Patent ID: 12230953

DETAILED DESCRIPTION

The present disclosure relates to electrical fault detection and load control/interrupter devices in, for example, wall box receptacles or breakers, and to arc fault detection and circuit interruption in such devices. Aspects of the present disclosure relate to detecting arc faults using a trained machine learning model and using input data, for the machine learning model, which are computed based on sensor measurements of electrical characteristics (e.g., voltage, current, low/high frequency signals, and the like) of a conductive path in such devices. In various embodiments, the electrical fault detection and load control/interrupter devices disclosed herein can be located in an electrical receptacle, in an electrical panel, or in other electrical systems. Aspects of the present disclosure provide an arc detection program which implements the trained machine learning model in a manner such that only machine learning parameters need to be updated during firmware updates. The firmware updates can provide updated machine learning parameters which are trained based on data gathered from installed AFCI devices in the field and which reduce false positive arc event decisions.

Sensors and sensor measurements or samples are be described below herein. Unless indicated otherwise by the context, the terms sensor measurement and sensor sample may be used interchangeably herein, such that occurrences of the term “sample” may be replaced with the term “measurement,” and vice versa.

FIG.1is a diagram of an exemplary electrical switch apparatus5. Various embodiments of the electrical switch apparatus5include an arc fault circuit interrupter (AFCI) that can be disposed inside of an enclosure such as a single-gang enclosure. For convenience, the following description will refer to the electrical switch apparatus5as an AFCI system or device. It will be understood, however, that any description of an AFCI system or device will apply to other types of electrical switch apparatuses as well. The AFCI system5may be sized and arranged to be disposed within a housing configured to be installed in a single gang electrical enclosure, such as a wall-box mounted AFCI. In various embodiments, the AFCI system may be housed or mounted in any suitable form factor such as a circuit breaker, a panel mount device, an in-line device, or another housed or mounted form.

As illustrated inFIG.1, the AFCI device5includes line side connections11and load side connections70. Line side connections11include a first line contact12and a second line contact14. First line contact12is coupled to first conductive path16(i.e., a line side phase conductive path), while second line contact14is coupled to second conductive path18(i.e., a line side neutral conductive path). Contacts12,14can be in the form of terminal screws, wire leads, or other connections that can be coupled to a power line. Load side70may be separated from line input conductive paths11by separable contacts (e.g., a controllably conductive switch)62, which include separable contacts64and66. Contact64is configured to separate first line side conductive path16from a first load side conductive path and contact66is configured to separate second line side conductive path18from a second load side conductive path.

In the illustrated embodiment shown inFIG.1, the first conductive path16is arranged to conduct current in the same direction as the second conductive path18relative to the sensors22and24. It should be readily appreciated by those skilled in the art that when referring to AC current, direction of current flow reverses with a certain frequency/period, e.g., sixty times a second in a standard sixty (60) Hz system. In the description herein, the AC current is described as flowing in a certain direction. When the “direction” of current flow is referred to, it is intended to reflect the “conventional current flow” of the AC circuit as known in the electrical arts.

FIG.1shows one embodiment of a wiring layout110. In various embodiments, another wiring layout may be implemented instead of the wiring layout110shown inFIG.1. It should be readily appreciated by those skilled in the art that any suitable number of conductive paths may be arranged to pass through or near any number of sensors, such as a high frequency sensor22, a current sensor24, and/or a differential current sensor26, which will be described in more detail below. Any of various sensors can be arranged and configured to monitor a single conductive path, e.g., a phase conductive path or a neutral conductive path. In various embodiments, any one of the sensors can be arranged and configured to monitor both the phase and neutral conductive paths.

Any or all of the three sensors22,24,26can be in electrical communication with one or more conductive paths, such as a phase conductive path or a neutral conductive path. As such, the sensors22,24,26can be configured to sense and/or measure electrical characteristics of conductive path(s) in communication therewith. As used herein, the term “electrical communication” may include positioning of any of the sensors in a configuration such as adjacent to the device conductive path, electrically coupled to the device conductive path, magnetically coupled to the device conductive path, or positioned such that the device conductive path passes through a core of the sensor, or in another configuration in which a sensor can sense a property of at least a portion of a conductive path. For example, high frequency sensor22may be configured to sense/measure high frequency signals, such as high frequency noise. Current sensor24may be configured to sense/measure a current value. Differential sensor26may be configured to sense/measure a current differential between, for example, the phase and neutral conductive paths. In various embodiments, sensor22and sensor24may be embodied as a single sensor20.

High frequency sensor22may be any high frequency sensor recognized by persons skilled in the art. In various embodiments, the high frequency sensor22may be a transformer having a coil wound around an air core (e.g.—a Rogowski coil) or a high permeability magnetic core (e.g., an iron powder core where powdered iron is encapsulated in an epoxy substrate). In various embodiments, high frequency sensor22may be configured with a toroidal core. Other types of high frequency sensors are contemplated to be within the scope of the present disclosure.

