerprint of the reference kiviat tube of a reference utility device that has undergone a power test sequence to determine whether the target utility device and the reference utility device belong to the same type. The comparison also includes generating an error metric from the ring residuals between the target kiviat plot and the reference kiviat plot when the same observations are made in the EMI fingerprint of the reference kiviat tube. The error metric is the cumulative area of ​​the ring residuals between the reference kiviat plot and the target kiviat plot on all observations. as well as A signal is generated, at least in part, to indicate the counterfeit status, based on the fact that the error metric does not meet a threshold test.

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