Rotating equipment unit speed extraction method and system based on internet of things, and medium

Abstract

This invention discloses a method, system, and storage medium for extracting the rotational speed of rotating equipment units based on the Internet of Things (IoT). The method includes: acquiring vibration acceleration signals from motor measuring points and calculating the corresponding vibration velocity signals; performing Fast Fourier Transform (FFT) processing on the vibration acceleration and velocity signals; finding the frequency with the largest amplitude in the frequency domain and verifying its accuracy, using this frequency as the extracted rotational frequency of the motor, dynamically adjusting the frequency center position and step size during the search process; calculating the extracted rotational speed of the motor based on the correspondence between rotational speed and rotational frequency; obtaining the transmission ratio of each rotating device within the unit based on the unit structure; and calculating the extracted rotational speed of each device in the unit using the extracted rotational speed and transmission ratio of the motor. This invention improves the anti-interference capability and accuracy of rotational speed extraction, and can be used for fault diagnosis, remaining life calculation, and maintenance recommendations for rotating units, reducing the false positive rate.

Description

Technical Field

[0001] This invention relates to the field of data analysis, and more specifically, to a method, system, and storage medium for accurately extracting the rotational speed of each rotating device in a generator set based on a synchronous data acquisition strategy using IoT-based intelligent vibration sensors and the transmission ratio of generator set equipment. Background Technology

[0002] With the rapid development of modern industry, various rotating mechanical equipment is widely used in various industrial fields. The normal operation of rotating machinery is an important guarantee for the normal and safe production of enterprises. Rotating machinery is becoming increasingly large and complex, and once a failure occurs, the production losses and safety risks can be very serious. Therefore, the condition monitoring of rotating machinery has become increasingly important. Among them, vibration monitoring, as an effective tool for condition monitoring and fault diagnosis of rotating machinery, has been widely accepted and applied.

[0003] The rotational speed of mechanical equipment is crucial for its condition monitoring and fault diagnosis, forming the basis for many subsequent vibration parameter extraction and fault analysis methods. For example, even with the same equipment health condition, the vibration RMS value will exhibit trend fluctuations caused by speed variations under different speed conditions. Furthermore, fault characteristic frequencies are also calculated based on the equipment's speed. However, measuring the speed signal of rotating equipment in industrial units is not easy. Many devices lack tachometers or other speed measurement devices, and subsequent installation is extremely difficult. In recent years, with the development of IoT wireless intelligent vibration sensors, wireless vibration sensors have been increasingly widely used in the monitoring and fault diagnosis of rotating equipment in industrial settings. However, wireless vibration sensors cannot directly measure speed signals; even if a speed signal compatible with a wireless vibration sensor exists on-site, synchronization between them remains a significant challenge for the industry. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method, system, and storage medium for extracting the rotational speed of rotating equipment units based on the Internet of Things.

[0005] The first aspect of this invention provides a method for extracting the rotational speed of a rotating equipment unit based on the Internet of Things, comprising:

[0006] The vibration acceleration signal of the motor measuring point is obtained by synchronous data acquisition strategy of IoT smart vibration sensor, the vibration acceleration signal is integrated to obtain vibration velocity signal, and the vibration acceleration signal and vibration velocity signal are processed by fast Fourier transform.

[0007] Based on the rated speed of the motor, the motor speed range positioning spectrum is determined. Within the positioning spectrum of the motor speed range, the frequency center position and step size are dynamically adjusted to find the frequency with the largest amplitude value for calculating the extracted motor frequency. After accuracy verification, it is used as the extracted motor frequency.

[0008] Based on the relationship between rotational speed and rotational frequency, the extracted rotational speed of the motor is calculated;

[0009] Based on the unit structure, the transmission ratio of each rotating device within the unit is obtained. By using the extraction speed of the motor and the transmission ratio, the extraction speed of each device in the unit is calculated.

[0010] In this solution, the strategy for synchronous data acquisition via IoT smart vibration sensors is specifically as follows:

[0011] Sensors at multiple measuring points on the same unit are grouped together using grouping technology. The initial wake-up time and wake-up interval of all sensors in each group are pre-configured and sent to all IoT smart vibration sensors in the group.

[0012] The main processor calculates the ideal wake-up time and configures the real-time clock to determine the time required for the next wake-up, thereby implementing the main processor's timed wake-up mechanism through the real-time clock.

[0013] After the main processor is woken up, it connects to the network time server to adjust the time and calculates the difference between the ideal wake-up time and the actual wake-up time.

[0014] If the difference between the ideal wake-up time and the actual wake-up time is greater than a preset time difference threshold, the main processor continues to sleep and calculates the duration of the continued sleep. If the difference is not greater, data is collected and uploaded through IoT smart sensors.

[0015] In this solution, the fast Fourier transform processing of the vibration velocity signal specifically involves:

[0016] The vibration acceleration signal of the motor of the rotating equipment unit to be measured is acquired, the vibration acceleration signal is integrated to obtain the vibration velocity signal, and the vibration acceleration signal and the vibration velocity signal are subjected to fast Fourier transform.

[0017] Based on the rated speed of the motor, the motor frequency search range is estimated and the frequency center position and step are dynamically adjusted to perform frequency search for motor frequency calculation. The frequency corresponding to the maximum amplitude within the motor frequency search range in the spectrum is obtained as the frequency for motor frequency extraction calculation, and the motor extraction frequency is calculated.