Current sensor24can be in the form of any low frequency sensor recognized by persons skilled in the art such as a current transformer. In various embodiments, a shunt25may be employed, such as a known resistance which allows the current flowing to be determined by measuring the voltage across the shunt.

In various embodiments, each sensor may be configured to detect signals in a predetermined frequency range. For example, the current24may be configured to detect electrical characteristics of a current path at a predetermined frequency range of, for example, a power line frequency range or, in various embodiments, a larger frequency range such as 0-2 MHz or 0-4 MHz The high frequency sensor22can be configured to detect electrical characteristics of a current path at a predetermined frequency range higher than the low frequency sensor, such as greater than a power line frequency range or, for example, 1-10 MHz, 2-10 MHz, or 4-10 MHz The sensitivity level and frequency range for high frequency sensor22may be governed/set by signal processing circuitry. Other frequency ranges are contemplated to be within the scope of the present disclosure.

In various embodiments, an optional differential sensor26can also be included in addition to the high frequency sensor22and current sensor24and can measure the differential current between the conductive paths. Aspects of the optional differential sensor26are described in U.S. Pat. No. 10,367,347, which was incorporated by reference above. In the illustrated embodiment, differential sensor26is shown in dashed lines to indicate that it may be optionally used or not used in certain embodiments. While sensors20,22,24and26are illustrated in the drawings, some or all of the sensors may not be present in various embodiments of the present disclosure.

InFIG.1, the outputs of sensors22,24, and26are connected to circuit50. Circuit50may be any suitable circuit such as, but not limited to, an analog signal processor (ASP). The analog signal processor circuit50can include any suitable circuit elements such as, but not limited to, amplifiers, rectifiers, comparators (or a combination thereof), or other elements that condition the signal from one or more of sensors22,24, and26for input into processor100. In various embodiments, the analog signal processor circuit50may sample the outputs of the sensors22,24, and26. In various embodiments, one or more of the output signals from sensors22,24, or26may be provided directly to microprocessor100without any analog conditioning.

Microprocessor100can be any suitable type of processor or controller such as a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), microcontroller, application specific integrated circuit (ASIC), field-programmable gate array (FPGA), or another type of processor or controller which can perform computations. Microprocessor100is configured or programmed to analyze output signals provided by one or more of sensors22,24, or26and determine if a predetermined condition exists, such as an arc fault. If microprocessor100detects a predetermined condition, the microprocessor100may be configured or programmed to trigger a controllably conductive switch60to electrically isolate one or more of the phase and neutral conductive paths, thus disconnecting power to load70. The AFCI system includes a power supply40which is configured to provide power to the components of the circuit, including the microprocessor100.

In accordance with aspects of the present disclosure, the microprocessor100can implement a trained machine learning model and can compute input data for the machine learning model based on the signals output from the various sensors, such as the high frequency sensor22and/or the current sensor24, among other sensors. The trained machine learning model will be described in more detail later herein. For now, it is sufficient to note that the machine learning model provides a decision about whether or not there has been an arc fault. If the machine learning model decides there has been an arc fault, the microprocessor100can trigger the controllably conductive switch60to interrupt power to one or more of the phase and neutral conductive paths, thereby disconnecting power to load70.

The microprocessor100can be coupled to one or more indicators104,106, communication circuitry107, and a test circuit30. A visual indicator104and/or an audio indicator106can be provided. The indicators104and106can be any suitable type of indicators such as a LED, light, neon, buzzer, or piezoelectric element. The microprocessor100may be configured or programmed to energize test circuit30to initiate a test sequence on the device. Aspects of the test functionality are described in U.S. Pat. No. 10,367,347, which was incorporated by referenced above.

The communication circuitry107can include a transceiver that enables communication with other devices. In various embodiments, the communication circuitry107may enable Wi-Fi communications and/or Bluetooth communications, or another type of communications (e.g., wired or wireless). In various embodiments, communication circuitry107can communicate any suitable data to any suitable device or receive data from any suitable device. In accordance with aspects of the present disclosure, the communication circuitry107can communicate arc fault information to a remote device/system by communicating, to the remote device/system, data which caused the controllably conductive switch60to be triggered. For example, the trained machine learning model mentioned above decides whether an arc fault has occurred based on input data computed by the microprocessor100. When the microprocessor100decides that an arc fault has occurred, the communication circuitry107can communicate the input data to the remote device/system for further processing. In various embodiments, the communication circuitry107can also communicate a version of the trained machine learning model to the remote system to identify the model which triggered the controllably conductive switch60. The remote device/system may be any device or system that is not a physical part of the AFCI device5. A remote device/system may include, for example, a cloud computing system, a propriety computing system, a desktop computer, a laptop, a tablet, a smartphone, and/other another type of device or system that is not a physical part of an AFCI device.

