Health monitor circuit for an electric machine

Abstract

A health monitor circuit for an electric machine is described. The health monitor circuit includes at least one sensor configured to measure a parameter of the electric machine, a communication interface, and a microprocessor coupled to the at least one sensor, the communication interface, and a memory. The microprocessor is configured to periodically collect time samples of the parameter measured by the at least one sensor, transmit factors of the time samples to the memory, and perform a high-resolution fast Fourier transform (FFT) on the factors. The microprocessor is further configured to extrapolate a result of the high-resolution FFT to produce a high-resolution frequency domain waveform, filter the high-resolution frequency domain waveform based on a parameter, and transmit the filtered frequency domain waveform to a remote system via the communication interface for further processing.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62 / 868,368, filed on June 28, 2019, entitled “HEALTH MONITOR CIRCUIT FOR ANELECTRIC MACHINE,” the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] The field of the present disclosure relates generally to a health monitor circuit for an electric machine, and more particularly to a microprocessor for a health monitor circuit that provides improved Fast Fourier Transform (FFT), improved data compression for transmission, and improved stator current signature analysis. Background Art

[0004] At least some motors include one or more sensors as part of a health monitor to periodically measure, for example, vibration, ambient temperature, current, voltage, humidity, torque, or other parameters. Such measurements are useful in determining, for example, the amount of wear and tear on the motor over time and the overall health of the motor, or in determining at least some aspects of the operation of the load while in operation. Some motors include, for example, piezoelectric accelerometers to measure vibration. Some motors include, for example, resistance temperature sensors (RTDs) embedded in the motor circuitry to monitor temperature. Such sensors can be integrated into the motor and its housing and are typically powered by a battery, power management circuitry, or power provided by other means independent of the motor itself.

[0005] Some electric motors, such as commutated electric motors (ECMs), include current sensors integrated into a controller (e.g., a motor controller) for measuring stator current to properly operate the motor. Other electric motors, such as induction motors, do not require stator current measurement for operation. Current sensors used to monitor the operation of an ECM or induction motor for diagnostic or health monitoring purposes are sometimes periodically installed to collect stator current data for analysis over a short duration. For example, an external motor current module may be installed on the motor on a quarterly basis to monitor the health of the ECM or induction motor over a period of hours, days, or other representative durations.

[0006] The data collected by the sensors can be used and stored locally on the motor, and more specifically, on a storage device integrated within the housing or integrated within a microprocessor that is itself integrated within or attached to the housing. Alternatively, the data collected by the sensors can be transmitted to a remote memory device, such as a mass storage device or a cloud server, using wired or wireless communication. In either embodiment, the performance of the health monitor circuit is often limited in terms of speed, resolution, or storage by the physical size, memory capacity, or power consumption of the health monitor circuit's microprocessor. Such health monitor circuits may be further limited in the amount of data that can be collected and transmitted for remote processing. Consequently, at least some data collection and analysis, such as stator current signature analysis (e.g., motor current signature analysis), is limited to being performed using external modules with greater processing power and storage capacity than can be integrated within the motor controller or within the health monitor circuit—which is integrated within or attached to the motor's housing. It would be desirable to improve the performance of health monitor circuits for electric motors. Summary of the Invention

[0007] In one aspect, a health monitor circuit for an electric motor is described. The health monitor circuit includes at least one sensor configured to measure a parameter of the electric motor, a communication interface, and a microprocessor coupled to the at least one sensor, the communication interface, and a memory. The microprocessor is configured to periodically collect time samples of the parameter measured by the at least one sensor, transfer factors of the time samples to the memory, and perform a high-resolution fast Fourier transform (FFT) on the factors. The microprocessor is further configured to extrapolate results of the high-resolution FFT to generate a high-resolution frequency domain waveform, filter the high-resolution frequency domain waveform based on a parameter, and transmit the filtered frequency domain waveform to a remote system via the communication interface for further processing.

