AIR CONDITIONER CAPACITOR EFFICACY LOSS DETECTION

Description

FIELD

The disclosure below relates to technically inventive, non-routine solutions that are necessarily rooted in computer technology and that produce concrete technical improvements. In particular, the disclosure below relates to detection of impending air conditioner capacitor failure and remediation.

BACKGROUND

Air conditioner systems often include a capacitor that can fail. But when the capacitor fails, the air conditioner system is rendered inoperable until a replacement part is installed. This means that users are often left without air conditioning during critical times when they need air conditioning the most, like during heat waves or even just a normal hot summer day. No adequate solutions currently exist to remedy the foregoing technological problem.

SUMMARY

Accordingly, in one aspect an apparatus includes a processor system and storage accessible to the processor system. The storage includes instructions executable by the processor system to identify a fan startup time for an air conditioner system. The instructions are also executable to determine, based on the fan startup time, that a capacitor of the air conditioner system is losing efficacy. The instructions are then executable to, based on the determination, present a graphical user interface (GUI) at a device. The GUI includes a notification indicating that the capacitor is losing efficacy.

In some example embodiments, the device may be a thermostat, and the apparatus may even include the thermostat. The apparatus may also include the air conditioner system if desired.

In various example implementations, fan startup time may relate to time for a fan of the air conditioning system to reach a target rotations per minute (RPMs).

Also in various example implementations, the fan startup time may be a current fan startup time, and the instructions may be executable to access a metric for average past fan startup times to then make the determination based on the current fan startup time and the metric for average past fan startup times. In some cases, the instructions may even be executable to store the past fan startup times and, based on the stored past fan startup times, determine the metric for the average past fan startup times.

In some examples, the notification may indicate that the capacitor is about to fail. Additionally, if desired the GUI may include a selector that is selectable to initiate a repair of the air conditioner system (e.g., by ordering a part online, contacting a technician, and/or scheduling a repair).

In another aspect, a method includes identifying a startup time of a hardware component of an air conditioner system and determining, based on the startup time, that a capacitor of the air conditioner system is losing efficacy. Based on the determination, the method includes presenting a graphical user interface (GUI) at a device, where the GUI includes a notification indicating that the capacitor is losing efficacy.

In some examples, the hardware component may include a fan of the air conditioner system and/or a compressor of the air conditioner system.

Also in some examples, the startup time may be a current startup time, and the method may include executing a model to infer that the current startup time is inconsistent with previous startup times for the hardware component. Here the method may then include determining, based on the inference, that the capacitor is losing efficacy. In certain non-limiting examples, the model may specifically include an artificial neural network.

In still another aspect, an apparatus includes at least one computer readable storage medium (CRSM) that is not a transitory signal. The at least one CRSM includes instructions executable by a processor system to identify an operational metric of a component of an air conditioner system. The instructions are also executable to determine, based on the operational metric, that a capacitor of the air conditioner system is going to fail. The instructions are further executable to, based on the determination, present a notification indicating that the capacitor is going to fail.

In various examples, the component may include a fan of the air conditioner system and/or a compressor of the air conditioner system.

Additionally, in some cases the instructions may be executable to input the operational metric to a model, and to receive an output from the model. The output may indicate that the capacitor of the air conditioner system is losing efficacy.

In some specific instances, the operational metric may be a current startup time, and the instructions may be executable to input, to the model, the current startup time and one or more past startup times for the component. The instructions may then be executable to execute the model to receive, based on the input of the current startup time and the one or more past startup times, the output. If desired, in non-limiting embodiments the model may be a machine learning (ML) model configured for pattern recognition.

The details of the present application, both as to its structure and operation, can be best understood in reference to the accompanying drawings, in which like reference numerals refer to like parts, and in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example computing system consistent with present principles;

FIG. 2 is a schematic diagram of a condenser unit of an air conditioning system and a smart thermostat with which the condenser unit communicates consistent with present principles;

FIG. 3 shows an example graphical notification that a capacitor of the air conditioning system is about to fail consistent with present principles;

FIG. 4 shows example logic in example flow chart format that may be executed by an apparatus consistent with present principles; and

FIG. 5 shows example artificial intelligence (AI) architecture for an ML-based model that may be implemented consistent with present principles.

