A method for monitoring the running state of a blower based on running capacitor voltage detection
By using a method based on operating capacitor voltage detection, the problems of high-voltage contact safety, physical characterization lag, and high edge computing power in blower monitoring are solved, achieving zero-latency fault early warning and low-cost real-time monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ANHUI BRANCH OF EAST CHINA AIR TRAFFIC ADMINISTRATION OF CIVIL AVIATION OF CHINA
- Filing Date
- 2026-03-18
- Publication Date
- 2026-05-29
AI Technical Summary
Existing blower monitoring methods suffer from problems such as high-voltage contact safety hazards, lagging physical characterization diagnosis, excessively high computational power requirements for edge computing algorithms, and the tendency of traditional motor current characteristic analysis methods to generate false alarms.
A monitoring method based on the detection of running capacitor voltage is adopted. Through a signal processing circuit consisting of a rectifier bridge, a series resistor module, a closed-loop Hall voltage sensor and a main control module, electrical isolation between 220V high voltage and 3.3V low voltage is achieved. Real-time voltage characteristic value calculation is performed through a time-domain electromechanical coupling model and an analog-to-digital conversion unit to achieve zero-delay fault early warning.
It enables precise monitoring of the blower's operating status, avoids the risk of high voltage intrusion burning out equipment and electric shock, reduces hardware costs, has anti-interference capabilities and real-time response speed, and is easy to deploy on a large scale in industry.
Smart Images
Figure CN122106918A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of online monitoring technology for industrial equipment status, and particularly to a method for monitoring the operating status of blowers based on the detection of operating capacitor voltage. It is mainly applied to the maintenance of power units in equipment such as aviation meteorological monitoring and industrial ventilation systems. Background Technology
[0002] In industrial automation, aviation meteorological monitoring (such as blowers protecting the lenses of airport runway visibility instruments), and various ventilation support equipment, single-phase capacitor-run asynchronous motors are widely used as core power units due to their simple structure, low cost, and reliable operation. Since single-phase alternating current cannot generate a rotating magnetic field on its own, these blower motors must have a running capacitor with phase-shifting function connected in series in the stator auxiliary winding. This running capacitor generates a phase-leading current, enabling the main and auxiliary windings to collaboratively establish a circular magnetic field vector that drives the rotor to rotate. Therefore, the running capacitor not only determines the motor's operating performance but also participates in the motor's steady-state energy conversion throughout the entire process, making it a core electrical component for maintaining the blower's power output and electromagnetic stability.
[0003] However, as a vulnerable component in the motor system, the running capacitor is highly susceptible to thermal aging or electrochemical damage under long-term high-load operation and complex working conditions. When the running capacitor fails, it causes a change in the impedance of the secondary winding circuit, which in turn disrupts the symmetry of the air gap magnetic field, causing the blower to fail to rotate normally.
[0004] Although the health of the operating capacitors and underlying drivers is crucial to the equipment, current industry methods for monitoring the condition of such blowers still have significant limitations. Specific literature support and technological deficiencies are manifested in the following aspects: Titled "Research on Common Fault Identification and Handling Technology of Roots Blowers," by Zhang Qingxiang, *Petrochemical Technology*, Vol. 32, No. 1, 2025, pp. 389-390. The article uses traditional methods such as listening for abnormal knocking sounds, observing insufficient airflow, and monitoring casing temperature to identify and shut down blower rotor wear, motor overload, and component overheating for repair. However, this method has significant shortcomings: first, it heavily relies on manual inspection and subjective experience, making it difficult to achieve 24-hour unattended online automated monitoring; second, it is a reactive fault handling mode, often intervening only when the equipment has already experienced serious faults or abnormal shutdowns, lacking early warning capabilities; and third, the shutdown and disassembly repair method is time-consuming and labor-intensive, seriously affecting the continuity of industrial production and economic efficiency.
[0005] Titled "Design of Fault Monitoring System for Air-Suspension Centrifugal Blower Based on Health Assessment," by Liu Qun, Master's Thesis, Dalian University of Technology, 2022. This paper uses eddy current displacement sensors to collect rotor vibration signals and combines ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) to monitor and diagnose blower shaft system faults. However, this method has significant shortcomings: First, the vibration sensors are extremely sensitive to their installation location, and non-contact measurements are easily affected by complex mechanical background noise in industrial environments; second, complex signal decomposition algorithms such as EEMD have high computational demands, making it difficult to meet the low-computing-power real-time processing requirements of edge devices; third, pure mechanical vibration monitoring has a significant lag, making it difficult to accurately capture and provide early warnings before early electrical faults such as capacitor aging cause severe mechanical vibrations.