It should be understood by a person of ordinary skill in the art that the individual blocks represented inFIG.1may or may not represent individual components. Rather, any suitable combination of the illustrated blocks may be implemented by a single component such as, but not limited to, a microprocessor, integrated circuit, or the like. Similarly, any given block may be implemented by more than one circuit component without departing from the scope of the present disclosure.

Referring now toFIG.2, there is shown a diagram of a machine learning model implemented by an electrical switch apparatus, such as the AFCI device or system ofFIG.1. The machine learning model may be implemented by, for example, the microprocessor100ofFIG.1. As used herein, a machine learning model refers to and includes a system which receives input data and performs machine learning computations based on the input data to decide which of two or more categories (e.g., presence of an arc event or absence of an arc event) apply the input data. Certain types of machine learning models provide classification scores and/or classification probabilities to in the process of making its decision. As used herein, the term “classification score” refers to and includes a value which indicates the extent to which a category is applicable to the input data. The term “classification probability” refers to classification scores which indicate a probability that a category is applicable to the input data, where the probabilities across all categories total to one hundred percent (e.g., unity, 100%). For simplicity, the following description may refer to classification scores, but it is intended that any description involving classification scores will apply to classification probabilities as well. In accordance with aspects of the present disclosure, certain machine learning models may not use classification scores or classification probabilities.

In the illustrated embodiment ofFIG.2, the machine learning model200receives a number n of input data210-201n. The input data210-210nwill be described in more detail below. The machine learning model200may be or may use, for example, a decision tree, k-nearest neighbor, neural network, random forest, support vector machine (SVM), logistic regression or another type of regression, Bayesian model, clustering, or deep learning, among other types of machine learning models. Persons skilled in the art will understand how to implement such machine learning models.

In the illustrated embodiment, the machine learning model200provides a decision230about whether the input data210-210nindicates presents of an arc event or absence of an arc event. In various embodiments, the decision230may be based on two classification scores generated within the machine learning model200, including a classification score that an arc event occurred and a classification score or probability that an arc event did not occur. With reference to the AFCI device5ofFIG.1, and in various embodiments, the microprocessor100can trigger the controllably conductive switch60if the classification score that an arc event occurred is greater than the classification score that an arc event did not occur. Otherwise, the microprocessor100would not trigger the controllably conductive switch60.

The illustrated embodiment ofFIG.2is exemplary, and variations are contemplated to be within the scope of the present disclosure. For example, in various embodiments, the machine learning model may use more than two classification scores or probabilities to make a decision. In various embodiments, the machine learning model may not operate based on input data and may operate based on deep learning techniques using signal samples as inputs. For example, signal samples/measurements from one or more sensors may be directly used as inputs to a deep learning neural network, which may be trained to decide whether the signal samples/measures indicate presence of an arc event or absence of an arc event. In various embodiments, more than one machine learning model may be implemented and the resulting decisions, classification scores, and/or probabilities provided by the multiple machine learning models may be combined in various ways. Such variations and other variations are contemplated to be within the scope of the present disclosure.

Referring now toFIG.3, there is shown an exemplary process for operating an AFCI device or system. The operations can be performed by, for example, the microprocessor100of an AFCI device or system5(FIG.1). At block310, the operation involves sampling the output of a power-line frequency/current sensor, such as current sensor24ofFIG.1, in a time period to provide power-line frequency samples. In various embodiments, the time period may be one half-cycle of a power-line frequency, such as one half-cycle of 60 Hz. The time period (e.g., one half-cycle) does not need to align with a zero-crossing of the power-line signal but may do so. When the time period does not align with a zero-crossing of the power-line signal, the samples may correspond to any portion of the power-line signal. At block320, the operation involves computing input data based on the power-line frequency samples (e.g., current samples). At block330, the operation involves sampling the output of a high frequency sensor, such as sensor22ofFIG.1, in the time period to provide high frequency samples. At block340, the operation involves computing input data based on the high frequency samples. At block350, the operation involves executing the machine learning model, using the input features based on the power-line frequency samples and/or using the input features based on the high frequency samples, to provide a decision on whether the input data indicates presence of an arc event or absence of an arc event. At block360, the operation involves actuating a controllably conductive switch when the decision indicates presence of an arc event or not actuating the controllably conductive switch when the decision indicates absence of an arc event.

The operation ofFIG.3is exemplary, and variations are contemplated to be within the scope of the present disclosure. For example, in various embodiments, blocks310and320may be optional, such that no input data based on power-line frequency samples are computed by the operation. In various embodiments, blocks330and340may be optional, such that no input data based on high frequency samples are computed by the operation. In various embodiments, only one input data may be computed at block320. In various embodiments, only one input data may be computed at block340. Such variations and other variations are contemplated to be within the scope of the present disclosure.