[0018] The accuracy of the extracted frequency is checked. If the check is passed, the extracted speed of the motor is calculated according to the conversion formula between the extracted frequency and the motor speed. If the check is not passed, the vibration acceleration signal of the motor of the measured rotating equipment unit is reacquired.

[0019] The formula for the Fast Fourier Transform is:

[0020]

[0021] Where x(n) is the nth data point of the vibration velocity signal, N is the total number of data points of the vibration velocity signal, and X(k) is the kth data point after the fast Fourier transform.

[0022] In this solution, the calculation of the extracted rotational frequency of the motor is specifically as follows:

[0023] If the vibration acceleration information of the motor measuring point of the rotating equipment unit is multidimensional, then the average value of the extracted rotational frequency in all dimensions is obtained, and the average value is used as the extracted rotational frequency of the motor measuring point.

[0024] If the motor of the rotating equipment unit being tested has multiple motor measurement points, the average value of the extracted rotational frequencies of the multiple motor measurement points shall be taken as the extracted rotational frequency of the motor of the rotating equipment unit being tested.

[0025] In this solution, the accuracy verification of the extracted frequency conversion specifically includes:

[0026] Obtain the minimum and maximum values ​​of the motor rotation frequency of the measured rotating equipment unit, and calculate the ratio of the difference between the minimum and maximum values;

[0027] If the phase difference ratio is within the preset phase difference ratio range, the accuracy of the motor frequency extraction of the measured rotating equipment unit is deemed to have passed the review; otherwise, the accuracy of the motor frequency extraction of the measured rotating equipment unit is deemed to have failed the review.

[0028] This solution also includes the extraction of the rotational speed of non-motor equipment within the unit, specifically:

[0029] The vibration acceleration signal of the non-motor equipment of the unit under test is obtained by using the synchronous data acquisition strategy of IoT smart vibration sensor;

[0030] The motors belonging to the same unit are located based on the sensor grouping, and the extraction speed of the motors is obtained;

[0031] The transmission ratio of the non-motorized equipment within the unit is obtained based on the unit structure, and the extraction speed of the non-motorized equipment is obtained through the transmission ratio of the non-motorized equipment within the unit.

[0032] A second aspect of the present invention also provides an Internet of Things (IoT)-based rotating equipment unit speed extraction system. The system includes a memory and a processor. The memory includes an IoT-based rotating equipment unit speed extraction method program. When executed by the processor, the IoT-based rotating equipment unit speed extraction method program performs the following steps:

[0033] The vibration acceleration signal of the motor measuring point is obtained by synchronous data acquisition strategy of IoT smart vibration sensor, the vibration acceleration signal is integrated to obtain vibration velocity signal, and the vibration acceleration signal and vibration velocity signal are processed by fast Fourier transform.

[0034] Based on the rated speed of the motor, the positioning spectrum of the motor speed range is determined. The center position and step size of the positioning spectrum of the motor speed range are dynamically adjusted to find the frequency with the largest amplitude value for calculating the extracted motor frequency. After accuracy verification, it is used as the extracted motor frequency.

[0035] Based on the relationship between rotational speed and rotational frequency, the extracted rotational speed of the motor is calculated;

[0036] Based on the unit structure, the transmission ratio of each rotating device within the unit is obtained. By using the extraction speed of the motor and the transmission ratio, the extraction speed of each device in the unit is calculated.

[0037] In this solution, the strategy for synchronous data acquisition via IoT smart vibration sensors is specifically as follows:

[0038] Sensors at multiple measuring points on the same unit are grouped together using grouping technology. The initial wake-up time and wake-up interval of all sensors in each group are pre-configured and sent to all IoT smart vibration sensors in the group.

[0039] The main processor calculates the ideal wake-up time and configures the real-time clock to determine the time required for the next wake-up, thereby implementing the main processor's timed wake-up mechanism through the real-time clock.

[0040] After the main processor is woken up, it connects to the network time server to adjust the time and calculates the difference between the ideal wake-up time and the actual wake-up time.

[0041] If the difference between the ideal wake-up time and the actual wake-up time is greater than a preset time difference threshold, the main processor continues to sleep and calculates the duration of the continued sleep. If the difference is not greater, data is collected and uploaded through IoT smart sensors.

[0042] In this solution, the fast Fourier transform processing of the vibration velocity signal specifically involves:

[0043] The vibration acceleration signal of the motor of the rotating equipment unit to be measured is acquired, the vibration acceleration signal is integrated to obtain the vibration velocity signal, and the vibration acceleration signal and the vibration velocity signal are subjected to fast Fourier transform.

[0044] Based on the rated speed of the motor, the motor frequency search range is estimated and the frequency center position and step are dynamically adjusted to perform frequency search for motor frequency calculation. The frequency corresponding to the maximum amplitude within the motor frequency search range in the spectrum is obtained as the frequency for motor frequency extraction calculation, and the motor extraction frequency is calculated.

[0045] The accuracy of the extracted frequency is checked. If the check is passed, the extracted speed of the motor is calculated according to the conversion formula between the extracted frequency and the motor speed. If the check is not passed, the vibration acceleration signal of the motor of the measured rotating equipment unit is reacquired.