[0006] The article, titled "Diagnostics Insights on Single and Three-Phase Induction Motors in Healthy and Single-Phasing Conditions," by Marcello Minervini et al., published in *IEEE Transactions on Industry Applications*, Vol. 61, No. 5, pp. 6941-6950, 2025, describes a method using motor current characteristic analysis (MCSA) to monitor harmonic bands around the power supply frequency in the stator current spectrum to identify the operating status and faults of single-phase capacitor-run motors. However, this method has significant drawbacks: first, the fault diagnosis threshold drifts severely under different operating conditions, rendering the fixed threshold standard of traditional theory completely ineffective; second, it is highly susceptible to load fluctuations, with low-frequency torque oscillations (LFTO) caused by mechanical equipment easily leading to false alarms; and third, signal analysis is easily masked by the inherent characteristics of the motor, with the high amplitude third harmonic and complex magnetic field distortion unique to single-phase motors making it difficult to accurately isolate weak early fault features from a single current signal.
[0007] The article titled "Abnormal temperature detection of blower components based on infrared video images analysis," by Zheng S et al., published in *IEEE Sensors Journal*, Vol. 24, No. 2, 2023, pp. 1919-1928, describes a method for processing infrared video images of blowers using an improved semantic segmentation network and employing a hierarchical strategy for temperature anomaly detection and thermal fault early warning. However, this method has significant drawbacks: firstly, it heavily relies on a single infrared mode, making it prone to misjudgment when there are environmental spurious heat sources; secondly, the inherently low resolution of infrared images limits segmentation accuracy at points where blower components are tightly adhered; and thirdly, it has poor robustness to harsh environmental conditions, easily leading to data quality degradation under complex operating conditions. Additionally, the article titled "MCNet: Multi-level correction network for thermalimage semantic segmentation of nighttime driving scene," by Xiong H et al., published in *Infrared Physics & Technology*, Vol. 113, pp. 103628, 2021, describes a method for semantic segmentation of thermal images using a multi-level attention correction network to extract target features. However, this method also has significant shortcomings: First, processing images using multi-level attention modules leads to extremely high computational complexity, making it difficult to meet the lightweight requirements of online monitoring of industrial blowers; second, it lacks a multi-modal fusion mechanism, making it unable to effectively utilize the clear contour information of visible light images; and third, it has insufficient noise resistance, and the segmentation accuracy will significantly decrease when facing strong noise interference commonly found in industrial environments.
[0008] In summary, the following problems still exist in the existing technology: 1. The contradiction between direct and accurate measurement and strong electrical isolation safety: Directly measuring the voltage at the capacitor terminal is the most accurate way to diagnose faults, but traditional resistor voltage divider circuits lack physical isolation between strong and weak currents, which can easily lead to AC high voltage above 220V and resonance peaks entering and breaking down the low-voltage MCU control board. Manual measurement is accompanied by an extremely high risk of electric shock.
[0009] 2. The contradiction between early real-time warning and physical characterization lag: Existing mechanical vibration and thermal field imaging monitoring are all external physical characterizations in the middle and late stages of faults, with serious mechanical conduction lag and thermal lag, which makes it impossible to achieve zero-delay real-time capture when the operating capacitor undergoes early electrical degradation.
[0010] 3. The contradiction between high diagnostic immunity and low edge computing power: In order to eliminate complex industrial interference, existing technologies are forced to introduce huge deep learning visual networks or complex joint time-frequency decomposition algorithms, which greatly increases the computing power threshold and deployment cost of edge monitoring equipment. Summary of the Invention
[0011] The technical problem to be solved by this invention is the high-voltage contact safety hazard, the lag in physical characterization diagnosis, the excessively high computing power requirements of edge-end algorithms, and the fact that the traditional motor current characteristic analysis (MCSA) method is prone to false alarms due to the inherent harmonic interference of single-phase motors in existing blower monitoring methods.