FIG.4shows an example of samples of a power-line frequency/current sensor, such as current sensor24ofFIG.1, over several cycles of the current signal. The box ofFIG.4indicates one half-cycle of the power-line frequency (e.g., the power line frequency being 60 Hz) for use as a sampling window. As mentioned above, the one half-cycle/sampling window does not need to align with a zero-crossing of the power-line signal but may do so. When the sampling window does not align with a zero-crossing of the power-line signal, the samples may correspond to any portion of the power-line signal. In various embodiments, approximately thirty-two samples can be obtained in a sampling window. In various embodiments, another sampling rate can be used to obtain another number of samples. In various embodiments, the current samples for a time period of one half-cycle can be used to compute input data based on the current samples. In various embodiment, a time period different from one half-cycle can be used, and current samples over such a time period/sampling window can be used to compute input data based on the current samples.

In accordance with aspects of the present disclosure, the input data based on the current samples over a time period, such as a one half-cycle sampling window, can include the mean of the current samples within the time period and/or the maximum difference between two consecutive current samples within the time period. Other input data based on the current samples can be computed and are contemplated to be within the scope of the present disclosure. In various embodiments, the machine learning model may use one input data based on the current samples, multiple input data based on the current samples, or no input data based on the current samples. Such variations are contemplated to be within the scope of the present disclosure.

FIG.5shows an example of samples of a high frequency sensor, such as high frequency sensor22ofFIG.1, over several cycles of a power-line frequency signal. The box ofFIG.5indicates one half-cycle of the power-line frequency (e.g., the power line frequency being 60 Hz) for use as a sampling window. As mentioned above, the one half-cycle/sampling window does not need to align with a zero-crossing of the power-line signal but may do so. In the illustrated embodiment, approximately thirty-two samples can be obtained in the sampling window. In various embodiments, another sampling rate can be used to obtain another number of samples. In various embodiments, the high frequency samples for a time period of one half-cycle can be used to compute input data based on the high frequency samples. In various embodiment, a time period different from one half-cycle can be used, and high frequency samples over such a time period can be used to compute input data based on the high frequency samples.

In accordance with aspects of the present disclosure, the input features based on the high frequency samples over a time period, such as but not limited to one half-cycle of a power-line frequency, can include one or more of input features shown in Table 1.

TABLE 1Input Features Based on High Frequency SamplesInput FeatureComputationAverage (F_av)Average of the high frequency samples.Close to low countA number of the high frequency samples in a first lowmeasurement range when an average of the high frequencysamples is in a first range, or a number of the high frequencycomponent samples in a second low measurement range whenthe average of the high frequency component samples is in asecond range.The second range may be lower than the first range. An endpoint of the first range and an end point of the second range maybe the same.The first low measurement range and the second lowmeasurement range may both be below the second range. Aportion of the first low measurement range may be higher thanthe second low measurement range. An end point of the first lowmeasurement range and an end point of the second lowmeasurement range may be the same.Example: within one half-cycle, when F_av > 900, the count ofhigh frequency samples with value below 250; or when400 < F_av < 900, the count of high frequency sampleswith value below 150.Close to high countA number of the high frequency samples in a first highmeasurement range when the average of the high frequencysamples is in a first range, or a number of the high frequencysamples in a second high measurement range when the averageof the high frequency component samples is in a second range.The second range may be lower than the first range. An endpoint of the first range and an end point of the second range maybe the same.The first high measurement range may be above a portion of thefirst range. At least a portion of the second high measurementrange may be below the first high measurement range. An endpoint of the first high measurement range and an end point of thesecond high measurement range may be the same. An end pointof the first high measurement range may be the lower ofF_av or a predetermined end point value. An end point of thesecond high measurement range may be within the second range.Example: within one half-cycle, when F_av > 900, the count ofhigh frequency samples with value larger than F_av, above 1000and below 2300; or when 400 < F_av < 900, the count of highfrequency samples with the value below 2300 and above 600.Maximum frequencyMaximum value of high frequency samples.sampleMinimum frequencyMinimum value of high frequency samples.sampleFirst high countA number of the high frequency samples in a high measurementrange.The high measurement range may be all values above apredetermined threshold.Example: count of high frequency samples with value above1100 within one half-cycle.Second high countA number of the high frequency samples in a high measurementrange.The high measurement range may be all values above apredetermined threshold.Example: count of high frequency samples with value above1300 within one half-cycle.Maximum increaseMaximum increase between consecutive samples of the highfrequency samples.Maximum increase may be always a positive number.Maximum decreaseMaximum decrease between consecutive samples of the highfrequency samples.Maximum decrease may be always a negative number.Distance betweenIndex difference between an index corresponding to themax increase andmaximum increase between consecutive samples of the highmax decreasefrequency samples and an index corresponding to a maximumdecrease between consecutive samples of the high frequencysamples.Low countA number of the high frequency samples in a low measurementrange.The low measurement range may be all values below apredetermined threshold.Example: count of high frequency measurements with the valuebelow 100 within one half cycle.