[0008] In another aspect, a method for monitoring the health of an electric motor is described, wherein the electric motor includes at least one sensor configured to measure a parameter of the electric motor, a communication interface, and a microprocessor coupled to the at least one sensor, the communication interface, and a memory. The method includes periodically collecting time samples of the parameter measured by the at least one sensor, transferring factors of the time samples to a memory, and performing a high-resolution fast Fourier transform (FFT) on the factors. The method also includes extrapolating results of the high-resolution FFT to generate a high-resolution frequency domain waveform, filtering the high-resolution frequency domain waveform based on a parameter, and transferring the filtered frequency domain waveform to a remote system via the communication interface for further processing.

[0009] In yet another aspect, a health monitor system is described. The health monitor system includes a motor and a health monitor circuit coupled to the motor. The health monitor circuit includes at least one sensor configured to measure a parameter of the motor, a communication interface, and a microprocessor coupled to the at least one sensor, the communication interface, and a memory. The microprocessor is configured to periodically collect time samples of the parameter measured by the at least one sensor, transfer factors of the time samples to the memory, and perform a high-resolution fast Fourier transform (FFT) on the factors. The microprocessor is further configured to extrapolate results of the high-resolution FFT to produce a high-resolution frequency domain waveform, filter the high-resolution frequency domain waveform based on a parameter, and transmit the filtered frequency domain waveform to a remote system via the communication interface for further processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a block diagram of an exemplary electric machine having a health monitor circuit;

[0011] Figure 2 is a graph illustrating symmetry in a cosine wave and an exemplary time domain waveform data set;

[0012] Figure 3 is a graph illustrating an exemplary improvement in FFT performance of an exemplary embodiment of a health monitor circuit;

[0013] Figure 4 is a graph showing an exemplary time-domain acceleration waveform of a motor;

[0014] Figure 5 It is shown from Figure 4 A graph of an exemplary frequency domain acceleration waveform derived from the time domain acceleration waveform shown;

[0015] Figure 6 It shows Figure 5 A graph of the frequency domain acceleration waveform shown in after amplitude filtering; and

[0016] Figure 7 is a flow chart of an exemplary method of collecting motor current data for motor current signature analysis. DETAILED DESCRIPTION

[0017] Embodiments of a health monitor circuit described herein provide a health monitor circuit for an electric motor that includes a microprocessor with improved Fast Fourier Transform (FFT) performance that utilizes less memory and has higher resolution than other available alternatives. For example, the improved FFT performance is achieved by storing and manipulating symmetry factors, or "twiddle factors," of a cosine wave, and then extrapolating the FFT results to reconstruct the full waveform in the frequency domain at higher resolution. In certain embodiments, the health monitor circuit described herein includes a microprocessor with improved data compression for transmitting collected data to a remote system. The microprocessor converts the collected data to the frequency domain and applies filtering based on one or more parameters to affect the compression. For example, the frequency domain data can be reduced based on local maxima representing the most critical data within the frequency domain data. This reduction can be achieved, for example, through amplitude filtering. In certain embodiments, the health monitor circuit is disposed within the electric motor and enables high-resolution stator current signature analysis by an integrated microprocessor or a remote system. The health monitor circuits described herein include current sensing circuitry capable of periodically collecting and storing stator current measurements (as well as other measurements of operating parameters and environmental conditions) controlled by a microprocessor or other suitable processing device.

[0018] The health monitor circuits described herein include one or more sensors for detecting operating parameters and environmental conditions of the electric motor, including, for example, ambient temperature, ambient humidity, barometric pressure, acceleration, and stator current. The health monitor circuit measurements enable improved analysis and monitoring of the electrical and mechanical degradation or "wear" experienced by the electric motor, and also enable inferences about various aspects of the health of a mechanical load coupled to and driven by the motor (for electric motors) or the health of a machine or other drive coupled to and rotating the rotor (for generators). The health monitor circuits described herein may further include voltage regulation and power distribution circuitry for generating and supplying power to the various components of the health monitor circuit.