[0012] The technical solution of the present invention is as follows.
[0013] A method for monitoring the operating status of a blower based on the detection of a running capacitor voltage, wherein the blower is driven by a single-phase capacitor-run asynchronous motor, and a running capacitor for phase shifting and participating in steady-state operation throughout the auxiliary winding of the single-phase capacitor-run asynchronous motor is connected in series; the electrical topology involved in the monitoring method includes a running capacitor and a status monitoring circuit, the status monitoring circuit being connected in parallel across the two ends of the running capacitor, and the status monitoring circuit consisting of a rectifier bridge, a series resistor module, a closed-loop Hall voltage sensor, and a main control module cascaded and grounded to form a signal processing loop, the main control module including an analog-to-digital converter unit; the monitoring method includes the following steps: Step 1: Acquire the real-time voltage across the operating capacitor using the rectifier bridge. And convert it into DC pulsating voltage. , Establish a time-domain electromechanical coupling model, and let the real-time voltage Satisfies the time-domain electromechanical coupling model; Step 2, the DC pulsating voltage The signal is input to the series resistor module, then passes through a closed-loop Hall voltage sensor and is converted into a low-voltage DC analog signal via an internal sampling resistor. And low-voltage DC analog signal DC pulsating voltage Effective resistance value of series resistor module satisfy: , It is the inherent conversion constant of voltage; Step 3: The analog-to-digital conversion unit samples the low-voltage DC analog signal at a set continuous sampling frequency. conduct Each sampling session acquires one discrete digital value; the values acquired through continuous sampling... Each discrete digital value is stored in a cache array, and then the characteristic value of the real-time voltage of the currently running capacitor is calculated by the digital signal processing algorithm built into the analog-to-digital converter unit. ; Step 4: Given the normal operating voltage reference threshold V, perform the following real-time judgment: when When the blower is in an abnormal operating state, a local status alarm signal is immediately triggered.
[0014] Preferably, the expression for the time-domain electromechanical coupling model in step 1 is:
[0015] in, The angular frequency of the power supply. The instantaneous phase angle, Sampling time; This is the power supply reference voltage component. For the rotational induced electromotive force, the power supply reference voltage component and rotational induced electromotive force The expressions are as follows:
[0016]
[0017] In the formula, This is the peak voltage of the external input power supply; The branch distribution coefficient is used to characterize the static voltage sharing characteristics of the series circuit formed by the stator auxiliary winding and the running capacitor of the motor. This refers to the real-time rotational speed of the blower rotor; The magnetoelectric coupling coefficient; This refers to the main magnetic flux in the air gap between the stator and rotor; The phase feedback correction coefficient between the main and auxiliary windings.
[0018] Preferably, the branch distribution coefficient The value range is: when the operating capacitor is running normally or aging, When a running capacitor fails and causes an open circuit fault, The value is 1.
[0019] Preferably, the method described in step 3 is... Any one of the sampling times is denoted as the nth sampling time. The second sampling, the first The discrete digital quantity obtained from the sampling is denoted as . , The characteristic value of the real-time voltage of the currently operating capacitor. The formula for calculation is:
[0020] in, is the system reference voltage of the analog-to-digital converter in the main control module; m is the number of bits for the hardware conversion accuracy of the analog-to-digital converter.
[0021] Compared with the prior art, the present invention has the following significant advantages: 1. Extremely high safety and anti-interference capability: This invention abandons the traditional direct resistor voltage divider scheme and innovatively adopts a hardware architecture of "adjustable resistor primary voltage reduction + closed-loop Hall sensor magnetic isolation". Utilizing the magnetic balance principle of the Hall effect, complete electrical isolation is achieved between the 220V high-voltage main circuit and the 3.3V low-voltage control circuit, fundamentally eliminating the risk of high voltage intrusion burning out monitoring equipment and electric shock. Simultaneously, the closed-loop Hall sensor has excellent frequency response characteristics and anti-electromagnetic interference capabilities, making it suitable for harsh industrial environments.
[0022] 2. Precise diagnosis based on physical principles, without hysteresis: Unlike vibration monitoring (mechanical hysteresis) and thermal imaging monitoring, this invention directly captures the real-time voltage across the operating capacitor. This invention monitors the real-time voltage across the running capacitor when the blower speed decreases due to bearing wear, excessive load, or aging of the running capacitor. The system can react within milliseconds of a fault occurring, achieving true zero-delay real-time early warning and effectively preventing motor stalling and burnout caused by capacitor failure.