Other input data based on the high frequency samples can be computed and are contemplated to be within the scope of the present disclosure. In various embodiments, the machine learning model may use one input data based on the high frequency samples, multiple input data based on the high frequency samples, or no input data based on the high frequency samples. Such variations are contemplated to be within the scope of the present disclosure.

Accordingly, described above are AFCI systems and devices which apply a machine learning model and uses input data based on current samples and/or input data based on high frequency samples to decide whether an arc event has occurred. Persons skilled in the art will understand how to train and implement various machine learning models based on one or more of the input data described in connection withFIG.4andFIG.5. As persons skilled in the art will understand, when supervised learning is implemented, input data corresponding to an arc fault are labeled as such, and input data corresponding to no arc fault are labeled as such. In various embodiments, labeling for supervised learning can be performed by persons knowledgeable in identifying arc faults, by AFCI devices which are configured to detect arc faults, and/or by computational systems which are configured to detect arc faults. When unsupervised learning is implemented, such as clustering, input data do not need to be labeled. Persons skilled in the art will understand other types of machine learning implementations, which are all intended to be within the scope of the present disclosure.

FIG.6is a diagram of an exemplary system for training and deploying machine learning models to arc fault detection devices for deciding whether an arc event is present or absent. A computing system600can implement and train a machine learning model. The computing system600may be a cloud system or may be a non-cloud system, such as a proprietary computing system. In various embodiments, the computing system600may be a desktop computer, a laptop, a tablet, or a smartphone, among other things. The computing system600can include components which persons skilled in the art will recognize, such as processors (e.g., CPUs, GPUs, etc.), memory (e.g., RAM), storage devices, and communication devices. All such components and other components which persons skilled in the art would recognize are contemplated to be within the scope of the present disclosure.

The computing system600may receive training data that includes training data corresponding to presence of arc faults and training data corresponding to absence of arc faults. The training data can include one or more of the input data described in connection withFIG.4andFIG.5. In accordance with aspects of the present disclosure, the computing system600can implement and train a decision tree using the input data described in connection withFIG.4andFIG.5and using classification and regression tree (CART) analysis. It has been found that a trained decision tree can detect serial arc faults at about 92% true positive rate. A decision tree implementation for arc fault detection is described in more detail in connection withFIG.8.

In accordance with aspects of the present disclosure, the computing system600can train a machine learning model and deploy the trained machine learning model to remote AFCI devices, such as AFCI devices which are installed and operational in residential building620, AFCI devices which are installed and operational in commercial buildings630, and/or other AFCI devices640-640n. As mentioned above herein, the computing system600is a remote from the AFCI devices640-640nin that the computing system600is not physically part of any of the AFCI devices640-640n. The AFCI devices620-640ncan be communication-enabled AFCI devices, such as an AFCI device having communication circuitry107, as shown inFIG.1. The communication circuitry107can enable, for example, Wi-Fi communications, Bluetooth communications, or other communication protocols. Various aspects of communication-enabled AFCI devices and various aspects of updating AFCI devices are described in U.S. Patent Application Publication No. 2020/0059081 and No. 2020/0051423, which are hereby incorporated by reference herein in their entirety. Aspects of deploying a trained machine learning model to an AFCI device will be described in connection withFIG.7.

In various embodiments, the AFCI devices620-640ncan communicate sensor data/measurements to the computing system600for the computing system600to use as further training data. The communications can occur over one or more networks (e.g., intranet, extranet, Internet, cellular network, etc.) and may occur through one or more intermediate devices (e.g., routers, switches, etc.) The networks and intermediate devices (if any) are not illustrated inFIG.6for simplicity. In various embodiments, the AFCI devices620-640ncan compute the input data used by the machine learning models, such as the input data described in connection withFIG.4andFIG.5, and can then communicate the input data to the computing system600for use as further training data. In various embodiments, the AFCI devices620-640ncan communicate a combination of sensor data/measurements and computed input data to the computing system600. In this manner, the computing system600can receive sensor data/measurements and/or input data from AFCI devices operating in real-world situations and can update and train machine learning models using such data.

In accordance with aspects of the present disclosure, the computing system600can convey an arc detection program, which implements a trained machine learning model and which includes instructions for computing input data for input to the machine learning model, to a manufacturing facility610which manufacturers AFCI devices, such as the AFCI device ofFIG.1. In various embodiments, the manufacturing facility610can deploy the arc detection program in the AFCI devices as firmware.