[0019] Figure 1is a block diagram of an exemplary electric machine 100 with a health monitor circuit 102. The electric machine 100 is illustrated as an electric motor including a rotor 104 and a stator 106. The rotor 104 is configured to be coupled to a mechanical load (or drive) 108. The mechanical load 108 may include, for example, a rotatable load such as a fan, wheel, blower, impeller, compressor, flywheel, transmission, or crankshaft. The mechanical load 108 may also include a linear load such as a solenoid or linear actuator. In an alternative embodiment, the rotor 104 is configured to be coupled to a machine or other drive that rotates the rotor 104 so that the electric machine 100 operates as a generator. Such a machine may include an internal combustion engine, a gas turbine, a wind turbine, a steam turbine, or any other suitable machine for rotating the rotor 104. Again referring to Figure 1 In embodiments where the electric machine 100 is an electric motor, the stator 106 typically includes one or more stator windings (not shown) that, when energized and conducting stator current, are electromagnetically coupled to the rotor 104 and cause the rotor 104 to rotate relative to the stator 106 about a longitudinal axis.

[0020] The electric motor 100 is supplied (or fed) with alternating current (AC) by an AC source 110 (or electrical load). The AC source 110 may include, for example, the electrical grid, diesel, a wind or turbine generator, or any other suitable AC source. The AC source 110 may alternatively include one or more direct current (DC) sources having an output that is converted or "inverted" to AC power before being supplied to the electric motor 100. In some embodiments, the AC power from the AC source 110 may be applied directly to the stator 106. In alternative embodiments, the electric motor 100 may be supplied with AC or DC power that is appropriately converted to AC and / or DC by the electric motor 100 itself. To this end, some embodiments of the electric motor 100 include a motor controller 112. In other embodiments, the motor controller 112 may be omitted.

[0021] The motor controller 112 typically includes one or more processors 114, one or more memory devices 116, and a drive circuit 118. Typically, the drive circuit 118 supplies power to the stator 106 of the motor 100 based on control signals received from the one or more processors 114. The drive circuit 118 may include, for example, various power electronics for conditioning the line frequency AC power so that it is supplied to the stator windings of the motor 100 at a desired current (i.e., phase and amplitude as well as frequency). Such power electronics may include, for example, but not limited to, one or more rectifier stages, power factor correction (PFC) circuits, filters, transient protection circuits, EMF protection circuits, inverters, or power semiconductors. In certain embodiments, the motor controller 112 may include a communication interface (not shown). The communication interface may include one or more wired or wireless hardware interfaces, such as a universal serial bus (USB), RS232 or other serial bus, CAN bus, Ethernet, near field communication (NFC), WiFi, Bluetooth, or any other suitable digital or analog interface for establishing one or more communication channels between the motor controller 112 and the remote system 120. Remote system 120 may include a system controller, a smartphone, a personal computer, a mass storage system, a cloud server, or any other suitable computing system. The communication interface may include, for example, a wired communication channel 122 to remote system 120 or an antenna 124 for establishing a wireless communication channel with remote system 120. The communication interface also includes a software or firmware interface for receiving one or more control parameters and writing them to, for example, a memory. In some embodiments, the communication interface includes, for example, a software application programming interface (API) for providing one or more parameters for operating motor 100.

[0022] In an alternative embodiment, the communication interface may be implemented independently of the motor controller 112 so that it serves both the motor controller 112 and the health monitor circuit 102. In another alternative embodiment, the communication interface may be integrated into the health monitor circuit 102 instead of or in addition to the motor controller 112.

[0023] The electric motor 100 may include a housing within which the rotor 104 and the stator 106 are located. The electric motor 100 may also include an electrical housing or "conduit box" within which the various electrical components of the electric motor 100, such as the motor controller 112 and the health monitor circuit 102, may be located.

