Rolling mill running state monitoring method, system and equipment and storage medium

Abstract

The invention provides a rolling mill running state monitoring method, system and device and a storage medium, and the method comprises the steps that various signal data of rolling mill equipment at all monitoring points are obtained, and the signal data comprise low-frequency signals and high-frequency signals; the low-frequency signal is a temperature signal, a ferromagnetic wear particle concentration signal, a pressure signal, a displacement signal, a current signal or a voltage signal, and the high-frequency signal comprises a rotating speed signal and a vibration signal; whether each low-frequency signal exceeds a preset threshold value or not is detected, early warning information is generated based on a detection result, and each low-frequency signal has a preset threshold value in a one-to-one correspondence mode; calculating a time domain feature, a frequency domain feature and a time-frequency domain feature according to the high-frequency signal, diagnosing the time domain feature, the frequency domain feature and the time-frequency domain feature, and generating alarm information based on a diagnosis result; and outputting and displaying the early warning information and the alarm information. Therefore, by integrating various types of signals, the running state of the rolling mill equipment can be monitored in real time more comprehensively and accurately.

Description

Technical Field

[0001] The present application relates to the technical field of rolling mill monitoring, and in particular to a rolling mill operating status monitoring method, system, equipment and storage medium. Background Art

[0002] As key equipment in the field of metal processing, rolling mills undertake the important task of converting metal materials into various specifications of plates, strips, pipes and profiles through rolling processes. In the steel and non-ferrous metal industries, rolling mills are widely used and are the core tools to ensure the quality and efficiency of material production. With the continuous growth of modern industry's demand for metal materials, rolling mill technology is also constantly evolving, striving to achieve higher levels of precision, efficiency and automation. The rolling mill is mainly composed of key components such as rollers, frames, roll gap adjustment devices, transmission devices, lubrication systems and control systems. Any change or failure of any component will have a comprehensive traction efficiency on the operation of the entire rolling mill equipment. For example, the transmission device, as the power core of the rolling mill equipment, mainly realizes the transmission and conversion of power through gears, bearings and other components. Its internal structure is complex and the working environment is relatively harsh. In actual operation, factors such as heavy loads, strong impacts, variable speeds, high temperatures and dust will seriously wear and damage the transmission device, causing it to malfunction, including bearing wear, gearbox wear, pitting and broken teeth, which will not only affect the normal operation of the rolling mill, but may also trigger a chain reaction, resulting in damage to the entire equipment, causing huge economic losses and safety hazards.

[0003] In the related art, in order to avoid failures of rolling mill equipment, the status of rolling mill components is mainly checked through regular maintenance. On the one hand, although it can prevent the occurrence of failures to a certain extent, it is impossible to capture subtle changes in the operating status of the rolling mill in real time, making it difficult to discover failures in time and unable to remind production companies in the first place; on the other hand, the relationship between failures occurring during the operation of the rolling mill and failure sign signals (such as temperature and vibration) is complicated, resulting in a problem of low diagnostic accuracy. Summary of the invention

[0004] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0005] In view of the above-mentioned shortcomings of the prior art, the present application discloses a rolling mill operation status monitoring method, system, device and storage medium to solve at least one of the above-mentioned technical problems.

[0006] In the first aspect, the present application provides a method for monitoring the operating status of a rolling mill, the method comprising: acquiring multiple signal data of the rolling mill equipment at each monitoring point, the signal data comprising low-frequency signals and high-frequency signals, the low-frequency signals being temperature signals, ferromagnetic wear particle concentration signals, pressure signals, displacement signals, current signals or voltage signals, and the high-frequency signals comprising speed signals and vibration signals; detecting whether each of the low-frequency signals exceeds a preset threshold, and generating early warning information based on the detection results, each of the low-frequency signals corresponding to the preset threshold one by one; calculating time domain characteristics, frequency domain characteristics and time-frequency domain characteristics according to the high-frequency signals, and diagnosing the time domain characteristics, the frequency domain characteristics and the time-frequency domain characteristics, and generating alarm information based on the diagnosis results; outputting and displaying the early warning information and the alarm information to realize the monitoring of the operating status of the rolling mill.