DETAILED DESCRIPTION

This disclosure relates generally to aspects of consumer electronics (CE) devices and other types of client devices and servers. Thus, devices herein may include server and client components which may be connected over a network such that data may be exchanged between the client and server components. The client components may include one or more computing devices including mobile smart phones and other mobile devices, wearable devices, game consoles, extended reality (XR) headsets such as virtual reality (VR) headsets and augmented reality (AR) headsets, display devices such as televisions (e.g., smart TVs, Internet-enabled TVs), personal computers such as laptops, desktop, and tablet computers, and still other types of devices. These client devices may operate with a variety of operating environments. For example, a client device consistent with present principles may employ, as examples, Linux and Unix operating systems, operating systems from Microsoft, or operating systems from Apple or Google. These operating environments may be used to execute one or more browsing programs, such as a browser made by Microsoft, Apple, Google, or Mozilla. The operating environments may also be used to execute other Internet-networked dedicated mobile applications that can access websites hosted by the Internet servers over a network such as the Internet, a local intranet, or a virtual private network.

Servers and/or gateways may be used that may include one or more processors executing instructions that configure the servers to receive and transmit data over a network such as the Internet. Or a client and server can be connected over a local intranet or a virtual private network. A server or controller may be instantiated by a personal computer, mobile device, rack or blade server, etc.

As indicated above, information may be exchanged over a network between client devices and servers. To this end and for security, servers and/or clients can include firewalls, load balancers, temporary storages, and proxies, and other network infrastructure for reliability and security.

As used herein, instructions may refer to computer-implemented steps for processing information in the system. Instructions can be implemented in software, firmware or hardware, or combinations thereof and include any type of programmed steps undertaken by components of the system.

A processor may be any single- or multi-chip processor that can execute logic by means of various lines such as address lines, data lines, and control lines and registers and shift registers. Moreover, any logical blocks, modules, and circuits described below can be implemented or performed with a processor/processor system such as a central processing unit (CPU), a digital signal processor (DSP), a field programmable gate array (FPGA) or other programmable logic device, an application specific integrated circuit (ASIC), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor can be implemented by a controller or state machine or a combination of computing devices.

Software modules described by way of the flow charts and user interfaces herein can include various sub-routines, procedures, etc. Without limiting the disclosure, logic stated to be executed by a particular module can be redistributed to other software modules and/or combined together in a single module and/or made available in a shareable library.

The functions and methods described below, when implemented in software, can be written in an appropriate language such as but not limited to hypertext markup language (HTML)-5, Java®/Javascript, C# or C++, and can be stored on or transmitted from a computer-readable storage medium such as a hard disk drive (HDD) or solid state drive (SSD), random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), compact disk read-only memory (CD-ROM) or other optical disk storage such as digital versatile disc (DVD), magnetic disk storage or other magnetic storage devices including removable thumb drives, etc. A connection may establish a computer-readable medium. Such connections can include, as examples, hard-wired cables including fiber optics and coaxial wires and digital subscriber line (DSL) and twisted pair wires.

In an example, a processor system can access information over its input lines from data storage, such as a computer readable storage medium as referenced above, and/or the processor system can access information wirelessly from an Internet server by activating a wireless transceiver to send and receive data. Data typically is converted from analog signals to digital by circuitry between the antenna and the registers of the processor system when being received and from digital to analog when being transmitted. The processor system then processes the data through its shift registers to output calculated data on output lines, for presentation of the calculated data on the device, etc.

Components included in one embodiment can be used in other embodiments in any appropriate combination. For example, any of the various components described herein and/or depicted in the Figures may be combined, interchanged, or excluded from other embodiments.

“A system having at least one of A, B, and C” (likewise “a system having at least one of A, B, or C” and “a system having at least one of A, B, C”) includes systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together.

The term “a” or “an” in reference to an entity refers to one or more of that entity. As such, the terms “a” or “an”, “one or more”, and “at least one” can be used interchangeably herein.

The term “circuit” or “circuitry” may be used in the summary, description, and/or claims. The term “circuitry” includes all levels of available integration, e.g., from discrete logic circuits to the highest level of circuit integration such as VLSI, and includes programmable logic components programmed to perform the functions of an embodiment as well as processors (e.g., special-purpose processors) programmed with instructions to perform those functions.