[0024] Health monitor circuit 102 includes one or more microprocessors 126 and one or more sensors 128. In some embodiments, microprocessor 126 includes a processing core capable of performing at least floating-point arithmetic calculations, and in some embodiments, includes a processing core capable of performing digital signal processing. Microprocessor 126 may further include one or more analog-to-digital converters (ADCs) and typically includes a certain amount of random access memory (RAM) and a certain amount of program memory, such as read-only memory (ROM), erasable programmable read-only memory (EPROM), or electrically erasable programmable read-only memory (EEPROM). Microprocessor 126 may also include multiple input / output interfaces and one or more communication interfaces. Often, the number and / or variety of interfaces, memory, and processing bandwidth that microprocessor 126 can provide may be limited due to the specific physical size, power consumption constraints, or cost of health monitor circuit 102. For example, the housing of health monitor circuit 102 may restrict the physical footprint of microprocessor 126, resulting in microprocessor 126 being limited in ROM and RAM capacity. Such memory limitations may further limit the resolution at which microprocessor 126 performs data collection and offloading to, for example, remote system 120 via wired communication channel 122 or antenna 124, and may limit the ability of remote system 120 to perform certain analyses.

[0025] In some embodiments, the sensor 128 may include an ambient temperature sensor, an ambient humidity sensor, an atmospheric pressure sensor, or an acceleration sensor, such as a microelectromechanical system (MEMS) three-axis accelerometer. The sensor 128 may also include one or more current sensors. The sensor 128 may include any other type of sensor or device for collecting analog or digital data from the motor 100. The sensor 128 is configured to monitor various operating parameters and environmental conditions of or around the motor 100. The sensor 128 may also enable monitoring of various operating parameters of the mechanical load (or drive) 108. At least some of the sensors 128 may be mounted on the motor 100, or, for example, on the motor controller 112, and transmit the measured data back to the microprocessor 126.

[0026] Figure 22 is a chart 200 illustrating an exemplary time-domain waveform dataset. More specifically, chart 200 includes a time-domain waveform 202 represented by a plurality of time samples acquired by sensor 128 of health monitor circuit 102, which appears in the table as a full waveform dataset 204. Time-domain waveform 202 is a sinusoidal waveform, and thus each quadrant of time-domain waveform 202 shares a symmetry characteristic or factor with the next quadrant. For example, the amplitude of first quadrant 206 decreases from one to zero by a symmetry factor (i.e., rate of declination) as in second quadrant 208. Similarly, the rate of declination in second quadrant 208 is symmetric with the rate of incline in third quadrant 210, which itself is symmetric with the rate of incline in fourth quadrant 212. Therefore, this symmetry characteristic can be "factored out" of time-domain waveform 202, allowing it to be stored and manipulated as a series of factors 214—sometimes referred to as "twiddle factors"—rather than as a complete waveform dataset 204. The factor 214 is generally calculated in the form of Equation 1 below, where W is the factor, N is the resolution, and m is the exponent.

[0027]

[0028] The microprocessor 126 is configured or programmed to store and manipulate the factors 214, and then extrapolate the factors 214 to reconstruct the full waveform with a higher resolution. For example, the microprocessor 126 is programmed to perform a fast Fourier transform (FFT). Traditionally, a microprocessor may be limited to a certain maximum resolution, such as 4096 points. However, the microprocessor 126 is configured to perform the FFT using the factors 214, and then extrapolate the result to produce a higher resolution FFT, such as 16,384 points.

[0029] Figure 3 It shows that Figure 1Graph 300 illustrates an example improvement in FFT performance for an example embodiment of the health monitor circuit 102. Graph 300 includes a horizontal axis representing resolution or FFT points and a vertical axis representing time in seconds on a logarithmic scale. Graph 300 includes curve 302, which illustrates native FFT performance on a microprocessor comparable to microprocessor 126. Graph 300 also includes curve 304, which illustrates FFT performance of microprocessor 126 configured as described above and used for health monitor circuit 102. Graph 300 includes curves 306 and 308, respectively, illustrating the performance of a fast discrete Fourier transform (fDFT) and discrete Fourier transform (DFT) on a microprocessor comparable to microprocessor 126. Curve 304 illustrates the improved resolution of the FFT performed by microprocessor 126 while also only slightly increasing the computation time relative to the computation time required for the fDFT and DFT, as shown by curves 306 and 308. Likewise, program code and data storage for FFT implementation on microprocessor 126 may be stored in a portion of ROM that is a fraction (eg, approximately 1 / 5) of the portion used for native FFT performance.

