Dynamic equipment state detection method and device and nonvolatile storage medium

Abstract

The invention discloses a dynamic equipment state detection method and device and a nonvolatile storage medium. The method comprises the steps that vibration data of the dynamic equipment are collected, the vibration data comprise at least one of a vibration signal sequence and original vibration acceleration data, and the dynamic equipment is rotary dynamic equipment; determining a first trend sequence of the dynamic equipment according to the vibration data; determining values of development trend indexes corresponding to the first trend sequences according to the first trend sequences; determining a health degree measurement value according to the development trend index and a preset weight corresponding to the development trend index; and uploading the first trend sequence, the development trend index and the health degree measurement value to a server for determining the state of the mobile equipment. The technical problems of low dynamic equipment state detection efficiency, poor stability and incomplete health state evaluation caused by large data transmission quantity, high delay and single fault diagnosis index of a traditional wireless vibration sensor are solved.

Description

Technical Field

[0001] The present application relates to the technical field of rotating moving equipment state detection, and in particular, to a moving equipment state detection method, device and non-volatile storage medium. Background Art

[0002] At present, the amount of data collected by vibration monitoring is huge, and a large amount of data needs to be transmitted to the local server for processing and evaluation, which easily causes bandwidth congestion and data transmission delays, and greatly reduces the timeliness of data evaluation for the equipment itself. In addition, in terms of vibration feature calculation, usually only a single feature in the time and frequency domain is considered to evaluate the operating status of the equipment, and the evaluation index is relatively simple.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method, device and non-volatile storage medium for detecting the state of a moving device, so as to at least solve the technical problems of low efficiency, poor stability and incomplete health status assessment of the state of a moving device caused by large data transmission volume, high latency and single fault diagnosis indicator of traditional wireless vibration sensors.

[0005] According to one aspect of an embodiment of the present application, a method for detecting the state of a moving device is provided, comprising: collecting vibration data of the moving device, wherein the vibration data comprises at least one of the following: a vibration signal sequence and original vibration acceleration data, and the moving device is a rotating moving device; determining a first trend sequence of the moving device based on the vibration data, wherein the first trend sequence comprises at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequencies, a historical trend sequence of gearbox characteristic frequencies, a historical trend sequence of vibration intensity, and a historical trend sequence of dimensionless characteristics in time domain; determining, based on the first trend sequence, a numerical value of a development trend indicator corresponding to each first trend sequence; determining a health measurement value based on the development trend indicator and a preset weight corresponding to the development trend indicator; uploading the first trend sequence, the development trend indicator, and the health measurement value to a server for determining the state of the moving device.

[0006] Optionally, determining a first trend sequence of the moving equipment based on vibration data includes: performing a fast Fourier transform on the vibration signal sequence to obtain a first frequency value sequence and a first amplitude value sequence corresponding to the first frequency value sequence; screening peak points in the first amplitude sequence whose relative height is greater than a preset threshold, and adding the frequency value corresponding to the peak point to a second frequency value sequence, wherein the relative height is the difference between the peak value and the valley value within a preset interval; determining multiple candidate frequency points in the second frequency value sequence based on the preset interval and the preset interval; determining a target frequency point from the candidate frequency points, and using the frequency corresponding to the target frequency point as the rotation frequency of the moving equipment; and determining a historical trend sequence of frequency domain characteristics of the moving equipment, a historical trend sequence of rolling bearing characteristic frequencies, and a historical trend sequence of gearbox characteristic frequencies based on the rotation frequency.

[0007] Optionally, determining the target frequency point from the candidate frequency points includes: determining multiple multiple frequency values ​​corresponding to the candidate frequency point; determining the frequency value with the smallest difference with each multiple frequency value in the second frequency value sequence as the approximate frequency value corresponding one-to-one to each multiple frequency value; determining the sum of the differences between each multiple frequency value and the approximate frequency value corresponding to the multiple frequency value as the sum of the differences of the candidate frequency points; and determining the candidate frequency point with the smallest sum of the differences as the target frequency point.

[0008] Optionally, determining the value of the development trend indicator corresponding to each first trend sequence based on the first trend sequence includes: filtering the first trend sequence; determining the second trend sequence based on the filtered first trend sequence; and determining the value of the development trend indicator based on the second trend sequence.

[0009] Optionally, the second trend sequence is determined according to the following formula:

[0010] Among them, Y = {y1, y2, ..., yn} is the second trend sequence, X = {x1, x2, ..., xn} is the first trend sequence, t is the sampling time corresponding to each sampling point in the first trend sequence, and λ is a preset regularization parameter.

[0011] Optionally, the value of the development trend indicator is determined according to the following formula: Wherein, Y={y1, y2, ..., yn} is the second trend sequence, and Δt is the time difference corresponding to each adjacent sampling point in the second trend sequence.