[0046] The formula for the Fast Fourier Transform is:

[0047]

[0048] Where x(n) is the nth data point of the vibration velocity signal, N is the total number of data points of the vibration velocity signal, and X(k) is the kth data point after the fast Fourier transform.

[0049] In this solution, the calculation of the extracted rotational frequency of the motor is specifically as follows:

[0050] If the vibration acceleration information of the motor measuring point of the rotating equipment unit is multidimensional, then the average value of the extracted rotational frequency in all dimensions is obtained, and the average value is used as the extracted rotational frequency of the motor measuring point.

[0051] If the motor of the rotating equipment unit being tested has multiple motor measurement points, the average value of the extracted rotational frequencies of the multiple motor measurement points shall be taken as the extracted rotational frequency of the motor of the rotating equipment unit being tested.

[0052] In this solution, the accuracy verification of the extracted frequency conversion specifically includes:

[0053] Obtain the minimum and maximum values ​​of the motor rotation frequency of the measured rotating equipment unit, and calculate the ratio of the difference between the minimum and maximum values;

[0054] If the phase difference ratio is within the preset phase difference ratio range, the accuracy of the motor frequency extraction of the measured rotating equipment unit is deemed to have passed the review; otherwise, the accuracy of the motor frequency extraction of the measured rotating equipment unit is deemed to have failed the review.

[0055] This solution also includes the extraction of the rotational speed of non-motor equipment within the unit, specifically:

[0056] The vibration acceleration signal of the non-motor equipment of the unit under test is obtained by using the synchronous data acquisition strategy of IoT smart vibration sensor;

[0057] The motors belonging to the same unit are located based on the sensor grouping, and the extraction speed of the motors is obtained;

[0058] The transmission ratio of the non-motorized equipment within the unit is obtained based on the unit structure, and the extraction speed of the non-motorized equipment is obtained through the transmission ratio of the non-motorized equipment within the unit.

[0059] A third aspect of the present invention also provides a computer-readable storage medium comprising a method program for extracting the rotational speed of a rotating equipment unit based on the Internet of Things (IoT). When the method program is executed by a processor, it implements the steps of the method program for extracting the rotational speed of a rotating equipment unit based on the IoT as described in any of the preceding claims.

[0060] This invention discloses a method for accurately extracting the rotational speed of each rotating device in a generator set based on a synchronous data acquisition strategy using IoT-based intelligent vibration sensors and a precise extraction method for the transmission ratio of generator set equipment. The method includes: acquiring vibration acceleration signals from motor measuring points and calculating the corresponding vibration velocity signals; performing Fast Fourier Transform (FFT) processing on the vibration acceleration and velocity signals; finding the frequency with the largest amplitude in the frequency domain and verifying its accuracy before using it as the extracted rotational frequency of the motor, dynamically adjusting the frequency center position and step size during the search process; calculating the extracted rotational speed of the motor based on the correspondence between rotational speed and rotational frequency; obtaining the transmission ratio of each rotating device within the generator set based on the generator set structure; and calculating the extracted rotational speed of each device in the generator set using the extracted rotational speed and transmission ratio of the motor. This invention improves the anti-interference capability and accuracy of rotational speed extraction, and can be used for fault diagnosis, remaining life calculation, and maintenance recommendations for rotating generator sets, reducing the false alarm rate. This invention is a very practical tool in the absence of analog or digital input signals from a tachometer. Attached Figure Description

[0061] Figure 1 A flowchart of a method for extracting the rotational speed of a rotating equipment unit based on the Internet of Things (IoT) according to the present invention is shown;

[0062] Figure 2 A flowchart of the synchronous data acquisition strategy for the IoT smart vibration sensor in this invention is shown.

[0063] Figure 3 A flowchart illustrating the process of obtaining the extracted speed of the motor in this invention is shown;

[0064] Figure 4 A flowchart illustrating the extraction speed of a non-motor device in this invention is shown;

[0065] Figure 5 A block diagram of an Internet of Things-based rotating equipment speed extraction system of the present invention is shown. Detailed Implementation

[0066] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0067] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0068] Figure 1 A flowchart of a method for extracting the rotational speed of a rotating equipment unit based on the Internet of Things (IoT) according to the present invention is shown.

[0069] like Figure 1 As shown, the first aspect of the present invention provides a method for extracting the rotational speed of a rotating equipment unit based on the Internet of Things, comprising:

[0070] S102, the vibration acceleration signal of the motor measuring point is obtained through the synchronous data acquisition strategy of IoT smart vibration sensor, the vibration acceleration signal is integrated to obtain the vibration velocity signal, and the vibration acceleration signal and vibration velocity signal are processed by fast Fourier transform.

[0071] S104, Based on the rated speed of the motor, determine the positioning spectrum of the motor speed range, dynamically adjust the center position and step size of the positioning spectrum of the motor speed range to find the frequency with the largest amplitude value for calculating the extracted motor frequency, and use it as the extracted motor frequency after accuracy verification;

[0072] S106, The extracted speed of the motor is calculated based on the correspondence between rotational speed and rotational frequency;

[0073] S108: Based on the unit structure, the transmission ratio of each rotating device in the unit is obtained. The extraction speed of each device in the unit is calculated by using the extraction speed of the motor and the transmission ratio.