[0030] Figure 4 is shown for a motor (eg Figure 1 100). Such acceleration waveforms may be collected using sensor 128, which may include, for example, a triaxial accelerometer that produces acceleration measurements on three axes (X, Y, Z). Acceleration is plotted relative to a vertical axis expressing acceleration in g's. A full time domain waveform data set for the three axes of acceleration measurement would require approximately 585 kilobytes (kB) to store in memory. The microprocessor 126 ( Figure 1 ) is configured to use the above reference Figure 2 and Figure 3 The described improved FFT implementation converts a time domain waveform into a frequency domain waveform. Figure 5 It is shown from Figure 4 Graph of an exemplary frequency-domain acceleration waveform derived from a time-domain acceleration waveform is shown. Figure 6 It shows Figure 5 The frequency domain acceleration waveform shown in FIG is a graph after amplitude filtering. The microprocessor 126 is configured to perform amplitude filtering on the frequency domain acceleration waveform to remove peaks below a configurable acceleration threshold. The resulting "compressed" frequency domain data set is several orders of magnitude smaller than the original time domain data set. For example, Figure 6The filtered frequency domain acceleration waveform shown in would require approximately 0.36 kB to store in memory. Thus, the microprocessor 126 is able to transmit the frequency domain data set to the remote system 120 for further processing over the wired communication channel 122 or via the antenna 124 in a more efficient manner.

[0031] Figure 7 7 is a flow chart of an example method 700 for collecting motor current data for motor current signature analysis. Method 700 can be implemented, for example, in electric machine 100. Health monitor circuit 102 collects 710 time-domain motor current data representing a motor current waveform. The time-domain motor current data is collected by one or more sensors 128 that measure stator current. Factors of the symmetric portion of the time-domain current waveform data are stored 720 in a memory device, such as RAM. Microprocessor 126 then performs 730 a high-resolution FFT on the factors of the time-domain waveform. Microprocessor 126 then extrapolates 740 the FFT results to produce a high-resolution frequency-domain stator current waveform. Microprocessor 126 filters 750 the frequency-domain waveform based on one or more parameters, such as amplitude. For example, by removing low-amplitude frequency content and retaining local maxima, the frequency-domain waveform data set is further compressed. Microprocessor 126 can then transmit 760 the filtered frequency-domain motor current signature to a remote system, such as remote system 120, for further processing. Remote system 120 may, for example, perform motor current signature analysis to determine, for example, the condition of the motor or to infer the operating condition of the mechanical load.

[0032] The methods and systems described herein may be implemented using computer programming or engineering techniques, including computer software, firmware, hardware, or any combination or subset thereof, where the technical effects may include at least one of: (a) improving FFT performance; (b) improving data compression for storage and / or transmission; and (c) enabling high-resolution stator current signature analysis.

[0033] In the foregoing description and the appended claims, reference is made to a number of terms having the following meanings.

[0034] As used herein, an element or step recited in the singular and preceded by the word "a" or "an" should be understood as not excluding plural elements or steps, unless such exclusion is explicitly recited. Furthermore, references to "an exemplary embodiment" or "one embodiment" of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.

[0035] "Optional" or "optionally" means that the subsequently described event or circumstance can or cannot occur, and that the description includes instances where the event occurs and instances where it does not.

[0036] Approximate language as used herein throughout the specification and claims can be used to modify any quantitative representation that is permissible to vary without causing a change in the basic function to which it is related. Therefore, a value modified by one or more terms such as "about," "approximately," and "substantially" is not limited to the precise value specified. In at least some cases, approximate language may correspond to the precision of the instrument used to measure the value. Here, as well as throughout the specification and claims, range limitations may be combined or interchanged. Such a range is determined to include all subranges contained therein unless the context or language indicates otherwise.