[0007] In one embodiment of the present application, after the early warning information and the alarm information are output, it also includes: transmitting the early warning information or the early warning information to a local server for display, and performing a primary verification on the early warning information or the alarm information; if the result of the primary verification is that the diagnosis is correct, uploading the early warning information or the alarm information to a cloud server, and establishing a one-to-many connection between the cloud server and the local server; displaying the early warning information or the alarm information from the cloud server through the remote terminal, and performing a secondary verification on the early warning information or the alarm information.

[0008] In one embodiment of the present application, the detection of whether each of the low-frequency signals exceeds a preset threshold and the generation of warning information based on the detection result include: if any of the low-frequency signals is a temperature signal, and the temperature signal is greater than the corresponding preset temperature threshold, a first-level warning information is generated; if only any one target low-frequency signal exceeds the corresponding preset threshold, a second-level warning information is generated, the target low-frequency signal is the low-frequency signal other than the temperature signal, and the severity of the second-level warning information is lower than the first-level warning information; if the temperature signal is less than or equal to the preset temperature threshold, and at least two of the target low-frequency signals exceed the corresponding preset threshold, the first-level warning information is generated.

[0009] In one embodiment of the present application, the time domain features, frequency domain features and time-frequency domain features are calculated based on the high-frequency signal, and the time domain features, the frequency domain features and the time-frequency domain features are diagnosed, including: obtaining the fault characteristic frequency of the rolling mill equipment, and determining whether the speed signal is stable; if the speed signal is stable, calculating the vibration signal in the time domain and frequency domain to determine the time domain features, the frequency domain features and the time-frequency domain features; if the speed signal is not stable, resampling the vibration signal, and calculating the time domain and frequency domain based on the resampled vibration signal to determine the time domain features, the frequency domain features and the time-frequency domain features; diagnosing whether there are abnormalities in the time domain features, the frequency domain features and the time-frequency domain features in combination with the fault characteristic frequency.

[0010] In a second aspect, the present application provides a rolling mill operation status monitoring system, the system comprising: a data analysis platform, a local server, a cloud server and a remote terminal; the data analysis platform is used to obtain a variety of signal data of the rolling mill equipment at each monitoring point, the signal data including low-frequency signals and high-frequency signals, the low-frequency signals are temperature signals, ferromagnetic wear particle concentration signals, pressure signals, displacement signals, current signals or voltage signals, and the high-frequency signals include speed signals and vibration signals; detecting whether each of the low-frequency signals exceeds a preset threshold, generating early warning information based on the detection results, each of the low-frequency signals corresponding to the preset threshold one by one; calculating the time domain characteristics, frequency domain characteristics and time-frequency domain characteristics according to the high-frequency signals, and diagnosing the time domain characteristics, the frequency domain characteristics and the time-frequency domain characteristics, and generating an alarm information based on the diagnosis results; The early warning information and the alarm information are output to the local server; the local server establishes a connection with the data analysis platform to display the early warning information or the early warning information, and performs a verification on the early warning information or the alarm information; if the result of the one verification is that the diagnosis is correct, the early warning information or the alarm information is uploaded to the cloud server, and a one-to-many connection is established between the cloud server and the local server; the cloud server establishes a one-to-many connection with the local server and the remote terminal respectively, to store the early warning information or the early warning information, and forward it to the remote terminal; the remote terminal is used to display the early warning information or the alarm information, and perform a secondary verification on the early warning information or the alarm information, so as to realize remote monitoring of the operating status of the rolling mill.

[0011] In one embodiment of the present application, the data analysis platform includes a processor, a communication module and an analog-to-digital conversion module, the processor is connected to the communication module and the analog-to-digital conversion module respectively, and the communication module also establishes a connection with the local server; the communication module is externally connected to a sensor for collecting low-frequency signals arranged on the rolling mill equipment, and is used to collect the low-frequency signals and transmit them to the processor, and the location where the sensor is arranged is the monitoring point; the analog-to-digital conversion module is externally connected to the sensor for collecting high-frequency signals arranged on the rolling mill equipment, and is used to collect the high-frequency signals and transmit them to the processor after analog-to-digital conversion; the processor is used to perform diagnosis and processing on the low-frequency signals and the high-frequency signals according to a preset diagnostic algorithm, generate the early warning information and / or the early warning information, and transmit them to the communication module for output.