[0074] It should be noted that the synchronous data acquisition strategy of IoT smart vibration sensors can enable multiple sensing points on a single unit under test to simultaneously acquire data at preset acquisition times. In other words, the IoT smart vibration sensors at different measurement points start acquiring data approximately simultaneously in absolute time. Only vibration data acquired through these nodes can be considered to be under the same rotational speed conditions, thereby enabling the calculation of the extraction speed of non-motor equipment using the extraction speed and transmission ratio of the motor.

[0075] Figure 2 A flowchart of the synchronous data acquisition strategy for the IoT smart vibration sensor in this invention is shown.

[0076] The aforementioned strategy for synchronous data acquisition via IoT smart vibration sensors specifically involves: grouping sensors at multiple measuring points on the same unit into a group using grouping technology; pre-configuring the initial wake-up time and wake-up interval for all sensors in each group for the day, and distributing this information to all IoT smart vibration sensors within the group; calculating the ideal wake-up time for the main processor and configuring the time required for the next wake-up via a real-time clock, thereby implementing a timed wake-up mechanism for the main processor; after being woken up, the main processor connects to a network time server for time synchronization and calculates the difference between the ideal wake-up time and the actual wake-up time; determining whether the difference between the ideal wake-up time and the actual wake-up time is greater than a preset time difference threshold; if it is greater, the main processor continues to sleep and calculates the continued sleep time; if it is not greater, data is acquired and uploaded via IoT smart sensors.

[0077] It should be noted that a rotating equipment unit refers to a group of rotating machines, driven by a drive motor through a transmission device or directly driving the working machines. Transmission devices include belt drives, chain drives, and gear drives; working machines include rotating equipment such as pumps and fans that directly provide production tasks. The transmission ratio of the unit can be obtained from its structure, and the calculation formula is as follows:

[0078] Transmission_Ratio=RPM_Motor / RPM_comp

[0079] Where Trion_Ratio is the transmission ratio of the unit equipment, RPM_Motor is the speed of the drive motor, and RPM_comp is the speed of the unit equipment.

[0080] Figure 3 A flowchart for obtaining the extracted speed of the motor in this invention is shown.

[0081] According to an embodiment of the present invention, the fast Fourier transform processing of the vibration acceleration signal and the vibration velocity signal specifically includes:

[0082] The vibration acceleration signal of the motor of the rotating equipment unit to be measured is acquired, the vibration acceleration signal is integrated to obtain the vibration velocity signal, and the vibration acceleration signal and the vibration velocity signal are subjected to fast Fourier transform.

[0083] Based on the rated speed of the motor, the motor frequency search range is estimated and the frequency center position and step are dynamically adjusted to perform frequency search for motor frequency calculation. The frequency corresponding to the maximum amplitude within the motor frequency search range in the spectrum is obtained as the frequency for motor frequency extraction calculation, and the motor extraction frequency is calculated.

[0084] The accuracy of the extracted frequency is verified. If the verification is successful, the extracted speed of the motor is calculated according to the conversion formula between the extracted frequency and the motor speed. If the verification is unsuccessful, the vibration acceleration signal of the motor of the measured rotating equipment unit is reacquired.

[0085] It should be noted that with the widespread application of accelerometers, measuring vibration acceleration signals has become the primary choice. Vibration velocity signals need to be integrated from vibration acceleration signals. To reduce computational load and minimize integration errors caused by residual trend terms and DC components in the vibration acceleration signal, a frequency domain integration method is used. This involves performing a Fourier transform on the signal before integration. The Fourier transform formula is as follows:

[0086]

[0087] Where f(t) is the vibration signal, t is time, i is the imaginary unit, phase w = 2πf, and f is the frequency;

[0088] Based on the integral properties of the Fourier transform, when obtaining velocity and displacement signals from the integration of acceleration signals, a Fourier transform can be performed first, transforming the integration into a division operation. Then, an inverse Fourier transform can be performed, and the real part can be taken to obtain the velocity signal in the time domain. Frequency domain integration directly uses the interchange relationship between the integrals of sine and cosine in the frequency domain (phase interchange), which effectively avoids the cumulative amplification effect of small errors in the time domain signal during the integration process, resulting in more accurate calculation results. Furthermore, when integrating vibration acceleration signals measured by accelerometers, the minimum effective operating frequency of the accelerometer should be considered. The amplitudes of components below the minimum effective operating frequency should be set to zero to improve the signal-to-noise ratio.

[0089] It should be noted that the formula for the Fast Fourier Transform is:

[0090]

[0091] Where x(n) is the nth data point of the vibration velocity signal, N is the total number of data points of the vibration velocity signal, and X(k) is the kth data point after the fast Fourier transform;

[0092] The formula for converting motor frequency to motor speed is:

[0093] f_rpm = RPM / 60

[0094] Where f_rpm is the motor frequency and RPM is the motor speed.

[0095] It should be noted that the calculation of the extracted rotational frequency of the motor is as follows:

[0096] If the vibration acceleration information of the motor measuring point of the rotating equipment unit is multidimensional, then the average value of the extracted rotational frequency in all dimensions is obtained, and the average value is used as the extracted rotational frequency of the motor measuring point.