[0012] Optionally, the vibration data is collected by a sensor, and the sensor is also used to locally determine the first trend sequence, the development trend indicator and the health measurement value, and store the first trend sequence, the development trend indicator and the health measurement value.

[0013] According to another aspect of an embodiment of the present application, a dynamic equipment status detection device is also provided, including: an acquisition module, used to acquire vibration data of the dynamic equipment, wherein the vibration data includes at least one of the following: a vibration signal sequence and original vibration acceleration data, and the dynamic equipment is a rotating dynamic equipment; a first determination module, used to determine a first trend sequence of the dynamic equipment based on the vibration data, wherein the first trend sequence includes at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequency, a historical trend sequence of gearbox characteristic frequency, a historical trend sequence of vibration intensity, and a historical trend sequence of dimensionless characteristics in time domain; a second determination module, used to determine the numerical value of the development trend indicator corresponding to each first trend sequence based on the first trend sequence; a third determination module, used to determine the health measurement value based on the development trend indicator and the preset weight corresponding to the development trend indicator; an upload module, used to upload the first trend sequence, the development trend indicator, and the health measurement value to a server used to determine the status of the dynamic equipment.

[0014] According to another aspect of an embodiment of the present application, a non-volatile storage medium is provided, in which a program is stored, wherein when the program is running, a device where the non-volatile storage medium is located is controlled to execute a device status detection method.

[0015] According to another aspect of an embodiment of the present application, there is further provided an electronic device, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the device status detection method is executed when the program is run.

[0016] According to another aspect of an embodiment of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the method for detecting the state of a mobile device is implemented.

[0017] In an embodiment of the present application, vibration data of a moving device is collected, wherein the vibration data includes at least one of the following: a vibration signal sequence and original vibration acceleration data, and the moving device is a rotating moving device; a first trend sequence of the moving device is determined based on the vibration data, wherein the first trend sequence includes at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequencies, a historical trend sequence of gearbox characteristic frequencies, a historical trend sequence of vibration intensity, and a historical trend sequence of dimensionless characteristics in the time domain; a numerical value of a development trend indicator corresponding to each first trend sequence is determined based on the first trend sequence; a health measurement value is determined based on the development trend indicator and a preset weight corresponding to the development trend indicator; the first trend sequence, the development trend indicator, and the health measurement value are uploaded to a server for determining the state of the moving device, and analyzed by edge computing multi-feature indicators, so as to achieve the purpose of real-time monitoring and rapid fault diagnosis, thereby achieving the technical effect of reducing data transmission volume, reducing delay, and comprehensively evaluating the health status of the equipment, thereby solving the technical problems of low efficiency, poor stability, and incomplete health status evaluation of the moving device state detection caused by large data transmission volume, high delay, and single fault diagnosis indicator of traditional wireless vibration sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a structural schematic diagram of a computer terminal provided according to an embodiment of the present application;

[0020] Figure 2 It is a flow chart of a method for detecting the state of a moving device provided according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of various parts of an optional sensor provided according to an embodiment of the present application;

[0022] Figure 4 It is a schematic diagram of an optional process of collecting vibration data of dynamic equipment provided according to an embodiment of the present application;

[0023] Figure 5 It is a schematic diagram of an optional characteristic value calculation process provided according to an embodiment of the present application;

[0024] Figure 6 It is a structural schematic diagram of a moving equipment state detection device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0028] At present, most vibration monitoring is done by inspection workers using handheld inspection instruments to inspect equipment, which is difficult to detect in complex environments. Currently, the same type of wireless vibration sensors usually integrate a small part of the data processing function and transmit the original waveform data to the server. The transmission process may cause data loss, affecting the server's analysis of data and diagnosis of the equipment.

[0029] At present, the amount of data collected by vibration monitoring is huge, and a large amount of data needs to be transmitted to the local server for processing and evaluation, which easily causes bandwidth congestion and data transmission delays, and greatly reduces the timeliness of data evaluation for the equipment itself. In addition, in terms of vibration feature calculation, usually only a single feature in the time and frequency domain is considered to evaluate the operating status of the equipment, and the evaluation index is relatively simple.

[0030] In order to solve the above problems, relevant solutions are provided in the embodiments of the present application, which are described in detail below.

[0031] According to an embodiment of the present application, a method embodiment of a method for detecting the status of a moving device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for detecting the state of a moving device. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0033] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0034] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the dynamic device status detection method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned dynamic device status detection method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0037] In the above operating environment, the present application embodiment provides a method for detecting the state of a mobile device, such as Figure 2 As shown, the method comprises the following steps:

[0038] Step S202, collecting vibration data of the moving device, wherein the vibration data includes at least one of the following: a vibration signal sequence and original vibration acceleration data, and the moving device is a rotating moving device.

[0039] Optionally, the vibration data is collected by a sensor, and the sensor is also used to locally determine the first trend sequence, the development trend indicator and the health measurement value, and store the first trend sequence, the development trend indicator and the health measurement value.