[0037] Some embodiments involve the use of one or more electronic processing or computing devices. As used herein, the terms "microprocessor," "processor," and "computer," and related terms such as "processing device," "computing device," and "controller" are not limited to those integrated circuits known in the art as computers, but broadly refer to processors, processing devices, controllers, general-purpose central processing units (CPUs), graphics processing units (GPUs), microcontrollers, microcomputers, programmable logic controllers (PLCs), reduced instruction set computers (RISC) processors, field programmable gate arrays (FPGAs), digital signal processing (DSP) devices, application-specific integrated circuits (ASICs), and other programmable circuits or processing devices capable of performing the functions described herein, and these terms are used interchangeably herein. The above embodiments are merely examples and are not intended to limit in any way the definitions or meanings of the terms processor, processing device, and related terms.

[0038] In the embodiments described herein, memory may include, but is not limited to, non-transitory computer-readable media such as flash memory, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). As used herein, the term "non-transitory computer-readable medium" is intended to mean any tangible computer-readable medium, including but not limited to non-transitory computer storage devices, including but not limited to volatile and non-volatile media, and removable and non-removable media, such as firmware, physical and virtual storage, CD-ROM, DVD, and any other digital source such as a network or the Internet, as well as digital devices yet to be developed, with the sole exception of temporary propagation signals. Alternatively, a floppy disk, compact disk-read-only memory (CD-ROM), magneto-optical disk (MOD), digital versatile disk (DVD), or any other computer-based device implemented in any method or technology for short-term and long-term storage of information (e.g., computer-readable instructions, data structures, program modules and submodules, or other data) may also be used. Thus, the methods described herein may be encoded as executable instructions implemented in a non-transitory computer-readable medium, such as "software" and "firmware." Furthermore, as used herein, the terms "software" and "firmware" are interchangeable and include any computer program stored in a memory for execution by a personal computer, workstation, client, or server. When executed by a processor, these instructions cause the processor to perform at least a portion of the methods described herein.

[0039] Furthermore, in the embodiments described herein, additional input channels may include, but are not limited to, computer peripherals associated with an operator interface, such as a mouse and keyboard. Alternatively, other computer peripherals may be used, including, for example, but not limited to, scanners. Furthermore, in exemplary embodiments, additional output channels may include, but are not limited to, an operator interface monitor.

[0040] The systems and methods described herein are not limited to the specific embodiments described herein, but rather, components of the systems and / or steps of the methods may be utilized independently and separately from other components and / or steps described herein.

[0041] Although specific features of various embodiments of the present disclosure may be shown in some drawings but not in others, this is for convenience only. According to the principles of the present disclosure, any feature of a drawing may be referenced and / or claimed in combination with any feature of any other drawing.

[0042] This written description uses examples to provide details about the present disclosure, including the best mode, and also to enable those skilled in the art to practice the present disclosure, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the present disclosure is defined by the claims and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

Claims

1. A health monitor circuit for an electric motor, the electric motor comprising a rotor and a stator, the health monitor circuit comprising: at least one sensor mounted on the electric machine, the at least one sensor comprising a current sensor configured to measure a stator current of the stator; Communication interface; as well as a microprocessor mounted on the motor and coupled to the at least one sensor, the communication interface, and the memory, the microprocessor being configured to: periodically collecting time domain samples of the stator current measured by at least one current sensor; transferring the factors of the time domain samples to the memory; generating a frequency domain result by performing a fast Fourier transform (FFT) on the factors retrieved from the memory; extrapolating the frequency domain result to generate a frequency domain waveform having a higher resolution than the frequency domain result; compressing the frequency domain waveform to improve data transmission by filtering the frequency domain waveform to remove peaks below a configurable threshold and retain only local maxima representing critical data within the frequency domain waveform; as well as The compressed frequency domain waveform is transmitted via the communication interface to a remote system for further processing regarding the health of the motor.