Note that present principles may also employ machine learning models, including deep learning models. Machine learning models use various algorithms trained in ways that include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, feature learning, self-learning, and other forms of learning. Examples of such algorithms, which can be implemented by computer circuitry, include one or more neural networks, such as one or more convolutional neural networks (CNNs) and/or one or more recurrent neural networks (RNNs) (such as a type of RNN known as a long short-term memory (LSTM) network). Support vector machines (SVM) and Bayesian networks also may be considered to be examples of machine learning models.

As understood herein, performing machine learning involves accessing and then training a model on training data to enable the model to process further data to make predictions. A neural network may include an input layer, an output layer, and multiple hidden layers in between that are configured and weighted to make inferences about an appropriate output.

Referring now to FIG. 1, an example system 10 is shown, which may include one or more of the example devices mentioned above and described further below in accordance with present principles. The first of the example devices included in the system 10 is a consumer electronics (CE) device 12. The CE device 12 may be a computerized Internet enabled (“smart”) phone, a tablet computer, a laptop/notebook computer, a desktop computer, a head-mounted device (HMD) and/or headset such as smart glasses or AR or VR headset, another wearable computerized device, etc. Regardless, it is to be understood that the CE device 12 is configured to undertake present principles (e.g., communicate with other CE devices and servers to undertake present principles, execute the logic described herein, and perform other functions and/or operations described herein).

Accordingly, to undertake such principles the CE device 12 can be established by some, or all, of the components shown. For example, the CE device 12 can include one or more touch-enabled displays 14 that may be implemented by a high definition or ultra-high definition “4K” or higher flat screens. The touch-enabled display(s) 14 may include, for example, a capacitive or resistive touch sensing layer with a grid of electrodes for touch sensing consistent with present principles (e.g., to provide input to the GUIs discussed below).

The CE device 12 may also include an analog audio output port 15 to drive one or more external speakers or headphones, and may include one or more internal speakers 16 for outputting audio in accordance with present principles, and at least one additional input device 18 such as an audio receiver/microphone, e.g., for conversing telephonically or for entering audible commands to the CE device 12 to control the CE device 12. The example CE device 12 may also include one or more wired or wireless network interfaces 20 for communication over at least one network 22 such as the Internet, a WAN, a LAN, etc. under control of one or more processors of a processor system 24, such as a CPU or other processor mentioned above. Thus, the interface 20 may be, without limitation, a Wi-Fi transceiver and/or wireless telephony transceiver for communicating over a wireless cellular network (e.g., operated by Verizon, T-Mobile, or AT&T), both of which are examples of a wireless computer network interface.

It is to be understood that the processor system 24 may include one or more processors acting independently or in concert with each other to execute an algorithm (e.g., the algorithms referenced herein), whether those processors are in one device or more than one device. Thus, in some specific examples, the processor system may include a single processor, while in other examples the processor system may include more than one processor. The processor system 24 controls the CE device 12 to undertake present principles, including the other elements of the CE device 12 described herein such as controlling the display 14 to present images thereon and receiving input therefrom. Furthermore, also note the network interface 20 may be a wired or wireless modem or router or other suitable network interface.

In addition to the foregoing, the CE device 12 may also include one or more input and/or output ports 26 such as a high-definition multimedia interface (HDMI) port or a universal serial bus (USB) port to physically connect to another CE device, and/or a headphone port to connect headphones to the CE device 12 for presentation of audio from the CE device 12 to a user through the headphones. For example, the input port 26 may be connected wired or wirelessly to a cable or satellite source 26a of audio video content. Thus, the source 26a may be a separate or integrated set top box, or a satellite receiver. Or the source 26a may be a game console or disk player containing content.

The CE device 12 may further include one or more non-transitory computer memories/computer-readable storage media 28 such as disk-based or solid-state storage that are not transitory signals, in some cases embodied in the chassis/housing of the CE device 12 (e.g., as standalone devices) or as removable memory media or the below-described server(s). Also, in some embodiments, the CE device 12 can include a position or location receiver such as but not limited to a cell phone transceiver, global positioning system (GPS) transceiver, and/or altimeter 30. This transceiver may therefore be configured to receive geographic position information from a satellite or cellphone base station (and/or determine an altitude at which the CE device 12 is disposed) and then provide the information to the processor system 24. However, it is to be understood that another suitable position receiver other than a GPS receiver, cell phone transceiver, and/or altimeter may be used consistent with present principles to determine the location of the CE device 12. In some examples, the GPS transceiver 30 may be located on a streetlight or other infrastructure for which location is to be reported for purposes described in greater detail below.