[0040] Optionally, the sensor has a complex edge computing function. After performing a series of calculations on the collected waveforms, only the calculated results are sent to the server, saving the time and energy consumption of transmission, reducing the amount of data sent, and reducing the packet loss rate in data transmission. A wireless vibration sensor is a device that combines wireless communication technology and vibration sensing technology to monitor and measure the vibration condition of motor equipment. It usually includes components such as an acceleration sensor module, a microcontroller, a communication module, and a power management unit. Figure 3 A schematic diagram of the various parts of the sensor is shown, such as Figure 3 As shown, according to the functional classification, the sensor includes a sensing module, a data acquisition part, a data processing part, a data storage part, a communication transmission part, and an energy management part:

[0041] Sensor module: Use a highly sensitive accelerometer or vibration sensor to detect vibration signals and convert them into voltage signals.

[0042] Data acquisition module: collects the voltage signal output by the sensor and converts it into a digital signal.

[0043] Data processing module: The embedded processor is used to receive sensor data and perform real-time data processing and analysis to determine the first trend sequence, development trend indicators, and health metrics locally. Using edge computing technology, data processing can be performed on the device side (i.e. in the sensor) to reduce data transmission and latency. Data processing algorithms include spectrum analysis, time domain analysis, and eigenvalue extraction of vibration signals. It supports online firmware upgrades and can be used to upgrade data processing algorithms in the future.

[0044] Data storage module: stores waveform data and edge computing module data.

[0045] Communication transmission module: realizes data transmission between sensors and servers.

[0046] Energy management module: Integrates an efficient energy management module with low power consumption design and energy-saving sleep mode to extend the service life of the equipment.

[0047] Optionally, Figure 4 A process of collecting vibration data of moving equipment is shown, such as Figure 4 As shown in the figure, the collection of vibration data of dynamic equipment includes:

[0048] (1) Turn on the ADC (Analog-to-Digital Converter, i.e., data acquisition module) power supply and the sensor module power supply.

[0049] (2) Vibration data collection. Real-time collection of vibration data, with configurable sampling rate and number of sampling points. Supports XYZ three-axis synchronous sampling, and can perform linkage analysis on three-axis data.

[0050] (3) After the acquisition is completed, turn off the ADC power supply and the sensor module power supply, and no more acquisition will be performed during the subsequent calculation process, thereby reducing the power consumption of the module and realizing a low-power design of the system.

[0051] Step S204, determining a first trend sequence of the moving equipment based on the vibration data, wherein the first trend sequence includes at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequencies, a historical trend sequence of gearbox characteristic frequencies, a historical trend sequence of vibration intensity, and a historical trend sequence of dimensionless characteristics in time domain.

[0052] In the technical solution provided in step S204, determining the first trend sequence of the moving equipment based on the vibration data includes: performing a fast Fourier transform on the vibration signal sequence to obtain a first frequency value sequence and a first amplitude value sequence corresponding to the first frequency value sequence; screening the peak points in the first amplitude sequence whose relative height is greater than a preset threshold, and adding the frequency value corresponding to the peak point to the second frequency value sequence, wherein the relative height is the difference between the peak value and the valley value within a preset interval; determining multiple candidate frequency points in the second frequency value sequence based on the preset interval and the preset interval; determining a target frequency point from the candidate frequency points, and using the frequency corresponding to the target frequency point as the rotation frequency of the moving equipment; and determining the historical trend sequence of the frequency domain characteristics of the moving equipment, the historical trend sequence of the characteristic frequency of the rolling bearing, and the historical trend sequence of the characteristic frequency of the gearbox based on the rotation frequency.

[0053] Optionally, determining the target frequency point from the candidate frequency points includes: determining multiple multiple frequency values ​​corresponding to the candidate frequency point; determining the frequency value with the smallest difference with each multiple frequency value in the second frequency value sequence as the approximate frequency value corresponding one-to-one to each multiple frequency value; determining the sum of the differences between each multiple frequency value and the approximate frequency value corresponding to the multiple frequency value as the sum of the differences of the candidate frequency points; and determining the candidate frequency point with the smallest sum of the differences as the target frequency point.

[0054] Optionally, the time domain eigenvalues ​​are calculated, and 10 types of characteristic data, including mean, peak, effective value, standard deviation, root amplitude, skewness, kurtosis, peak factor, waveform factor, and margin factor, are calculated based on the original vibration data.