[0012] In one embodiment of the present application, the local server is also used to construct a transmission structure framework of the rolling mill equipment, and determine component parameters based on the transmission structure framework; calculate the fault characteristic frequency of the rolling mill equipment according to the component parameters, and send it to the data analysis platform, so that the data analysis platform can diagnose whether there is an abnormality in the time domain characteristics, the frequency domain characteristics and the time-frequency domain characteristics in combination with the fault characteristic frequency.

[0013] In one embodiment of the present application, the local server is also used to configure the channel parameters, communication parameters, acquisition parameters, measurement point parameters and diagnostic parameters of each signal data, and send them to the data analysis platform.

[0014] In the third aspect, the present application also provides an electronic device, comprising: a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the rolling mill operation status monitoring method as described in the above embodiment.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer executes the method described in the above embodiments.

[0016] Beneficial effects of the present application: The present application proposes a method, system, device and storage medium for monitoring the operation status of a rolling mill. By integrating various types of signal data of rolling mill equipment, the temperature, oil, current, voltage, displacement, pressure, vibration and speed signals of the rolling mill are integrated to monitor whether there are abnormalities and faults in the rolling mill equipment, and then generate early warning information and / or alarm information, which can monitor the operation status of the rolling mill equipment in a more comprehensive and accurate manner in real time, and output the early warning information and alarm information in a timely manner for display, thereby ensuring the timeliness of the monitoring of the operation status of the rolling mill and helping production enterprises to discover problems at the early stage of abnormalities of the rolling mill.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0019] Figure 1 is a flow chart of a rolling mill operation status monitoring method shown in an exemplary embodiment of the present application;

[0020] Figure 2 is a block diagram of a rolling mill operation status monitoring system shown in an exemplary embodiment of the present application;

[0021] Figure 3 is a schematic diagram of the overall framework of a monitoring system shown in an exemplary embodiment of the present application;

[0022] Figure 4 is a schematic diagram of an overall process of monitoring the operation status of a rolling mill shown in an exemplary embodiment of the present application;

[0023] Figure 5 It is a structural diagram of a computer system suitable for implementing the electronic device of the present application, shown as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will describe the implementation methods of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, not for limiting the scope of protection of the present application.

[0025] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the form, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0026] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0027] See also Figure 1 , is a flow chart of a rolling mill operation status monitoring method shown in an exemplary embodiment of the present application. The rolling mill operation status monitoring method can be executed by a client, such as Figure 1 As shown, in an exemplary embodiment, the rolling mill operation status monitoring method includes at least steps S110 to S140, which are described in detail as follows:

[0028] Step S110, obtaining various signal data of the rolling mill equipment at each monitoring point, the signal data including low-frequency signals and high-frequency signals, the low-frequency signals are temperature signals, ferromagnetic wear particle concentration signals, pressure signals, displacement signals, current signals or voltage signals, and the high-frequency signals include speed signals and vibration signals.

[0029] In one embodiment of the present application, the location of the monitoring point is synchronously determined based on the layout points of various types of sensors in the rolling mill equipment, and can be adjusted according to actual needs. Different types of rolling mill equipment may have different structures, and the layout points of the sensors will also be adjusted accordingly, without any restrictions. For example, the ferromagnetic wear particle sensor is arranged in the lubricating oil system of the rolling mill equipment, such as the lowest point of the return oil pipeline before the filter, to effectively capture and monitor the ferromagnetic wear particles in the oil; the pressure sensor is arranged on the arch of the frame and the part connected to the roller bearing seat to monitor the magnitude of the rolling force during the rolling process; the speed sensor is arranged on the output shaft of the transmission device to monitor the speed change of the roller in real time; the vibration sensor or acceleration sensor is arranged on the key components of the transmission device, such as the gear box, bearing, etc., to monitor the vibration in real time, discover potential faults in time, and avoid equipment damage and downtime; the temperature sensor is arranged in the heat-sensitive parts of the rolling mill equipment, such as the bearing seat, lubricating oil system, motor winding, etc., to accurately reflect the temperature state of the equipment; the strain gauge sensor is arranged in the stress-bearing parts of the rolling mill equipment, such as the roller bearing seat, the frame column, etc., to reflect the strain state of the equipment (such as displacement change); the current and voltage sensors are arranged in the electrical system of the rolling mill equipment, such as the motor input end, the transformer output end, etc., to accurately reflect the current and voltage state of the equipment.