[0023] 3. Lightweight algorithm and low edge deployment cost: Compared to monitoring methods that introduce deep learning visual networks or complex time-frequency domain transformations (such as wavelet transform and EEMD), the "high-frequency ADC oversampling + RMS root mean square calculation" algorithm adopted in this invention has extremely low computational complexity and requires very little computing power from the microcontroller. This not only reduces hardware costs but also ensures the real-time response speed of the system, facilitating large-scale industrial deployment. Attached Figure Description
[0024] Figure 1 This is a flowchart of the present invention.
[0025] Figure 2 This is the electrical topology diagram involved in the present invention.
[0026] Figure 3 This is a flowchart of the main control module software of the present invention.
[0027] Figure 4 This is a comparison chart showing the healthy and faulty states of a capacitor detected using this invention. Detailed Implementation
[0028] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0029] Figure 2This is the circuit connection diagram of the present invention, by Figure 2 As can be seen, the electrical topology involved in the monitoring method includes a running capacitor and a status monitoring circuit. The status monitoring circuit is connected in parallel across the two ends of the running capacitor. The status monitoring circuit consists of a rectifier bridge, a series resistor module, a closed-loop Hall voltage sensor, and a main control module cascaded in sequence and grounded together to form a signal processing loop. The main control module includes an analog-to-digital conversion unit.
[0030] Figure 1 The flowchart of this invention shows that the invention provides a method for monitoring the operating status of a blower based on the detection of the operating capacitor voltage. The blower is driven by a single-phase capacitor-driven asynchronous motor, and the auxiliary winding of the single-phase capacitor-driven asynchronous motor is connected in series with an operating capacitor for phase shifting and participating in steady-state operation throughout the process.
[0031] Example 1.
[0032] The monitoring method includes the following steps: Step 1: Acquire the real-time voltage across the operating capacitor using the rectifier bridge. And convert it into DC pulsating voltage. , Establish a time-domain electromechanical coupling model, and let the real-time voltage It satisfies the time-domain electromechanical coupling model.
[0033] In this embodiment, the expression for the time-domain electromechanical coupling model is:
[0034] in, The angular frequency of the power supply. The instantaneous phase angle is t, where t is the sampling time. This is the power supply reference voltage component. For the rotational induced electromotive force, the power supply reference voltage component and rotational induced electromotive force The expressions are as follows:
[0035]
[0036] In the formula, This is the peak voltage of the external input power supply; The branch distribution coefficient is used to characterize the static voltage sharing characteristics of the series circuit formed by the stator auxiliary winding and the running capacitor of the motor. This refers to the real-time rotational speed of the blower rotor; The magnetoelectric coupling coefficient; This refers to the main magnetic flux in the air gap between the stator and rotor; The phase feedback correction coefficient between the main and auxiliary windings.
[0037] In this embodiment, the branch distribution coefficient The value range is: when the operating capacitor is running normally or aging, When a running capacitor fails and causes an open circuit fault, The value is 1.
[0038] Branch distribution coefficient This characteristic is used to characterize the static voltage sharing of the series circuit formed by the stator auxiliary winding and the running capacitor of the motor. Its value depends on the circuit impedance distribution. Therefore, it varies depending on whether the capacitor is operating normally or aging. When a capacitor fails and an open circuit fault occurs, the circuit current returns to zero and the winding voltage drop disappears. The value is 1.
[0039] Step 2, the DC pulsating voltage The signal is input to the series resistor module, then passes through the Hall voltage sensor and is converted into a low-voltage DC analog signal by the internal sampling resistor. And low-voltage DC analog signal DC pulsating voltage Effective resistance value of series resistor module satisfy: , This is the inherent voltage conversion constant.
[0040] Step 3: The analog-to-digital conversion unit samples the low-voltage DC analog signal at a set continuous sampling frequency. Perform N samplings, with each sampling yielding one discrete digital value; convert the continuously sampled values... Each discrete digital value is stored in a cache array, and then the characteristic value of the real-time voltage of the currently running capacitor is calculated by the digital signal processing algorithm built into the analog-to-digital converter unit. .