[0055] Optionally, the rotation speed of the device is estimated based on the vibration data, and the calculation method is as follows:

[0056] 1) Performing FFT transformation (Fast Fourier Transform) based on the vibration signal sequence to obtain its frequency value sequence F (first frequency value sequence) and its corresponding amplitude sequence A (first amplitude sequence);

[0057] 2) Calculate the median of the amplitude sequence A and record it as Am;

[0058] 3) Perform relative height difference peak search on the amplitude sequence A with a relative peak value greater than 0.1*Am (i.e., the preset threshold), and record it as the amplitude sequence Ap and the frequency sequence Fp. The specific steps are as follows: 1. Select the point in A with a value higher than the values ​​on the left and right ends (i.e., the peak point) as the peak sequence A1; 2. Put a mark on the peak value and record it as A11; 3. Extend a horizontal line from the peak to the left and right until the line passes through the signal or reaches the left and right endpoints; 4. Find the minimum value of the signal in each of the two intervals defined in step 3. This point is a trough or one of the signal endpoints; 5. Among the minimum values ​​of the two intervals, the higher one is the reference level Ao (i.e., the trough value). If A11-Ao>0.1*Am, the amplitude of the point is placed in the Ap sequence, and the frequency value of the point is placed in the Fp sequence (the second frequency value sequence);

[0059] 4) Determine the frequency search interval for speed search, recorded as [r1, r2, s], where r1 is the starting value, r2 is the ending value, and s is the interval value.

[0060] 5) Start from r1 and search at intervals of s. For each search frequency, record it as Fi (i.e., the frequency value corresponding to the candidate frequency point), find the corresponding multiplied frequency Fi2-Fi6, and for each value in Fi1-Fi6, find the value closest to it in the Fp sequence (i.e., the approximate frequency value), record each difference Ei1-Ei6, and sum them up Ei;

[0061] 6) The lowest point among the sum of the differences (i.e. the sum of the differences) of all points in the search interval is taken as the device's rotation frequency Fr.

[0062] Optionally, based on the calculated rotation frequency Fr, the frequencies of its 1-12 times and the corresponding amplitudes are calculated to obtain the historical trend sequences F1-F12 of 12 frequency domain features: based on Fr, the frequency values ​​of its 1 to 12 times are calculated. These frequency points correspond to 1 times, 2 times, 3 times and up to 12 times of the fundamental frequency of the equipment. The purpose of this step is to identify the characteristic frequencies that are closely related to the operating status of the equipment. Subsequently, for each calculated characteristic frequency, the peak frequency closest to it is found from the amplitude sequence Ap calculated from the sensor edge to extract the corresponding amplitude. By combining these amplitudes with the time series, the historical trend sequences F1 to F12 of 12 frequency domain features are constructed. Each sequence represents the change in vibration energy of the equipment at a specific frequency multiple.

[0063] The construction of historical trend sequences F1 to F12 helps monitor the changes in the vibration energy of the equipment at different frequency multiples over time, so that early signs of equipment failure, such as bearing wear and gear damage, can be discovered in a timely manner, because these failures usually cause a significant increase in vibration energy at a specific frequency multiple. By analyzing these sequences, the health of the equipment can be more accurately assessed, potential failures can be predicted, and corresponding preventive maintenance measures can be taken to reduce downtime and improve equipment reliability and production efficiency. This historical trend analysis method based on frequency domain characteristics has significantly improved the fault diagnosis capabilities of wireless vibration sensors in edge computing and the comprehensiveness of equipment health status assessment.

[0064] Optionally, based on the calculated rotation frequency Fr, based on the bearing parameters set by the host computer, the bearing fault characteristic frequency is calculated, and the characteristic frequency and amplitude of the bearing are searched to obtain the rolling bearing characteristic frequency historical trend sequence FB: First, the bearing parameters of the device where the wireless vibration sensor is located are obtained. These parameters usually include information such as the type of bearing (such as deep groove ball bearings, tapered roller bearings, etc.), inner and outer diameter dimensions, number of rollers, and bearing cage structure. Through these parameters, the bearing fault frequency calculation formula can be applied, and the rotation frequency Fr can be used as input to calculate a series of bearing fault characteristic frequencies, such as ball rotation frequency, inner and outer ring fault frequency, etc. These frequencies are typical frequency points that appear in the vibration signal when the bearing fails, and can reflect the abnormality of the internal structure of the bearing.

[0065] Next, based on the spectrum analysis results of the wireless vibration sensor edge computing, the above-calculated bearing fault characteristic frequencies are searched to identify the vibration amplitude at each characteristic frequency point. This process essentially compares the spectrum data with the theoretical fault characteristic frequency, finds the peak frequency closest to the theoretical frequency in the actual vibration signal, and records its amplitude. Over time, these amplitude data will be continuously collected and stored as part of the rolling bearing characteristic frequency historical trend sequence FB, providing time series data on the health of the bearing.

[0066] Optionally, based on the calculated rotational frequency Fr, the gear meshing frequency is calculated according to the gear parameters set by the host computer, and the frequency and amplitude of the gear meshing frequency are searched to obtain the gearbox characteristic frequency historical trend sequence FG: First, the relevant parameters of the gearbox are obtained from the host computer, including key information such as gear module, number of teeth, gear center distance, and gear speed. These parameters are crucial for calculating the gear meshing frequency, which is directly related to the number of teeth and speed of the gear. Using the rotational frequency Fr, combined with the gear parameters, and applying the gear meshing frequency calculation formula, the theoretical meshing frequency of the gear under normal working conditions is calculated.