[0097] If the motor of the rotating equipment unit being tested has multiple motor measurement points, the average value of the extracted rotational frequencies of the multiple motor measurement points shall be taken as the extracted rotational frequency of the motor of the rotating equipment unit being tested.

[0098] According to an embodiment of the present invention, the accuracy verification of the extracted frequency conversion specifically includes:

[0099] Obtain the minimum and maximum values ​​of the motor rotation frequency of the measured rotating equipment unit, and calculate the ratio of the difference between the minimum and maximum values;

[0100] If the phase difference ratio is within the preset phase difference ratio range, the accuracy of the motor frequency extraction of the measured rotating equipment unit is deemed to have passed the review; otherwise, the accuracy of the motor frequency extraction of the measured rotating equipment unit is deemed to have failed the review.

[0101] It should be noted that, for example, for the extraction of rotational speed from multiple dimensions of a motor measuring point, if the difference between the maximum and minimum values ​​is less than 10%, the accuracy of the extracted rotational speed at that measuring point passes the verification; otherwise, it fails. For the extraction of rotational speed from multiple measuring points of a motor, if the difference between the maximum and minimum values ​​is less than 10%, the accuracy of the extracted rotational speed at that motor passes the verification; otherwise, it fails.

[0102] Figure 4 A flowchart for obtaining the extracted rotational speed of a non-motor device in this invention is shown.

[0103] According to an embodiment of the present invention, the present invention further includes the extraction of the rotational speed of non-motor equipment within the unit, specifically:

[0104] The vibration acceleration signal of the non-motor equipment of the unit under test is obtained by using the synchronous data acquisition strategy of IoT smart vibration sensor;

[0105] The motors belonging to the same unit are located based on the sensor grouping, and the extraction speed of the motors is obtained;

[0106] The transmission ratio of the non-motorized equipment within the unit is obtained based on the unit structure, and the extraction speed of the non-motorized equipment is obtained through the transmission ratio of the non-motorized equipment within the unit.

[0107] Figure 5 A block diagram of an Internet of Things-based rotating equipment speed extraction system of the present invention is shown.

[0108] A second aspect of the present invention also provides an Internet of Things (IoT)-based rotating equipment unit speed extraction system 5. The system includes a memory 51 and a processor 52. The memory includes an IoT-based rotating equipment unit speed extraction method program. When executed by the processor, the IoT-based rotating equipment unit speed extraction method program performs the following steps:

[0109] The vibration acceleration signal of the motor measuring point is obtained by synchronous data acquisition strategy of IoT smart vibration sensor, the vibration acceleration signal is integrated to obtain vibration velocity signal, and the vibration acceleration signal and vibration velocity signal are processed by fast Fourier transform.

[0110] Based on the rated speed of the motor, the positioning spectrum of the motor speed range is determined. The center position and step size of the positioning spectrum of the motor speed range are dynamically adjusted to find the frequency with the largest amplitude value for calculating the extracted motor frequency. After accuracy verification, it is used as the extracted motor frequency.

[0111] Based on the relationship between rotational speed and rotational frequency, the extracted rotational speed of the motor is calculated;

[0112] Based on the unit structure, the transmission ratio of each rotating device within the unit is obtained. By using the extraction speed of the motor and the transmission ratio, the extraction speed of each device in the unit is calculated.

[0113] It should be noted that the synchronous data acquisition strategy of IoT smart vibration sensors can enable multiple sensing points on a single unit under test to simultaneously acquire data at preset acquisition times. In other words, the IoT smart vibration sensors at different measurement points start acquiring data approximately simultaneously in absolute time. Only vibration data acquired through these nodes can be considered to be under the same rotational speed conditions, thereby enabling the calculation of the extraction speed of non-motor equipment using the extraction speed and transmission ratio of the motor.

[0114] The aforementioned strategy for synchronous data acquisition via IoT smart vibration sensors specifically involves: grouping sensors at multiple measuring points on the same unit into a group using grouping technology; pre-configuring the initial wake-up time and wake-up interval for all sensors in each group for the day, and distributing this information to all IoT smart vibration sensors within the group; calculating the ideal wake-up time for the main processor and configuring the time required for the next wake-up via a real-time clock, thereby implementing a timed wake-up mechanism for the main processor; after being woken up, the main processor connects to a network time server for time synchronization and calculates the difference between the ideal wake-up time and the actual wake-up time; determining whether the difference between the ideal wake-up time and the actual wake-up time is greater than a preset time difference threshold; if it is greater, the main processor continues to sleep and calculates the continued sleep time; if it is not greater, data is acquired and uploaded via IoT smart sensors.

[0115] It should be noted that a rotating equipment unit refers to a group of rotating machines, driven by a drive motor through a transmission device or directly driving the working machines. Transmission devices include belt drives, chain drives, and gear drives; working machines include rotating equipment such as pumps and fans that directly provide production tasks. The transmission ratio of the unit can be obtained from its structure, and the calculation formula is as follows:

[0116] Transmission_Ratio=RPM_Motor / RPM_comp

[0117] Where Transmission_Ratio is the transmission ratio of the unit equipment, RPM_Motor is the speed of the drive motor, and RPM_comp is the speed of the unit equipment.