[0041] In this embodiment, any one of the N samplings described in step 3 is denoted as the nth sampling. The second sampling, the first The discrete digital quantity obtained from the sampling is denoted as . , The characteristic value of the real-time voltage of the currently operating capacitor. The formula for calculation is:
[0042] in, is the system reference voltage of the analog-to-digital converter in the main control module; m is the number of bits for the hardware conversion accuracy of the analog-to-digital converter.
[0043] Step 4, give the normal operating voltage reference threshold. And perform the following real-time judgments: when When the blower is in an abnormal operating state, a local status alarm signal is immediately triggered.
[0044] Figure 3 The main control module processes low-voltage DC analog signals. The data processing flow for sampling.
[0045] Example 2.
[0046] The monitoring steps and data processing procedures are the same as in Example 1.
[0047] In this embodiment, for the lens protection blower of the airport runway visibility instrument, the system uses an effective value of AC mains power supply, peak voltage of external input power supply Approximately The system presets the real-time speed of the blower rotor. for Magnetoelectric coupling coefficient for Main magnetic flux for Phase feedback coefficient for .
[0048] When the blower is operating at its rated speed in a healthy state, the impedance distribution of the auxiliary circuit is normal, and the coefficient characterizing the branch voltage division is [not specified]. Approximately At this moment, the amplitude of the rotational induced electromotive force generated in the secondary winding by the high-speed cutting magnetic field of the rotor is... for The dynamic characteristic term is vector-superimposed with the static voltage divider term, and the real-time voltage across the operating capacitor can be calculated at this time. The peak value of the waveform envelope is approximately The real-time instantaneous waveform exhibits a significant high-level resonance rise characteristic, with its peak value far exceeding the peak value of the mains power supply.
[0049] When the operating capacitor experiences an open-circuit fault due to insulation breakdown or lead burnout, causing the secondary winding to lose phase-shifting current and the blower to stop, the rotational induced electromotive force... sudden drop to Meanwhile, affected by the open-circuit physical effect, the branch distribution coefficient for At this point, according to the mathematical model, the real-time voltage across the capacitor is measured. The waveforms of the mains power supply for the blower completely overlap.
[0050] The CHV-25P / 400 sensor and corresponding peripheral circuitry were selected, and the inherent voltage conversion constant was determined through experimental calibration. Values The effective resistance value of the series resistor module used in this circuit Values .
[0051] Select hardware conversion precision bit depth m as 12, and the number of samples... The system reference voltage of the analog-to-digital converter unit in the main control module is 8000. It is 3.3V.
[0052] To demonstrate the technical effects of this invention, a structure as follows is constructed. Figure 2 The circuit topology diagram shown is used to analyze the characteristic values of the real-time voltage of the current operating capacitor under different operating states. and low-voltage DC analog signals Conduct testing.
[0053] Figure 4 This is a comparison chart showing the healthy and faulty states of a capacitor detected using this invention. Figure 4 As can be seen, under normal blower speed conditions, the operating capacitor voltage outputs a low-voltage DC analog signal through the rectifier bridge, series resistor module, and closed-loop Hall voltage sensor. The peak voltage is 3.11V at normal blower speed, which is the characteristic value of the real-time voltage of the currently operating capacitor calculated by the main control module. The voltage is 1.72V, representing a low-voltage DC analog signal when the blower malfunctions and runs idle. The peak voltage drops to 2.56V, which is the characteristic value of the real-time voltage of the currently operating capacitor. The voltage dropped to 1.39V. This is the characteristic value of the current operating capacitor's real-time voltage calculated by the main control module. The value can effectively reflect the current operating status of the blower, verifying the effectiveness of the blower operating status monitoring method based on operating capacitor voltage detection proposed in this invention.
[0054] In summary, this invention provides a method for monitoring the operating status of a blower based on the detection of the running capacitor voltage. Its core mechanism lies in the following: when the running capacitor is working normally, the blower rotor speed is normal, and the rotor cuts the magnetic field generated by the main winding, inducing a rotating induced electromotive force in the auxiliary winding. A rotational induced electromotive force is generated. Real-time speed of blower rotor It satisfies a direct proportional function relationship. Because the LC quasi-resonant circuit formed by the running capacitor circuit and the secondary winding has a positive charge feedback effect on the rotor magnetic field, the real-time voltage across the running capacitor... Essentially composed of the power supply reference voltage component With the rotational induced electromotive force It is formed by the superposition of vectors. Therefore, the real-time voltage across the capacitor is... Higher than the power supply voltage of the blower.