[0030] Through the above method, by rationally arranging various types of sensors in the rolling mill equipment, real-time monitoring of signals such as vibration, temperature, rotation speed, oil, current, voltage, strain, pressure, etc. of the rolling mill equipment can be realized, which helps to understand the operating status of the rolling mill more comprehensively and accurately, and improve the accuracy and timeliness of subsequent fault diagnosis.

[0031] Step S120, detecting whether each low-frequency signal exceeds a preset threshold, and generating warning information based on the detection result, each low-frequency signal has a one-to-one corresponding preset threshold.

[0032] In one embodiment of the present application, if any low-frequency signal exceeds the corresponding preset threshold, the detection result is that the low-frequency signal is abnormal, and an early warning message is generated that carries at least the abnormal low-frequency signal and the location information of the monitoring point to which it belongs; if any low-frequency signal exceeds the corresponding preset threshold, the detection result is that the low-frequency signal is normal, and no early warning message is generated. This helps to more comprehensively and accurately understand whether there is any abnormality in the operating status of the rolling mill.

[0033] Specifically, in order to more intuitively identify the abnormal type and severity of the rolling mill equipment, different warning levels are divided. Among them, if any low-frequency signal is a temperature signal, and the temperature signal is greater than the corresponding preset temperature threshold, a first-level warning information is generated; if only any target low-frequency signal exceeds the corresponding preset threshold, a second-level warning information is generated, and the target low-frequency signal is a low-frequency signal other than the temperature signal. The severity of the second-level warning information is lower than that of the first-level warning information; if the temperature signal is less than or equal to the preset temperature threshold, and at least two target low-frequency signals exceed the corresponding preset threshold, a first-level warning information is generated. In this way, it is helpful to formulate corresponding response strategies according to different warning levels, and help relevant personnel quickly locate the location of the problem.

[0034] In one embodiment of the present application, since temperature is one of the important parameters reflecting the operating status of the rolling mill equipment, excessively high temperature may mean that the equipment has problems such as overheating, wear, and poor lubrication. If these problems are not handled in time, they may cause equipment damage or production accidents. In the actual operation of the rolling mill equipment, the measurement and monitoring of the temperature signal are relatively stable, and the false alarm rate is low. Therefore, setting the warning level of the temperature signal to a higher level can timely discover and deal with potential safety hazards, and greatly avoid serious failures of the rolling mill equipment. In addition, compared with the temperature signal, other low-frequency signals (such as vibration, speed, etc.) are also important, but usually do not immediately cause equipment damage or safety accidents. Therefore, their warning levels are set to a lower level to avoid them from developing into more serious failures. However, the operating status of the rolling mill equipment usually involves the synergy of multiple signal data. Relying on a single signal data (such as temperature signal) for warning may not fully reflect the actual status of the equipment. Therefore, when multiple low-frequency signals are abnormal at the same time, it means that the equipment is very likely to have a serious failure or an emergency failure is about to occur. Therefore, a first-level warning information is generated. In this way, a multi-level warning mechanism helps to timely and comprehensively discover potential failures and abnormalities of the rolling mill equipment, ensuring the stable operation and production safety of the equipment.

[0035] Step S130, calculating the time domain features, frequency domain features and time-frequency domain features according to the high-frequency signal, diagnosing the time domain features, frequency domain features and time-frequency domain features, and generating alarm information based on the diagnosis results.

[0036] Specifically, the fault characteristic frequency of the rolling mill equipment is obtained, and it is determined whether the speed signal is stable; if the speed signal is stable, the vibration signal is calculated in the time domain and frequency domain to determine the time domain characteristics, frequency domain characteristics and time-frequency domain characteristics; if the speed signal is not stable, the vibration signal is resampled, and the time domain and frequency domain calculations are performed based on the resampled vibration signal to determine the time domain characteristics, frequency domain characteristics and time-frequency domain characteristics; the fault characteristic frequency is combined to diagnose whether there is an abnormality in the time domain characteristics, frequency domain characteristics and time-frequency domain characteristics.