In accordance with aspects of the present disclosure, the AFCI devices620-640ncan be manufactured to contain an arc detection program which implements a trained machine learning model in accordance with aspects of the present disclosure. Over time, the computing system600may improve the accuracy of the machine learning model by further training, such as reducing false positive arc event decision. In accordance with aspects of the present disclosure, the AFCI devices620-640ncan be manufactured to contain a trained machine learning model in accordance with aspects of the present disclosure. Over time, the computing system600may improve the accuracy of the machine learning model by further training. The computing system600can be configured to communicate an updated machine learning model, or data thereof, to the communication circuitry of the installed AFCI devices, and the microprocessors of the AFCI devices can update the detection programs in the AFCI devices.

In accordance with aspects of the present disclosure, the computing system600may train different machine learning models for different AFCI devices. For example, the AFCI devices in the residential building620may experience different conditions than the AFCI devices in the commercial building630. The computing system600may train a particular machine learning model for the AFCI devices in the residential building620and may train a different machine learning model for the AFCI devices in the commercial building630. In general, the computing system600may train a particular machine learning model for any particular group of AFCI devices.

In various embodiments, an AFCI device or a group of AFCI devices may experience “nuisance trips,” which are false positive arc event decisions. In accordance with aspects of the present disclosure, such communication-enabled AFCI devices may communicate their sensor data and/or communicate their input data to the computing system600every time the AFCI trips. In various embodiments, the computing system600may process the received sensor data and/or input data to distinguish between data reflecting a true arc event and data reflecting a false positive arc event/nuisance trip. Techniques for such processing to distinguish data reflecting true arc event and data reflecting false positive arc event will be recognized by persons skilled in the art. In various embodiments, data analysts and/or engineers may process the received sensor data and/or input data to categorize the data as a true arc fault or a false positive arc event. The computing system may train a machine learning model based on such sensor data and/or input data to reduce false positive arc event decisions. The communications may occur using various aspects described in U.S. Patent Application Publication No. 2020/0059081 and No. 2020/0051423, which were incorporated by reference above. As persons skilled in the art will recognize, manually adjusting an arc detection program by engineers is time consuming. Training a machine learning model using sensor data/measurements representative of true arc events and nuisance trips can provide a quicker solution.

The aspects and embodiments described in connection withFIG.6are exemplary. Variations of the described aspects and embodiments are contemplated to be within the scope of the present disclosure. For example, in various embodiments, the AFCI devices may be configured to communicate sensor data or input data to the computing system600based on certain criteria. For example, the AFCI devices may be configured to communicate sensor data or input data to the computing system600when a user instructs the AFCI device to transmit the data, such as by pressing a button on the AFCI device, or engaging an interface of a connected smartphone app, or directing the transmission through a cloud interface, or by other ways. In various embodiments, an AFCI device may be configured to communicate sensor data or input data to the computing system600when the AFCI device detects a predetermined number of arc events followed by resets, which occur in a predetermined period of time (e.g., an AFCI trips and resets three times in a span of three minutes). In various embodiments, all sensor data/input data received by the computing system600may be designated as true arc data by default, and the computing system600or a data analyst/engineer may then re-categorize certain data as false positive arc event data. Such and other variations are contemplated to be within the scope of the present disclosure.

In accordance with aspects of the present disclosure,FIG.7shows a block diagram of exemplary components of an AFCI device, such as the AFCI device ofFIG.1. For clarity, only certain components are illustrated, and persons skilled in the art will recognize other components which may be present in an AFCI device, such as various components shown inFIG.1. The exemplary components include a controller710and a memory720. The controller710may be any processor or controller, such as those described above in connection with microprocessor100FIG.1. The memory720may be any type of memory, such as random access memory or flash memory, among others. Although a single controller710and a single memory720are illustrated, embodiments having more than one controller and/or more than one memory are contemplated to be within the scope of the present disclosure.

The memory720is configured to store an arc detection program730, which may be firmware and may include machine-readable and machine-executable instructions to be executed by the controller710. The arc detection program730may implement a trained machine learning model for deciding between presence and absence of an arc event, such as the machine leaning models described in connection withFIG.2, and may include instructions for computing input data for input to the machine learning model, such as the input data described in connection withFIG.4and/orFIG.5. In various embodiments, the memory720may store the sensor measurements for computing the input data and/or may store the input data computed based on the sensor measurements.

In accordance with aspects of the present disclosure, the arc detection program730includes a field-updatable program portion732and a non-field-updatable program portion734. The non-field-updatable program portion734may include instructions which implement a machine learning model and which do not change when the machine learning model is improved by a computing system (such as by the computing system600ofFIG.6). The field-updatable program portion732may include program parameters to be used by the non-field-updatable program portion734for deciding between presence of an arc event or absence of an arc event.