[0118] According to an embodiment of the present invention, the fast Fourier transform processing of the vibration velocity signal specifically includes:

[0119] The vibration acceleration signal of the motor of the rotating equipment unit to be measured is acquired, the vibration acceleration signal is integrated to obtain the vibration velocity signal, and the vibration acceleration signal and the vibration velocity signal are subjected to fast Fourier transform.

[0120] Based on the rated speed of the motor, the motor frequency search range is estimated and the frequency center position and step are dynamically adjusted to perform frequency search for motor frequency calculation. The frequency corresponding to the maximum amplitude within the motor frequency search range in the spectrum is obtained as the frequency for motor frequency extraction calculation, and the motor extraction frequency is calculated.

[0121] The accuracy of the extracted frequency is verified. If the verification is successful, the extracted speed of the motor is calculated according to the conversion formula between the extracted frequency and the motor speed. If the verification is unsuccessful, the vibration acceleration signal of the motor of the measured rotating equipment unit is reacquired.

[0122] It should be noted that with the widespread application of accelerometers, measuring vibration acceleration signals has become the primary choice. Vibration velocity signals need to be integrated from vibration acceleration signals. To reduce computational load and minimize integration errors caused by residual trend terms and DC components in the vibration acceleration signal, a frequency domain integration method is used. This involves performing a Fourier transform on the signal before integration. The Fourier transform formula is as follows:

[0123]

[0124] Where f(t) is the vibration signal, t is time, i is the imaginary unit, phase w = 2πf, and f is the frequency;

[0125] Based on the integral properties of the Fourier transform, when obtaining velocity and displacement signals from the integration of acceleration signals, a Fourier transform can be performed first, transforming the integration into a division operation. Then, an inverse Fourier transform can be performed, and the real part can be taken to obtain the velocity signal in the time domain. Frequency domain integration directly uses the interchange relationship between the integrals of sine and cosine in the frequency domain (phase interchange), which effectively avoids the cumulative amplification effect of small errors in the time domain signal during the integration process, resulting in more accurate calculation results. Furthermore, when integrating vibration acceleration signals measured by accelerometers, the minimum effective operating frequency of the accelerometer should be considered. The amplitudes of components below the minimum effective operating frequency should be set to zero to improve the signal-to-noise ratio.

[0126] It should be noted that the formula for the Fast Fourier Transform is:

[0127]

[0128] Where x(n) is the nth data point of the vibration velocity signal, N is the total number of data points in the vibration velocity signal, X(k) is the kth data point after the fast Fourier transform, and i is the imaginary unit;

[0129] The formula for converting motor frequency to motor speed is:

[0130] f_rpm = RPM / 60

[0131] Where f_rpm is the motor frequency and RPM is the motor speed.

[0132] It should be noted that the calculation of the extracted rotational frequency of the motor is as follows:

[0133] If the vibration acceleration information of the motor measuring point of the rotating equipment unit is multidimensional, then the average value of the extracted rotational frequency in all dimensions is obtained, and the average value is used as the extracted rotational frequency of the motor measuring point.

[0134] If the motor of the rotating equipment unit being tested has multiple motor measurement points, the average value of the extracted rotational frequencies of the multiple motor measurement points shall be taken as the extracted rotational frequency of the motor of the rotating equipment unit being tested.

[0135] According to an embodiment of the present invention, the accuracy verification of the extracted frequency conversion specifically includes:

[0136] Obtain the minimum and maximum values ​​of the motor rotation frequency of the measured rotating equipment unit, and calculate the ratio of the difference between the minimum and maximum values;

[0137] If the phase difference ratio is within the preset phase difference ratio range, the accuracy of the motor frequency extraction of the measured rotating equipment unit is deemed to have passed the review; otherwise, the accuracy of the motor frequency extraction of the measured rotating equipment unit is deemed to have failed the review.

[0138] It should be noted that, for example, for the extraction of rotational speed from multiple dimensions of a motor measuring point, if the difference between the maximum and minimum values ​​is less than 10%, the accuracy of the extracted rotational speed at that measuring point passes the verification; otherwise, it fails. For the extraction of rotational speed from multiple measuring points of a motor, if the difference between the maximum and minimum values ​​is less than 10%, the accuracy of the extracted rotational speed at that motor passes the verification; otherwise, it fails.

[0139] According to an embodiment of the present invention, the present invention further includes the extraction of the rotational speed of non-motor equipment within the unit, specifically:

[0140] The vibration acceleration signal of the non-motor equipment of the unit under test is obtained by using the synchronous data acquisition strategy of IoT smart vibration sensor;

[0141] The motors belonging to the same unit are located based on the sensor grouping, and the extraction speed of the motors is obtained;

[0142] The transmission ratio of the non-motorized equipment within the unit is obtained based on the unit structure, and the extraction speed of the non-motorized equipment is obtained through the transmission ratio of the non-motorized equipment within the unit.

[0143] A third aspect of the present invention also provides a computer-readable storage medium comprising a method program for extracting the rotational speed of a rotating equipment unit based on the Internet of Things (IoT). When the method program is executed by a processor, it implements the steps of the method program for extracting the rotational speed of a rotating equipment unit based on the IoT as described in any of the preceding claims.