Continuing the description of the CE device 12, in some embodiments the CE device 12 may include one or more cameras 32 that may be thermal imaging cameras, digital cameras such as webcams, infrared (IR) sensors, and/or other types of cameras or other optical sensors integrated into the CE device 12 and controllable by the processor system 24 to gather pictures/images and/or video consistent with present principles. Also included on the CE device 12 may be a Bluetooth^® transceiver 34 and/or other Near Field Communication (NFC) element 36 for communication with other devices using respective Bluetooth and/or NFC wireless technologies/communication standards. An example NFC element can be a radio frequency identification (RFID) element.

Further still, the CE device 12 may include one or more auxiliary sensors 38 that provide input to the processor system 24. For example, one or more of the auxiliary sensors 38 may include one or more pressure sensors forming a layer of the touch-enabled display 14 itself and may be, without limitation, piezoelectric pressure sensors, capacitive pressure sensors, piezoresistive strain gauges, optical pressure sensors, electromagnetic pressure sensors, etc.

Other sensor examples include a motion sensor such as an accelerometer, gyroscope, magnetometer, a speed and/or cadence sensor, an event-based sensor, a gesture sensor (e.g., for sensing gesture command), etc. In one specific example, the sensor 38 thus may be implemented as an inertial measurement unit (IMU) with motion sensors including individual accelerometers, gyroscopes, and magnetometers, and/or other components of that include a combination of accelerometers, gyroscopes, and magnetometers, to determine the location and orientation of the CE device 12 in three dimensions. A gyroscope consistent with present principles may sense and/or measure the orientation of the CE device 12 and provide related input to the processor system 24, an accelerometer consistent with present principles may sense acceleration and/or movement of the CE device 12 and provide related input to the processor system 24, and a magnetometer consistent with present principles may sense and/or measure directional movement of the CE device 12 and provide related input to the processor 122.

The CE device 12 may also include an over-the-air TV broadcast port 40 for receiving OTA TV broadcasts and providing the input to the processor system 24. In addition to the foregoing, it is noted that the CE device 12 may also include an IR transceiver 42 such as an IR data association (IRDA) device. A battery (not shown) may be provided for powering the CE device 12, as may a kinetic energy harvester that may turn kinetic energy into power to charge the battery and/or power the CE device 12. A graphics processing unit (GPU) 44 and field programmable gated array 46 also may be included.

One or more haptics/vibration generators 47 may also be provided for generating tactile signals/vibrations that can be sensed by a person holding or in contact with the device. The haptics generators 47 may thus vibrate all or part of the CE device 12 using an electric motor connected to an off-center and/or off-balanced weight via the motor’s rotatable shaft so that the shaft may rotate under control of the motor (which in turn may be controlled by a processor such as the processor system 24) to create vibration of various frequencies and/or amplitudes as well as force simulations in various directions.

In addition to the CE device 12, the system 10 may include one or more other CE devices/types, which may include some or all of the components mentioned above in relation to the CE device 12. In one example, a second CE device 48 may be established by an Internet of things (IoT) device, a smartphone, a laptop computer, etc. A third CE device 50 is also shown in FIG. 1 and may include similar components as the other CE devices. Thus, in one example, the CE device 50 may be configured as a head-mounted display (HMD) that may include a heads-up transparent or non-transparent display for respectively presenting extended reality (XR) content such as AR content, VR, content, and/or mixed reality (MR) content. The XR content itself might include, as an example, one or more of the GUIs described below, presented stereoscopically. The HMD may be configured as a glasses-type display, or as goggle-type and/or VR-type display vended by various computer hardware manufacturers such as Apple, Oculus, Meta, etc. Or the CE device 50 may be established by a smart streetlight consistent with present principles and, as such, the smart streetlight may include a network communication interface (e.g., Wi-Fi transceiver and/or cellular data transceiver) for communicating with other devices to implement present principles.