In various embodiments, the non-field-updatable program portion734may include memory pointers to the memory locations storing the field-updatable program portion732. Updates to the field-updatable program portion732may be stored in the memory locations referenced by the memory pointers. The non-field-updatable program portion734may access the updates by accessing the same memory locations referenced by the memory pointers.

In various embodiments, the non-field-updatable program734portion may include machine instructions which implement computational operations, and the field-updatable program portion732may include variables and numerical values used by the computational operations. An example for implementing a decision tree is described in connection withFIG.8.

In various embodiments, the non-field-updatable program portion734may be stored in read-only memory or in a protection portion of the memory720. In various embodiments, the non-field-updatable program portion734may not be stored in protected memory. Rather, the non-field-updatable program portion734may not be field-updatable because the firmware update program for the AFCI device may not include instructions for updating the non-field-updatable program portion734. Other ways of protecting or not updating the non-field-updatable program portion734are contemplated to be within the scope of the present disclosure.

Implementing a machine learning model using the illustrated embodiment is advantageous for ensuring that the capabilities of the controller710are adequate to implement the arc detection program730effectively, while still permitting the arc detection program730to be updated to some degree to improve accuracy and reduce false positive decisions.

FIG.8shows a diagram of an exemplary memory800which may store the field-updatable program portion of the arc detection program and which may be referenced by a memory pointer of the non-field-updatable program portion734of the arc detection program. The illustrated parameters implement an exemplary decision tree. As persons skilled in the art will understand, a decision tree includes nodes, and each node includes either a decision tree decision (e.g., presence of an arc event or absence of an arc event) or a branch criterion together with destination nodes. InFIG.8, five nodes810-834are illustrated, in which four nodes810-832include a branch criterion and destination nodes, and one node834includes a decision tree decision.FIG.9shows the same five nodes810-834presented in the arrangement that decision trees are typically viewed and understood. As shown byFIG.9, a node which includes a branch criterion and destination nodes will branch off to further nodes, whereas a node which includes a decision tree decision does not branch off to any other nodes.

In a decision tree, the node operation for each node is the same and does not change. As such, the node operation may be implemented by the non-field-updatable program portion734of the arc detection program. For example, the node operation for each node may be as follows: (1) if the node includes a decision, provide the decision; (2) if the node includes a branch criterion, evaluate the branch criterion to select one of the destination nodes; (3) repeat the node operation for the selected node destination. Starting with the initial node810, the node operation determines that the node810does not include a decision. Rather, the node810includes a branch criterion and destination nodes. Accordingly, the node operation evaluates the branch criterion to select one of the destination nodes822or824. If the result of the branch criterion selects destination node822, the node operation repeats for the destination node822. If the result of the branch criterion selects destination node824, the node operation repeats for the destination node824.

If destination node822is selected, the node operation is performed for node822. The node operation determines that node822does not include a decision. Rather, the node822includes a branch criterion and destination nodes. Accordingly, the node operation evaluates the branch criterion to select one of the destination nodes832or834. If the result of the branch criterion selects destination node832, the node operation repeats for the destination node832. If the result of the branch criterion selects destination node834, the node operation repeats for the destination node834.

If destination node834is selected, the node operation is performed for node834. The node operation determines that node834does include a decision (e.g., presence of an arc event, or absence of an arc event). Accordingly, the node operation provides the decision of node834as the resulting decision of the decision tree.

As shown by the exemplary decision tree ofFIGS.8and9, the node operation is the same regardless of how the decision tree is configured. Thus, the node operation may form the non-field-updatable program portion of the arc detection program. In various embodiments, the node operation may include various types of predetermined operators (e.g., greater than, less than, equal to, not equal to, greater than or equal to, less than or equal to, etc.). The branch criterion may specify one or more operators to use and may specify the operands for the operator(s). The operands may include the input data described above in connection withFIGS.4and5and may include numerical values. As an example, and referring to Table 1 andFIG.9, the branch criterion of node810may be:
(Maximum increase between consecutive samples of the high frequency samples)<10.
The operator of the branch criterion is a “less than” operator. The operands are one of the input data (maximum increase between consecutive samples of the high frequency samples) and a numerical value (10). The input data for the operand may be computed based on sensor measurements. Thus, the branch criteria can specify operators and operands. For node810, if the criterion is true, the destination node822may be selected, and if the criterion is false, the destination node824may be selected.

The nodes shown inFIGS.8and9are exemplary, and variations are contemplated to be within the scope of the present disclosure. For example, node operations other than those described above are contemplated to be within the scope of the present disclosure. Additionally, other ways of separating an arc detection program into a field-updatable program portion and a non-field-updatable program portion are contemplated to be within the scope of the present disclosure.