[0144] This invention discloses a method for accurately extracting the rotational speed of each rotating device in a generator set based on a synchronous data acquisition strategy using IoT-based intelligent vibration sensors and a precise extraction method for the transmission ratio of generator set equipment. The method includes: acquiring vibration acceleration signals from motor measuring points and calculating the corresponding vibration velocity signals; performing Fast Fourier Transform (FFT) processing on the vibration acceleration and velocity signals; finding the frequency with the largest amplitude in the frequency domain and verifying its accuracy before using it as the extracted rotational frequency of the motor, dynamically adjusting the frequency center position and step size during the search process; calculating the extracted rotational speed of the motor based on the correspondence between rotational speed and rotational frequency; obtaining the transmission ratio of each rotating device within the generator set based on the generator set structure; and calculating the extracted rotational speed of each device in the generator set using the extracted rotational speed and transmission ratio of the motor. This invention improves the anti-interference capability and accuracy of rotational speed extraction, and can be used for fault diagnosis, remaining life calculation, and maintenance recommendations for rotating generator sets, reducing the false alarm rate. This invention is a very practical tool in the absence of analog or digital input signals from a tachometer.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0146] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0147] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0148] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0149] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0150] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for extracting rotating equipment set rotating speed based on Internet of Things, characterized in that, The method comprises the following steps: The vibration acceleration signal of the motor measuring point is obtained through the synchronous data acquisition strategy of the Internet of Things intelligent vibration sensor, the vibration acceleration signal is integrated to obtain the vibration velocity signal, and the vibration acceleration signal and the vibration velocity signal are subjected to fast Fourier transform processing; The synchronous data acquisition strategy of the Internet of Things intelligent vibration sensor is specifically as follows: The sensors at multiple measuring point positions on the same unit are grouped by grouping technology, the initial wake-up time and the wake-up time interval of all sensors in each group on the same day are pre-configured, and are delivered to all Internet of Things intelligent vibration sensors in the group; The main processor calculates the ideal wake-up time, and configures the time required for the next wake-up of the real-time clock to realize the timing wake-up mechanism of the main processor through the real-time clock; After the main processor is woken up, it connects a network time server for time adjustment, and calculates the difference between the ideal wake-up time and the actual wake-up time; It is judged whether the difference between the ideal wake-up time and the actual wake-up time is greater than a preset time difference threshold, if yes, the main processor continues to sleep and calculates the time of continued sleep, and if no, data acquisition and uploading are performed through the Internet of Things intelligent sensor; The motor extraction rotation frequency is calculated based on the motor rated rotation speed to determine the motor rotation speed range positioning frequency spectrum, the frequency center position and the step size are dynamically adjusted in the motor rotation speed range positioning frequency spectrum to find the frequency with the maximum amplitude for calculating the motor extraction rotation frequency, and the precision is reviewed to serve as the extraction rotation frequency of the motor; The calculation of the extraction rotation frequency of the motor is specifically as follows: If the vibration acceleration information of the motor measuring point of the measured rotating equipment unit is multi-dimensional, the mean value of the extraction rotation frequencies of all dimensional directions is obtained, and the mean value is taken as the extraction rotation frequency of the motor measuring point; If the motor of the measured rotating equipment unit has multiple motor measuring points, the average value of the extraction rotation frequencies of the multiple motor measuring points is taken as the extraction rotation frequency of the motor of the measured rotating equipment unit; According to the corresponding relationship between the rotation speed and the rotation frequency, the extraction rotation speed of the motor is calculated; the accuracy of the extraction rotation frequency is reviewed, specifically as follows: The minimum value and the maximum value of the extraction rotation frequency of the motor of the measured rotating equipment unit are obtained, and the difference ratio of the minimum value and the maximum value is calculated; It is judged whether the difference ratio is within a preset difference ratio range, if yes, it is determined that the accuracy review of the extraction rotation frequency of the motor of the measured rotating equipment unit is passed, and if no, it is determined that the accuracy review of the extraction rotation frequency of the motor of the measured rotating equipment unit is not passed; According to the transmission ratio of each rotating equipment in the unit, the extraction rotation speed of each equipment in the unit is calculated through the extraction rotation speed of the motor and the transmission ratio. 2.The method of claim 1, wherein, The fast Fourier transform processing of the vibration acceleration signal and the vibration velocity signal is specifically as follows: The vibration acceleration signal of the motor of the measured rotating equipment unit is obtained, the vibration acceleration signal is integrated to obtain the vibration velocity signal, and the vibration acceleration signal and the vibration velocity signal are subjected to fast Fourier transform; According to the rated speed of the motor, the motor rotation frequency search range is estimated, and the frequency center position and step are dynamically adjusted for motor rotation frequency calculation frequency search, the frequency corresponding to the maximum amplitude in the motor rotation frequency search range in the frequency spectrum is obtained as the motor extraction rotation frequency calculation frequency, and the motor extraction rotation frequency is calculated; The accuracy of the extraction rotation frequency is checked, and if it passes the check, the extraction rotation speed of the motor is calculated according to the conversion formula of the extraction rotation frequency and the motor speed, and if it does not pass the check, the vibration acceleration signal of the motor of the measured rotating equipment unit is reacquired; The formula of the fast Fourier transform is: , , wherein, is the i-th data of the vibration velocity signal, is the total number of data of the vibration velocity signal, is the i-th data after the fast Fourier transform, is the i-th data after the fast Fourier transform.​ 3.The method of claim 1, wherein, Further comprising, the rotation speed extraction of non-motor equipment in the unit, specifically: The vibration acceleration signal of the non-motor equipment in the measured unit is acquired by using the synchronous data acquisition strategy of the Internet of Things intelligent vibration sensor; According to the sensor grouping, the motor belonging to the same unit is found, and the extraction rotation speed of the motor is acquired; According to the unit structure, the transmission ratio of the non-motor equipment in the unit is obtained, and the extraction rotation speed of the non-motor equipment is obtained through the transmission ratio of the non-motor equipment in the unit.