In the example shown, only three CE devices are shown, it being understood that fewer or more devices may be used. A device herein may implement some or all of the components shown for the CE device 12. Any of the components shown in the following figures may incorporate some or all of the components shown in the case of the CE device 12.

Now in reference to the afore-mentioned at least one server 52, it includes at least one server processor 54 and at least one tangible computer readable storage medium 56 such as disk-based or solid-state storage. The server 52 also includes at least one network interface 58 that, under control of the server processor 54, allows for communication with other illustrated devices over the network 22 (e.g., the Internet), and indeed may facilitate communication between the server 52 and any other servers/client devices as described herein. Note that the network interface 58 may be, e.g., a wired or wireless modem or router, Wi-Fi or Ethernet transceiver, or other appropriate interface such as, e.g., a wireless telephony transceiver.

Accordingly, in some embodiments the server 52 may be an Internet server or an entire server “farm” of multiple services. If desired, the server 52 may include/perform “cloud” functions such that the devices of the system 10 may access a “cloud” environment via the server 52 in certain example embodiments. Additionally or alternatively, the server 52 may be implemented by one or more computers in the same room as the other devices shown, or nearby.

The components shown in the following figures may include some or all components shown herein. Any user interfaces (UI) described herein may be consolidated and/or expanded, and UI elements may be mixed and matched between UIs. UIs may be presented at a client device like the CE device 12 under control of the client device itself and/or under control of the server 52 as remotely controlling the CE device 12 to present the UIs thereon. Also note that selectors and options on the UIs discussed below may be selected via cursor input, touch input to a touch-enabled display on which the GUI is presented, using voice input, and/or using other input methods.

Present principles may employ various machine learning models, including deep learning models. Machine learning models consistent with present principles may use various algorithms trained in ways that include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, feature learning, self-learning, and other forms of learning. Examples of such algorithms, which can be implemented by computer circuitry, include one or more neural networks, such as a convolutional neural network (CNN), a recurrent neural network (RNN), and a type of RNN known as a long short-term memory (LSTM) network. Generative pre-trained transformers (GPTT) also may be used. Support vector machines (SVM) and Bayesian networks also may be considered to be examples of machine learning models. In addition to the types of networks set forth above, models herein may be implemented by classifiers.

As understood herein, performing machine learning may therefore involve accessing and then training a model on training data to enable the model to process further data to make inferences. An artificial neural network trained through machine learning may thus include an input layer, an output layer, and multiple hidden layers in between that are configured and weighted to make inferences about an appropriate output.

In non-limiting examples, a capacitor in an air conditioning condenser unit may deliver power to the motor(s) driving the air conditioning system (equivalently, heating system). For instance, the capacitor may provide an initial jolt of electricity to the air conditioner’s compressor motor and fan motor. As such, the capacitor may store electricity and send it to the unit’s motor(s) in powerful bursts that get the unit revved up as it starts the cooling cycle.

With the foregoing in mind, refer to FIG. 2. A condenser unit 200 of an air conditioning/cooling system is shown. The condenser unit 200 may include an electrical panel 210 to which one or more electrical components are electrically connectable to operate the unit 200. One such electrical component is an air conditioner capacitor 220, and another such electrical component is a control module 230. The control module 230 may include a processor, memory, and storage for undertaking present principles.

Among those principles is that the control module 230 may monitor the rotations per minute (RPMs) of the fan inside the condenser unit 200 during startup, runtime, and shutdown for each duty cycle of the air conditioner system. The control module 230 may do so through the electronic circuitry in the unit 200. Additionally or alternatively, the RPMs may be determined optically using a camera inside the unit 200 and that has its field of view directed toward the fan, with computer vision then being executed to count RPMs of the fan from video provided by the camera. Other methods of determining RPMs are also encompassed by present principles.

The control module 230 may also communicate wirelessly or through a wired interface with a smart thermostat 250. The smart thermostat 250 might be located in the living room of a personal residence or in the reception area of an office, for example. End-users are able to control the air conditioner system through the smart thermostat 250, including by increasing or decreasing a target ambient air temperature that the air conditioner system is to then work to meet. To enter the target temperature, the users may direct input to the touch-enabled display 260 of the smart thermostat 250, with the display 260 presenting a graphical user interface (GUI) 270 to enable users to do so.