[0067] Then, the calculated gear meshing frequency is searched in detail in the spectrum analysis results of the edge computing module of the wireless vibration sensor. Spectrum analysis can give the intensity distribution of the vibration signal at each frequency point. This search process compares the theoretical frequency with the actual spectrum data, identifies the frequency point closest to the theoretical value, and records the vibration intensity at that point.

[0068] As time goes by, these vibration intensity data at the gear meshing frequency will be continuously recorded to form a gearbox characteristic frequency historical trend sequence FG.

[0069] Optionally, based on the real-time intensity of the sensor Ins and intensity historical data series I InsTrend , 6 types of time-domain dimensionless features (skewness factor historical trend sequence I SkewTrend , Kurtosis factor historical trend sequence I KurtTrend , Waveform Factor Historical Trend Series I ShapeTrend , Peak Factor Historical Trend Sequence I CresTrend 、Pulse Factor Historical Trend Sequence I ImpTrend , Margin Factor Historical Trend Series I ClearTrend ) Comprehensively calculate the health index of the equipment. The above sequence factors are calculated as follows:

[0070]

[0071] I Cres =x p / x RMS (4)

[0072]

[0073] In the formula, x(n) represents the vibration data waveform collected by the sensor, N represents the number of points in the data waveform, represents the mean of the waveform data, σ x Indicates the standard deviation of the waveform data, x RMS Represents the RMS value of the waveform data, x p Indicates the peak value of the waveform data.

[0074] Step S206, determining the value of the development trend indicator corresponding to each first trend sequence according to the first trend sequence.

[0075] In the technical solution provided in step S206, determining the value of the development trend indicator corresponding to each first trend sequence based on the first trend sequence includes: filtering the first trend sequence; determining the second trend sequence based on the filtered first trend sequence; and determining the value of the development trend indicator based on the second trend sequence.

[0076] Optionally, the second trend sequence is determined according to the following formula: Among them, Y = {y1, y2, ..., yn} is the second trend sequence, X = {x1, x2, ..., xn} is the first trend sequence, t is the sampling time corresponding to each sampling point in the first trend sequence, and λ is a preset regularization parameter.

[0077] Optionally, the value of the development trend indicator is determined according to the following formula: Wherein, Y={y1, y2, ..., yn} is the second trend sequence, and Δt is the time difference corresponding to each adjacent sampling point in the second trend sequence.

[0078] Optionally, determining the second trend sequence includes:

[0079] 1) The ratio f1 of the real-time intensity to the intensity threshold is used as the severity of exceeding the 1 / 2 / 3 order threshold of intensity;

[0080] 2) The intensity interval slope and f2 are used as the degree measure of the intensity development trend, and the calculation method is as follows:

[0081] 1. Obtain intensity historical trend sequence X = {x1, x2, ..., xn};

[0082] 2. Filter X and extract its overall trend to obtain Y = {y1, y2, ..., yn}. The formula is as follows, where the minimization optimization method is a general method and will not be repeated here:

[0083]

[0084] 3. Calculate the interval slope and f2 of Y as follows:

[0085]

[0086] Wherein, Δt is the time difference between adjacent sampling points in the Y sequence.

[0087] The interval slopes of the six dimensionless indicators and f3, f4, f5, f6, f7, f8, as well as the historical trend sequence of frequency domain characteristics, the historical trend sequence of rolling bearing characteristic frequency, the interval slopes of the historical trend sequence of gearbox characteristic frequency and f9 (1-12), f10, f11 are used as the degree measurement of the development trend of time domain indicators, where the corresponding relationship is shown in the following table, and the calculation method is the same as above;

[0088]

[0089] Step S208: determining a health degree measurement value according to the development trend indicator and a preset weight corresponding to the development trend indicator.

[0090] Optionally, a weighted sum is performed based on the above indicators to obtain a health metric value of the motor: Where wi is the weight of each indicator to measure health.

[0091] Step S210: Upload the first trend sequence, the development trend indicator, and the health metric value to a server for determining the status of the mobile device.

[0092] Optionally, the wireless vibration sensor can process and filter data locally on the device and only transmit important data results (first trend sequence, development trend indicators, health metrics) to the cloud or central server, reducing the amount of transmitted data and network bandwidth usage, better protecting the privacy and security of sensor data, and reducing the data transmission frequency and energy consumption of the wireless vibration sensor, thereby extending the battery life of the sensor device.

[0093] Optionally, Figure 5 A characteristic value calculation process is shown, such as Figure 5 As shown, the original waveform data is used to calculate the time domain characteristics, frequency domain amplitude, vibration intensity, vibration intensity trend, gear meshing frequency amplitude, bearing characteristic frequency amplitude, harmonic characteristics, and then the equipment health is calculated, and the calculation results are transmitted to the server through communication.