Referring again toFIG.7, the arc detection program730may implement other machine learning models using the same approach. For example, as persons skilled in the art will recognize, neural networks have nodes and certain neural networks may be implemented using the same node operations for each node. Accordingly, the non-field-updatable program portion734may implement node operations for a neural network, and the field-updatable program portion732may include parameters to be used by the node operations. The same approach may be used for other types of machine learning models as well.

Various benefits of the approach ofFIG.7were described above. Additionally, updating only “parameters” of a machine learning model without updating the code/operations of the machine learning model may permit the field-deployed AFCI devices to be updated without needing recertification, such as UL recertification. In various embodiments, the AFCI devices may include a default factory-installed set of parameters, which may be used in the event remotely updated parameters become corrupted or otherwise unusable. The arc detection program730and/or a separate safety program (not shown) may occasionally perform tests to confirm the integrity and usability of the updated parameters. If such tests fail, the arc detection program730may then use the default factory-installed set of parameters.

FIG.10shows an exemplary operation for improving a machine learning model and deploying updated parameters of the improved machine learning model to AFCI devices. The illustrated operation can be performed by, for example, the computing system600ofFIG.6. At block1010, the operation involves accessing a trained machined learning model. At block1020, the operation involves accessing input data generated based on sensor data/measurements of installed, operational, and communication-enabled arc fault circuit interrupt (AFCI) devices, such as those shown inFIG.6. In various embodiments, the input data can be generated by the AFCI devices. In various embodiments, the AFCI devices can communicate sensor data/measurements to a computing system, and the computing system can generate the input data based on the sensor data/measurements. At block1030, the operation involves further training the trained machine learning model based on the input data to provide an updated machine learning model. The updated machine learning model may include, for example, parameters which are different from the parameters of the previously trained machined learning model. At block1040, the operation involves communicating parameters of the updated machine learning model to at least some of the installed and operational AFCI devices to replace the field-updatable program portions of the arc detection programs. In this manner, a process is disclosed for gathering real-world AFCI data for further training and improving trained machine learning models, and for providing parameters of an updated machine learning model to the installed AFCI devices. The illustrated embodiment ofFIG.10is exemplary, and variations are contemplated to be within the scope of the present disclosure.

The embodiments disclosed herein are examples of the disclosure and may be embodied in various forms. For instance, although certain embodiments herein are described as separate embodiments, each of the embodiments herein may be combined with one or more of the other embodiments herein. Specific structural and functional details disclosed herein are not to be interpreted as limiting, but as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure. Like reference numerals may refer to similar or identical elements throughout the description of the figures.

The phrases “in an embodiment,” “in embodiments,” “in various embodiments,” “in some embodiments,” or “in other embodiments” may each refer to one or more of the same or different embodiments in accordance with the present disclosure. A phrase in the form “A or B” means “(A), (B), or (A and B).” A phrase in the form “at least one of A, B, or C” means “(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).”

Any of the herein described methods, programs, algorithms, or codes may be converted to, or expressed in, a programming language or computer program. The terms “programming language” and “computer program,” as used herein, each include any language used to specify instructions to a computer, processor, or controller, and include (but is not limited to) the following languages and their derivatives: Assembler, Basic, Batch files, BCPL, C, C+, C++, Delphi, Fortran, Java, JavaScript, machine code, operating system command languages, Pascal, Perl, PL1, scripting languages, Visual Basic, metalanguages which themselves specify programs, and all first, second, third, fourth, fifth, or further generation computer languages. Also included are database and other data schemas, parameters and/or variables, and any other meta-languages. No distinction is made between languages which are interpreted, compiled, or use both compiled and interpreted approaches. No distinction is made between compiled and source versions of a program. Thus, reference to a program, where the programming language could exist in more than one state (such as source, compiled, object, or linked) is a reference to any and all such states. Reference to a program may encompass the actual instructions, variables, and parameters, and/or the intent of those instructions, variables, and parameters.

The systems described herein may also utilize one or more controllers to receive various information and transform the received information to generate an output. The controller may include any type of computing device, computational circuit, or any type of processor or processing circuit capable of executing a series of instructions that are stored in a memory. The controller may include multiple processors and/or multicore central processing units (CPUs) and may include any type of processor, such as a microprocessor, digital signal processor, microcontroller, programmable logic device (PLD), field programmable gate array (FPGA), or the like. The controller may also include a memory to store data and/or instructions that, when executed by the one or more processors, causes the one or more processors to perform one or more methods and/or algorithms.

It should be understood that the foregoing description is only illustrative of the present disclosure. Various alternatives and modifications can be devised by those skilled in the art without departing from the disclosure. Accordingly, the present disclosure is intended to embrace all such alternatives, modifications, and variations. The embodiments described with reference to the attached drawing figures are presented only to demonstrate certain examples of the disclosure. Other elements, steps, methods, and techniques that are insubstantially different from those described above and/or in the appended claims are also intended to be within the scope of the disclosure.