As shown in FIG. 2, the GUI 270 may include not just target temperature selectors (not shown for simplicity), but also an indication 280 of the current ambient air temperature in the subject area. The GUI 270 may also include a graphical icon 290 in the form of a green check mark to graphically indicate that the air conditioner system is working in normal condition.

However, suppose the control module 230 and/or smart thermostat 250 determines that the condenser unit’s capacitor 220 is failing or otherwise losing efficacy. The module 230 or thermostat 250 might do so based on readings from the module 230 that indicate the current startup time for the condenser unit’s fan and/or compressor. Then if the device determines that the current startup time is indicative of the capacitor 220 losing efficacy (as set forth in greater detail below), the smart thermostat 250 may present the GUI 300 of FIG. 3 in response.

As shown in FIG. 3, the GUI 300 may include an indication 310 of the current ambient air temperature in the subject area. The GUI 300 may also include a graphical object 320 in the form of a caution icon to draw the user’s attention to the imminent or eventual capacitor failure. As also shown in FIG. 3, the GUI 300 may include a text prompt 330 that the capacitor in the condenser unit 200 appears to be failing.

The GUI 300 may also include various options for taking remedial measures to address the failing capacitor, even if the capacitor is still currently operational but should be replaced prior to complete failure. The GUI 300 may therefore include a selector 340 that is selectable to initiate an e-commerce transaction with single-selection “buy now” input to immediately purchase another capacitor as a replacement to the failing one, like-for-like (e.g., same capacitor manufacturer and model). The GUI 300 may also include a selector 350 that is selectable to initiate a live, audible voice chat with a technician of the user’s local air conditioning company at the smart thermostat 250 to further diagnose the problem (e.g., using a microphone and speaker on the thermostat 250 to communicate telephonically using VoIP). The GUI 300 may further include a selector 360 that is selectable to command the thermostat 250 to access an online portal for the same local air conditioning company so that the user can then schedule an appointment through the portal for a repair technician to visit the user’s premises to diagnose and remedy the problem with the capacitor 220.

Now in reference to FIG. 4, this figure shows example logic that may be executed by an apparatus such as the CE device 12, a client device like the smart thermostat 250, and/or a coordinating server alone or in any appropriate combination consistent with present principles. Thus, in some examples the logic may be executed by a client device alone. In other examples, the logic may be executed by the remotely-located server alone. In still other examples, the logic may be executed by a client device and remotely-located server, where the client device performs some steps while the server performs other steps, and/or where the client device and server work together to perform a given step. Further note that while the logic of FIG. 4 is shown in flow chart format, other suitable logic may also be used (e.g., a state machine).

Beginning at block 400, the apparatus may send a startup signal to the air condenser unit to begin operation to cool a controlled environment for which the ambient air temperature has gone above a target or setpoint air temperature. The logic may then proceed to block 410 where the apparatus may receive input from the electronic circuitry within the condenser unit (e.g., control module 230) to, at block 420, identify a current fan and/or compressor startup time from the input.

However, further note that other operational metrics besides current fan startup time and current compressor startup time may also be used consistent with present principles. For example, operational metrics may be used for runtime fan speed and runtime compressor output to determine whether the relevant operational metric is inconsistent with past runtime metrics for the same component (and hence whether the capacitor is losing efficacy). But for simplicity, startup times will be referenced below with it being nonetheless understood that other operational metrics may also be used accordingly.

The logic may then proceed to block 430 to access past startup times for the fan and/or compressor, as may have been stored by the module 230 at past times for respective past duty cycles. Then at block 440 the apparatus may input the current and stored (past) startup times to a machine learning (ML) model.

Additionally or alternatively but still at block 440, the apparatus may execute one or more rules-based algorithms to compare the current startup time to a metric for average past startup times of the fan and/or compressor (mean, median, or mode startup time). If an increase in startup time of more than a threshold amount is determined to have occurred as compared to the metric (e.g., currently five percent more time to reach target RPMs than the mean), the logic may then proceed directly to block 460 to provide a notification to the user of impending capacitor failure so that the user has warning in advance of complete failure.