2. The health monitor circuit of claim 1 , wherein: The microprocessor is further configured to generate a time domain data set based on a factor of the time domain samples.

3. The health monitor circuit of claim 2, wherein: The microprocessor is further configured to generate a compressed data set containing data based on the compressed frequency domain waveform.

4. The health monitor circuit of claim 3, wherein: The microprocessor is further configured to generate the compressed data set in such a manner that the compressed data set is orders of magnitude smaller than the time-domain data set.

5. The health monitor circuit of claim 1 , wherein: The at least one sensor further includes at least one of an ambient temperature sensor, an ambient humidity sensor, an atmospheric pressure sensor, and an acceleration sensor.

6. The health monitor circuit of claim 1 , wherein: The microprocessor is communicatively coupled to a motor controller of the electric machine via the communication interface.

7. The health monitor circuit of claim 1 , wherein: The remote system is located external to the motor.

8. A method for monitoring the health of a motor, wherein: The electric machine includes a rotor, a stator, at least one sensor, a communication interface, and a microprocessor coupled to the at least one sensor, the communication interface, and a memory, the method comprising: periodically collecting time domain samples of stator current measured by the at least one sensor including a current sensor; transferring the factors of the time domain samples to the memory; generating a frequency domain result by performing a fast Fourier transform (FFT) on the factors retrieved from the memory; extrapolating the frequency domain result to generate a frequency domain waveform having a higher resolution than the frequency domain result; compressing the frequency domain waveform to improve data transmission by filtering the frequency domain waveform to remove peaks below a configurable threshold and retain only local maxima representing critical data within the frequency domain waveform; and The compressed frequency domain waveform is transmitted via the communication interface to a remote system for further processing regarding the health of the motor. 9 . The method of claim 8 , further comprising generating a time-domain dataset based on a factor of the time-domain samples.

10. The method of claim 9, further comprising generating a compressed data set containing data based on the compressed frequency domain waveform.

11. The method of claim 10, further comprising generating the compressed dataset in such a manner that the compressed dataset is several orders of magnitude smaller than the time-domain dataset.

12. The method according to claim 8, wherein The at least one sensor further includes at least one of an ambient temperature sensor, an ambient humidity sensor, an atmospheric pressure sensor, and an acceleration sensor.

13. A health status monitor system comprising: an electric motor comprising a rotor and a stator; as well as a health monitor circuit coupled to the motor, the health monitor circuit comprising: at least one sensor, the at least one sensor comprising a current sensor configured to measure a stator current of the electric machine; a communication interface; and a microprocessor mounted on the motor and coupled to the at least one sensor, the communication interface, and the memory, the microprocessor being configured to: periodically collecting time domain samples of the stator current measured by the current sensor; transferring the factors of the time domain samples to the memory; generating a frequency domain result by performing a fast Fourier transform (FFT) on the factors retrieved from the memory; extrapolating the frequency domain result to generate a frequency domain waveform having a higher resolution than the frequency domain result; compressing the frequency domain waveform to improve data transmission by filtering the frequency domain waveform to remove peaks below a configurable threshold and retain only local maxima representing critical data within the frequency domain waveform; and The compressed frequency domain waveform is transmitted via the communication interface to a remote system for further processing regarding the health of the motor.

14. The health monitor system of claim 13, wherein: The microprocessor is further configured to generate a time domain data set based on a factor of the time domain samples.

15. The health monitor system of claim 14, wherein: The microprocessor is further configured to generate a compressed data set containing data based on the compressed frequency domain waveform.

16. The health monitor system of claim 15, wherein: The microprocessor is further configured to generate the compressed data set in such a manner that the compressed data set is orders of magnitude smaller than the time-domain data set.

17. The health monitor system of claim 13, wherein: The at least one sensor further includes at least one of an ambient temperature sensor, an ambient humidity sensor, an atmospheric pressure sensor, and an acceleration sensor.

18. The health monitor system of claim 13, wherein: The microprocessor is communicatively coupled to a motor controller of the electric machine via the communication interface.

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