[0055] When the blower rotor speed drops to zero due to a damaged operating capacitor, the branch distribution coefficient... The value is 1. This directly reduces the voltage superposition component at the capacitor terminals, triggering the real-time voltage across the capacitor. The voltage drop corresponds to the blower's power supply voltage. This invention captures the real-time voltage across the operating capacitor. This characteristic fluctuation of the effective value enables accurate monitoring of the blower's operating status.
Claims
1. A method for monitoring the operating status of a blower based on the detection of operating capacitor voltage, characterized in that, The blower is driven by a single-phase capacitor-driven asynchronous motor. A running capacitor, used for phase shifting and participating in steady-state operation throughout the entire process, is connected in series in the auxiliary winding of the single-phase capacitor-driven asynchronous motor. The electrical topology involved in the monitoring method includes a running capacitor and a status monitoring circuit. The status monitoring circuit is connected in parallel across the two ends of the running capacitor. The status monitoring circuit consists of a rectifier bridge, a series resistor module, a closed-loop Hall voltage sensor, and a main control module cascaded and grounded to form a signal processing loop. The main control module includes an analog-to-digital converter unit. The monitoring method includes the following steps: Step 1: Acquire the real-time voltage across the operating capacitor using the rectifier bridge. And convert it into DC pulsating voltage. , Establish a time-domain electromechanical coupling model, and let the real-time voltage Satisfies the time-domain electromechanical coupling model; Step 2, the DC pulsating voltage The signal is input to the series resistor module, then passes through a closed-loop Hall voltage sensor and is converted into a low-voltage DC analog signal via an internal sampling resistor. And low-voltage DC analog signal DC pulsating voltage Effective resistance value of series resistor module satisfy: , It is the inherent conversion constant of voltage; Step 3: The analog-to-digital conversion unit samples the low-voltage DC analog signal at a set continuous sampling frequency. conduct Each sampling session acquires one discrete digital value; the values acquired through continuous sampling... Each discrete digital value is stored in a cache array, and then the characteristic value of the real-time voltage of the currently running capacitor is calculated by the digital signal processing algorithm built into the analog-to-digital converter unit. ; Step 4: Given the normal operating voltage reference threshold V, perform the following real-time judgment: when When the blower is in an abnormal operating state, a local status alarm signal is immediately triggered.
2. The method for monitoring the operating status of a blower based on the detection of operating capacitor voltage according to claim 1, characterized in that, The expression for the time-domain electromechanical coupling model described in step 1 is: in, The angular frequency of the power supply. The instantaneous phase angle, Sampling time; This is the power supply reference voltage component. For the rotational induced electromotive force, the power supply reference voltage component and rotational induced electromotive force The expressions are as follows: In the formula, This is the peak voltage of the external input power supply; The branch distribution coefficient is used to characterize the static voltage sharing characteristics of the series circuit formed by the stator auxiliary winding and the running capacitor of the motor on the power supply. This refers to the real-time rotational speed of the blower rotor; The magnetoelectric coupling coefficient; This refers to the main magnetic flux in the air gap between the stator and rotor; The phase feedback correction coefficient between the main and auxiliary windings.
3. The method for monitoring the operating status of a blower based on the detection of operating capacitor voltage according to claim 2, characterized in that, The branch distribution coefficient The value range is: when the operating capacitor is running normally or aging, When an open circuit fault occurs due to capacitor failure, The value is 1.
4. The method for monitoring the operating status of a blower based on the detection of operating capacitor voltage according to claim 1, characterized in that, The steps described in step 3 Any one of the sampling times is denoted as the nth sampling time. The second sampling, the first The discrete digital quantity obtained from the sampling is denoted as . , The characteristic value of the real-time voltage of the currently operating capacitor. The formula for calculation is: in, is the system reference voltage of the analog-to-digital converter in the main control module; m is the number of bits for the hardware conversion accuracy of the analog-to-digital converter.