[0094] Through the above steps, it is possible to reduce data transmission delay and bandwidth occupancy, realize real-time processing and response of vibration data, improve the efficiency and performance of the sensor system, and in terms of edge feature calculation, integrate time-frequency domain features, impact features, etc., effectively realize the state monitoring and health assessment of rotating equipment. Edge computing wireless vibration sensors can be used to monitor and analyze the vibration of equipment, realize equipment health status monitoring, preventive maintenance and fault diagnosis, and improve equipment reliability and production efficiency. Specifically, this application has the following advantages:

[0095] (1) Wireless vibration sensors add edge computing functions. Real-time data processing and analysis of the collected vibration data can be performed to reduce the motor equipment fault assessment cycle and quickly characterize the current motor equipment operation stability. With the help of edge computing, wireless vibration sensors can process and filter data locally on the device and only transmit important data results to the cloud or central server, reducing the amount of transmitted data and network bandwidth occupation, which can better protect the privacy and security of sensor data, reduce the data transmission frequency and energy consumption of wireless vibration sensors, and extend the battery life of sensor devices. Multiple eigenvalues ​​are extracted from the vibration waveform on the edge side of the sensor, including the characteristic frequency amplitude of key components such as bearings and gears, which can not only accurately identify the type of mechanical fault, but also greatly reduce the amount of data transmission and improve the transmission stability of wireless sensors. The multiple eigenvalues ​​calculated by edge computing are more intuitive and easy to use, the amount of data transmitted is smaller, and there is no need to manually judge the equipment status after multi-layer forwarding of data. Wireless communication is more stable and the packet loss rate is reduced, making the use of wireless vibration sensors in industrial scenarios more reliable.

[0096] Multi-index feature calculation and joint evaluation function. In addition to vibration waveform filtering, time domain feature calculation, and frequency domain feature calculation, bearing characteristic value calculation, gear meshing characteristic value calculation, and equipment health evaluation are also taken into account, and equipment failure conditions can be analyzed from multiple angles. The time-frequency domain characteristics of edge computing and the frequency amplitude of key components such as bearings and gears can effectively reflect the presence and type of equipment failures. The health value will effectively evaluate the overall operating status of the current equipment. In addition to conventional vibration waveform filtering, time domain feature calculation, and frequency domain feature calculation, bearing characteristic value calculation, gear meshing characteristic value, and equipment health evaluation are also taken into account, which can achieve a comprehensive evaluation of equipment in multiple dimensions.

[0097] Equipment health assessment method based on multi-feature thresholds and trend changes. Equipment health can be used to assess the real-time operating status of equipment. This patent considers multiple eigenvalues ​​and constructs a comprehensive equipment health assessment system based on threshold values ​​and trend changes, which can more comprehensively reflect the health status of the equipment. It comprehensively and comprehensively assesses the operating status of the equipment based on the threshold and trend changes of six major categories of time-domain dimensionless eigenvalues ​​such as vibration intensity, waveform factors, etc., to facilitate personnel to judge the health status of the equipment in real time. Equipment health can be used to assess the real-time operating status of the equipment. The method embodiment of this application constructs a comprehensive equipment health assessment system based on intensity threshold values ​​and multi-feature trend changes, which can more comprehensively reflect the health status of the equipment.

[0098] The present application embodiment provides a device for detecting the state of a mobile device. Figure 6 is a schematic diagram of the structure of the device, such as Figure 6As shown, the device includes: an acquisition module 60, which is used to acquire vibration data of the moving equipment, wherein the vibration data includes at least one of the following: a vibration signal sequence and original vibration acceleration data, and the moving equipment is a rotating moving equipment; a first determination module 62, which is used to determine the first trend sequence of the moving equipment based on the vibration data, wherein the first trend sequence includes at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequency, a historical trend sequence of gearbox characteristic frequency, a historical trend sequence of vibration intensity, and a historical trend sequence of dimensionless characteristics in time domain; a second determination module 64, which is used to determine the value of the development trend indicator corresponding to each first trend sequence based on the first trend sequence; a third determination module 66, which is used to determine the health measurement value based on the development trend indicator and the preset weight corresponding to the development trend indicator; an upload module 68, which is used to upload the first trend sequence, the development trend indicator, and the health measurement value to a server used to determine the status of the moving equipment.