4. A rotating equipment unit rotation speed extraction system based on the Internet of Things, characterized by, The system comprises a memory and a processor, the memory comprises an Internet of Things-based rotating equipment unit rotation speed extraction method program, and the Internet of Things-based rotating equipment unit rotation speed extraction method program is executed by the processor to realize the following steps: The vibration acceleration signal of the motor measuring point is acquired by using the synchronous data acquisition strategy of the Internet of Things intelligent vibration sensor, the vibration acceleration signal is integrated to obtain a vibration velocity signal, and the vibration acceleration signal and the vibration velocity signal are subjected to fast Fourier transform processing; The synchronous data acquisition strategy of the Internet of Things intelligent vibration sensor comprises: The sensors at multiple measuring point positions on the same unit are grouped into a group by using grouping technology, the initial wake-up time and the wake-up time interval of all sensors in each group on the same day are pre-configured, and the pre-configured data is sent to all Internet of Things intelligent vibration sensors in the group; The main processor calculates the ideal wake-up time and configures the time required for the real-time clock to wake up next time, and realizes the timing wake-up mechanism of the main processor through the real-time clock; After the main processor is woken up, it connects a network time server to adjust the time, calculates the difference between the ideal wake-up time and the actual wake-up time, and judges whether the difference is greater than a preset time difference threshold value, if yes, the main processor continues to sleep and calculates the time for continuing to sleep, if not, the data acquisition and uploading are performed through the Internet of Things intelligent sensor; The motor rotation speed range positioning frequency spectrum is determined based on the rated speed of the motor, the frequency center position and step are dynamically adjusted in the motor rotation speed range positioning frequency spectrum to find the frequency with the maximum amplitude for calculating the motor extraction rotation frequency, and after precision review, the motor extraction rotation frequency is obtained; The calculation of the motor extraction rotation frequency comprises: If the vibration acceleration information of the motor measuring point of the measured rotating equipment unit is multi-dimensional, the mean value of the extraction rotation frequency of all dimensional directions is acquired, and the mean value is taken as the extraction rotation frequency of the motor measuring point. ​ If the motor of the measured rotating equipment unit has multiple motor measuring points, the average of the extraction rotation frequencies of the multiple motor measuring points is taken as the extraction rotation frequency of the motor of the measured rotating equipment unit; According to the correspondence between the rotation speed and the rotation frequency, the extraction rotation speed of the motor is calculated; the accuracy of the extraction rotation frequency is reviewed, specifically: The minimum value and the maximum value of the extraction rotation frequency of the motor of the measured rotating equipment unit are obtained, and the difference ratio of the minimum value and the maximum value is calculated; It is judged whether the difference ratio is within the preset difference ratio range. If yes, it is determined that the extraction rotation frequency of the motor of the measured rotating equipment unit passes the accuracy review, otherwise, it is determined that the extraction rotation frequency of the motor of the measured rotating equipment unit fails the accuracy review; According to the unit structure, the transmission ratio of each rotating equipment in the unit is obtained, and the extraction rotation speed of each equipment in the unit is calculated according to the extraction rotation speed of the motor and the transmission ratio.

5. The rotating equipment unit rotation speed extraction system based on the Internet of Things according to claim 4, characterized in that, The vibration speed signal is processed by fast Fourier transform, specifically: The vibration acceleration signal of the motor of the measured rotating equipment unit is obtained, and the vibration speed signal is obtained by integrating the vibration acceleration signal. The vibration acceleration signal and the vibration speed signal are processed by fast Fourier transform; According to the rated rotation speed of the motor, the motor rotation frequency search range is estimated, and the frequency center position and the step are dynamically adjusted for motor rotation frequency calculation frequency search. The frequency corresponding to the maximum amplitude in the motor rotation frequency search range in the frequency spectrum is taken as the extraction rotation frequency calculation frequency of the motor, and the extraction rotation frequency of the motor is calculated; The accuracy of the extraction rotation frequency is reviewed. If it passes the review, the extraction rotation speed of the motor is calculated according to the conversion formula of the extraction rotation frequency and the motor rotation speed. If it fails the review, the vibration acceleration signal of the motor of the measured rotating equipment unit is obtained again. The formula of the fast Fourier transform is: ， ， in, The first vibration velocity signal One data point, The total number of vibration velocity signal data. The first after Fast Fourier Transform Data points.

6. A computer-readable storage medium, characterized in that: The computer readable storage medium includes a rotating equipment unit rotation speed extraction method program based on Internet of Things. When the rotating equipment unit rotation speed extraction method program based on Internet of Things is executed by the processor, the steps of the rotating equipment unit rotation speed extraction method based on Internet of Things in any one of claims 1 to 4 are realized.

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