However, in embodiments where the aforementioned ML model is used, the logic may first proceed from block 440 to block 450. At block 450 the apparatus may execute the ML model to analyze the current and past startup times and to receive, from the model, an output indicating whether the capacitor is about to fail.

For example, the control module 230 or thermostat 250 may execute the ML model to monitor the progressively increasing RPMs of the fan in the condenser unit as the fan begins spinning for the current duty cycle to determine whether the fan is starting up slower (more time to reach target RPM) than during past startups, or whether the current startup time is otherwise inconsistent with previous startup times for the fan. Additionally or alternatively, the control module 230 may execute the ML model to analyze input from the circuitry in the unit 200 that indicates that power is being supplied to the compressor but that the compressor is not getting up to operational speed as quickly as in the past. In either case (fan or compressor monitoring), the ML model may be executed to provide an output indicating that the capacitor is losing efficacy.

Then, responsive to a determination based on the ML model output that that the capacitor of the air conditioner system is losing efficacy, the logic may proceed to block 460 where the apparatus may present a GUI that includes a notification indicating that the capacitor is losing efficacy. The GUI may be presented on the display of a smart thermostat as described above, and/or may be presented at another client device such as the smartphone of the end user that is executing a smart home application (“app”) to communicate with the smart thermostat.

Note that other types of notifications may be presented additionally or in the alternative but still at block 460. For example, audible notifications may be provided where a predetermined chime or tone may be played out periodically at the smart thermostat using a speaker on the thermostat. Audible tones may also be presented at the connected smartphone through the smart home app. As another example, tactile/vibration notifications may also be presented at the smartphone to get the user’s attention (e.g., vibrating the phone a predetermined number of times at predetermined intervals).

From block 460 the logic may then proceed to block 470 where the apparatus may store the current startup time in the existing history of startup times for use of this additional startup time in the future consistent with the logic set forth above.

In terms of the aforementioned metric for the average of past startup times for the fan or compressor, note that the apparatus itself may in some embodiments calculate the average of the past startup times as part of the logic for block 440, accessing the previously stored times to perform the calculation.

Now in reference to FIG. 5, example artificial intelligence (AI) architecture is shown for an ML model 500 that may be executed consistent with the logic of FIG. 4. However, note that the architecture 500 is but an example and that other AI architectures are also encompassed by present principles. Also note that in certain examples, the ML model 500 may include one or more artificial neural networks (ANNs).

As shown in FIG. 5, the model 500 may include a startup time pattern recognizer 510. The recognizer 510 may be established by an ANN such as a convolutional neural network or other suitable pattern recognition model.

The model 500 may also include a discriminative capacitor failure detector 520.

The recognizer 510 may therefore receive, as input, a current startup time and one or more past startup times for a hardware component (e.g., fan or compressor) to then infer a pattern as to whether the current startup time is inconsistent with previous startup times for the respective component. The output from the recognizer 510 may then be provided as input to the detector 520, which may be an activation function of the recognizer 510 or its own model that, in either case, outputs an inference that the relevant capacitor is about to fail (or not). If the detector 520 is a separate model, it may be embodied as any suitable classifier such as a decision tree, k-nearest neighbor, support vector machine, etc.

Therefore, note that the ML model 500 may therefore be trained using supervised learning and other machine learning techniques. In one particular example, the ML model 500 may be trained through supervised learning, using a dataset that includes respective pairs of current and past startup times and a respective ground truth label for whether capacitor failure is implicated or not by the pair.

In one particular aspect, an apparatus and method consistent with present principles may operate substantially as shown and described above, but may also be claimed as including some but not all aspects in any intermediate claim approach.

Before concluding, it is to be understood that although a software application for undertaking present principles may be vended with a device, present principles apply in instances where such an application is downloaded from a server to a device over a network such as the Internet. Furthermore, present principles apply in instances where such an application is included on a computer readable storage medium that is vended and/or provided by itself, where the computer readable storage medium is not a transitory signal and/or a signal per se.

It may now be appreciated that present principles provide, among other technical improvements, improved computer-based user interfaces that increase the functionality and ease of use of the devices disclosed herein. The disclosed concepts are rooted in computer technology for computers to carry out their functions.

It is to be understood that whilst present principles have been described with reference to some example embodiments, these are not intended to be limiting, and that various alternative arrangements may be used to implement the subject matter claimed herein.