[0099] In some embodiments of the present application, the first determination module 62 determines the first trend sequence of the moving equipment based on the vibration data, including: performing a fast Fourier transform on the vibration signal sequence to obtain a first frequency value sequence and a first amplitude sequence corresponding to the first frequency value sequence; screening the peak points in the first amplitude sequence whose relative height is greater than a preset threshold, and adding the frequency value corresponding to the peak point to the second frequency value sequence, wherein the relative height is the difference between the peak value and the valley value within a preset interval; determining multiple candidate frequency points in the second frequency value sequence based on the preset interval and the preset interval; determining the target frequency point from the candidate frequency points, and using the frequency corresponding to the target frequency point as the rotation frequency of the moving equipment; determining the historical trend sequence of the frequency domain characteristics of the moving equipment, the historical trend sequence of the characteristic frequency of the rolling bearing, and the historical trend sequence of the characteristic frequency of the gearbox based on the rotation frequency.

[0100] In some embodiments of the present application, the first determination module 62 determines the target frequency point from the candidate frequency points, including: determining multiple multiple frequency values ​​corresponding to the candidate frequency point; determining the frequency value with the smallest difference with each multiple frequency value in the second frequency value sequence as the approximate frequency value corresponding to each multiple frequency value; determining the sum of the differences between each multiple frequency value and the approximate frequency value corresponding to the multiple frequency value as the sum of the differences of the candidate frequency points; and determining the candidate frequency point with the smallest sum of the differences as the target frequency point.

[0101] In some embodiments of the present application, the second determination module 64 determines the value of the development trend indicator corresponding to each first trend sequence based on the first trend sequence, including: filtering the first trend sequence; determining the second trend sequence based on the filtered first trend sequence; and determining the value of the development trend indicator based on the second trend sequence.

[0102] In some embodiments of the present application, the second determination module 64 determines the second trend sequence according to the following formula: Among them, Y = {y1, y2, ..., yn} is the second trend sequence, X = {x1, x2, ..., xn} is the first trend sequence, t is the sampling time corresponding to each sampling point in the first trend sequence, and λ is a preset regularization parameter.

[0103] In some embodiments of the present application, the second determination module 64 determines the value of the development trend indicator according to the following formula: Wherein, Y={y1, y2, ..., yn} is the second trend sequence, and Δt is the time difference corresponding to each adjacent sampling point in the second trend sequence.

[0104] In some embodiments of the present application, vibration data is collected by a sensor, and the sensor is also used to locally determine a first trend sequence, a development trend indicator, and a health metric value, and store the first trend sequence, the development trend indicator, and the health metric value.

[0105] It should be noted that the various modules in the above-mentioned dynamic equipment status detection device can be program modules (for example, a set of program instructions that implement a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0106] An embodiment of the present application provides a non-volatile storage medium, in which a program is stored, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the following dynamic device status detection method: collecting vibration data of the dynamic device, wherein the vibration data includes at least one of the following: a vibration signal sequence and original vibration acceleration data, and the dynamic device is a rotating dynamic device; determining a first trend sequence of the dynamic device based on the vibration data, wherein the first trend sequence includes at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequency, a historical trend sequence of gearbox characteristic frequency, a historical trend sequence of vibration intensity, and a historical trend sequence of dimensionless characteristics in time domain; determining the value of a development trend indicator corresponding to each first trend sequence based on the first trend sequence; determining a health measurement value based on the development trend indicator and a preset weight corresponding to the development trend indicator; uploading the first trend sequence, the development trend indicator, and the health measurement value to a server for determining the status of the dynamic device.

[0107] An embodiment of the present application provides an electronic device, comprising: a memory and a processor, the processor being used to run a program stored in the memory, wherein the program executes the following dynamic device status detection method when it is running: collecting vibration data of the dynamic device, wherein the vibration data includes at least one of the following: a vibration signal sequence and original vibration acceleration data, and the dynamic device is a rotating dynamic device; determining a first trend sequence of the dynamic device based on the vibration data, wherein the first trend sequence includes at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequency, a historical trend sequence of gearbox characteristic frequency, a historical trend sequence of vibration intensity, and a historical trend sequence of dimensionless characteristics in time domain; determining a value of a development trend indicator corresponding to each first trend sequence based on the first trend sequence; determining a health measurement value based on the development trend indicator and a preset weight corresponding to the development trend indicator; uploading the first trend sequence, the development trend indicator, and the health measurement value to a server for determining the status of the dynamic device.

[0108] An embodiment of the present application provides a computer program product, including a computer program, which implements the following dynamic equipment status detection method when executed by a processor: collecting vibration data of the dynamic equipment, wherein the vibration data includes at least one of the following: a vibration signal sequence and original vibration acceleration data, and the dynamic equipment is a rotating dynamic equipment; determining a first trend sequence of the dynamic equipment based on the vibration data, wherein the first trend sequence includes at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequency, a historical trend sequence of gearbox characteristic frequency, a historical trend sequence of vibration intensity, and a historical trend sequence of dimensionless characteristics in time domain; determining the value of a development trend indicator corresponding to each first trend sequence based on the first trend sequence; determining a health measurement value based on the development trend indicator and a preset weight corresponding to the development trend indicator; uploading the first trend sequence, the development trend indicator, and the health measurement value to a server for determining the status of the dynamic equipment.

[0109] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0111] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0112] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0113] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0114] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for detecting the state of a moving device, characterized in that: include: Collecting vibration data of a moving device, wherein the vibration data includes at least one of the following: a vibration signal sequence and original vibration acceleration data, and the moving device is a rotating moving device; Determine a first trend sequence of the moving equipment according to the vibration data, wherein the first trend sequence includes at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequency, a historical trend sequence of gearbox characteristic frequency, a historical trend sequence of vibration severity, and a historical trend sequence of dimensionless characteristics in time domain; Determining, according to the first trend sequence, a value of a development trend indicator corresponding to each of the first trend sequences; Determining a healthiness metric value according to the development trend indicator and a preset weight corresponding to the development trend indicator; The first trend sequence, development trend indicator, and health metric are uploaded to a server for determining the status of the mobile device.

2. The method for detecting the state of a moving device according to claim 1, characterized in that: Determining a first trend sequence of the moving device according to the vibration data includes: Performing a fast Fourier transform on the vibration signal sequence to obtain a first frequency value sequence and a first amplitude value sequence corresponding to the first frequency value sequence; Filtering the peak points whose relative height is greater than a preset threshold in the first amplitude sequence, and adding the frequency values ​​corresponding to the peak points to the second frequency value sequence, wherein the relative height is the difference between the peak value and the valley value within a preset interval; Determining a plurality of candidate frequency points in the second frequency value sequence according to a preset interval and a preset interval; Determine a target frequency point from the candidate frequency points, and use the frequency corresponding to the target frequency point as the rotation frequency of the moving device; The historical trend sequence of the frequency domain characteristics of the moving equipment, the historical trend sequence of the characteristic frequency of the rolling bearing and the historical trend sequence of the characteristic frequency of the gearbox are determined according to the rotation frequency.

3. The method for detecting the state of a moving device according to claim 2, characterized in that: Determining a target frequency point from the candidate frequency points includes: Determine a plurality of multiplied frequency values ​​corresponding to the candidate frequency points; Determine, in the second frequency value sequence, a frequency value with the smallest difference from each of the multiple frequency values ​​as an approximate frequency value corresponding one-to-one to each of the multiple frequency values; Determine the sum of the differences between each of the doubled frequency values ​​and the approximate frequency value corresponding to the doubled frequency value as the sum of the differences of the candidate frequency points; The candidate frequency point having the smallest difference is determined as the target frequency point.

4. The method for detecting the state of a moving device according to claim 1, characterized in that: Determining the value of the development trend indicator corresponding to each of the first trend sequences according to the first trend sequence includes: Performing filtering processing on the first trend sequence; Determining a second trend sequence according to the first trend sequence after filtering; The value of the development trend indicator is determined according to the second trend sequence.

5. The method for detecting the state of a moving device according to claim 4, characterized in that: The second trend sequence is determined according to the following formula: Among them, Y={y1, y2, ..., yn} is the second trend sequence, X={x1, x2, ..., xn} is the first trend sequence, t is the sampling time corresponding to each sampling point in the first trend sequence, and λ is a preset regularization parameter.

6. The method for detecting the state of a moving device according to claim 4, characterized in that: The value of the development trend indicator is determined according to the following formula: Among them, Y={y1, y2, ..., yn} is the second trend sequence, and Δt is the time difference corresponding to each adjacent sampling point in the second trend sequence.

7. The method for detecting the state of a moving device according to claim 1, characterized in that: The vibration data is collected by a sensor, and the sensor is also used to locally determine the first trend sequence, the development trend indicator and the health metric value, and store the first trend sequence, the development trend indicator and the health metric value.

8. A device for detecting the state of a moving device, characterized in that: include: A collection module, used for collecting vibration data of a moving device, wherein the vibration data includes at least one of the following: a vibration signal sequence and original vibration acceleration data, and the moving device is a rotating moving device; A first determination module is used to determine a first trend sequence of the moving equipment according to the vibration data, wherein the first trend sequence includes at least one of the following: a historical trend sequence of frequency domain characteristics, a historical trend sequence of rolling bearing characteristic frequency, a historical trend sequence of gearbox characteristic frequency, a historical trend sequence of vibration severity, and a historical trend sequence of dimensionless characteristics in time domain; A second determining module, configured to determine, based on the first trend sequences, values ​​of development trend indicators corresponding to the first trend sequences; A third determination module is used to determine a health degree measurement value according to the development trend indicator and a preset weight corresponding to the development trend indicator; An uploading module is used to upload the first trend sequence, development trend indicator, and health measurement value to a server used to determine the status of the dynamic device.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the method for detecting the state of a mobile device according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the program executes the dynamic device status detection method described in any one of claims 1 to 7 when running.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the mobile device status detection method described in any one of claims 1 to 7 are implemented.

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