[0037] In one embodiment of the present application, since the rolling mill equipment is affected by a variety of external factors during operation, such as load changes, material hardness changes, rolling speed adjustments, etc., these will cause the speed signal of the rolling mill to change, that is, the statistical characteristics of the speed signal (such as mean, variance, etc.) cannot remain unchanged over time. If the speed signal is stable, then it can be considered that the operating state of the rolling mill equipment is relatively stable, and the vibration signal can be directly calculated in the time domain and frequency domain; if the speed signal is not stable, it will affect the amplitude and frequency components of the vibration signal, making the accuracy of the vibration signal analysis low. At this time, the vibration signal needs to be resampled, such as angular domain resampling, to eliminate the influence of speed changes on the vibration signal analysis.

[0038] In one embodiment of the present application, judging whether the speed signal is stable includes: calculating the statistical characteristics of the speed signal in the current preset first time period, if the statistical characteristics change in the current preset second time period, the speed signal is stable, if the statistical characteristics remain consistent in the current preset second time period, the speed is stable; wherein the preset first time period is less than the preset second time period, and the statistical characteristics are mean, variance, autocorrelation function or distribution law. Alternatively, monitoring the time domain waveform and power spectrum density of the speed signal, if the time domain waveform presents a similar shape and amplitude at each time point, and the power spectrum density remains constant or approximately constant within the frequency range, then the speed signal can be considered to be stable.

[0039] In one embodiment of the present application, the time domain index of the vibration signal includes at least the maximum value, the minimum value, the peak value, the average value, the absolute average amplitude, the variance, the standard deviation, the effective value, the root amplitude, the kurtosis index, the waveform index, the peak value index, the pulse index and the margin index. The expression for calculating the time domain index is as follows:

[0040] x max =max(x[n]) Formula (1)

[0041] x min =min(x[n]) Formula (2)

[0042] x p =max(|x[n]|) Formula (3)

[0043]

[0044] Wherein, x[n] in equations (1) to (14) represents the nth sampling value of the vibration signal in the current preset first time period, N is the signal length of the vibration signal, that is, the number of samples or sampling points contained in the vibration signal; X max Indicates the maximum value, that is, the maximum sampling value in the vibration signal; X min Represents the minimum value, that is, the minimum sampling value in the vibration signal; X p Represents the peak value, that is, the maximum absolute value sampled value in the vibration signal; represents the average value of the vibration signal, reflecting the overall level of the vibration signal; δ represents the variance, that is, the mean of the square of the difference between the vibration signal and its mean, reflecting the degree of dispersion of the vibration signal; σ x represents the standard deviation, that is, the square root of the variance, reflecting the discreteness of the vibration signal. rms Indicates effective value, X r represents the square root amplitude, K represents the kurtosis index, F represents the waveform index, C represents the peak index, I represents the pulse index, and L represents the margin index.

[0045] In one embodiment of the present application, the frequency domain characteristics of the vibration signal are converted into spectrum information by discrete Fourier transform, and then calculated, including but not limited to amplitude spectrum, envelope spectrum, power spectrum and inverse spectrum. The expression for calculating the frequency domain characteristics is as follows:

[0046]

[0047] E(u)=FFT[abs(H(x))] Formula (17)

[0048]

[0049] C p =IFFT[log G f ] Formula (19)

[0050] Wherein, F[k] in equations (15) to (19) represents the frequency domain signal, FFT[x] represents the Fourier transform of the time domain signal, x[n] represents the time domain signal of the vibration signal, N is the number of sampling points, n is the discrete time point index of the time domain signal, from 0 to N_1, k represents the index of the frequency domain or the frequency component, j is the imaginary unit used to construct the complex number; F x [k] represents the amplitude spectrum, which represents the amplitude of the vibration signal at different frequencies. R [k] represents the real part of the frequency domain signal, F I [k] represents the imaginary part of the frequency domain signal; E(u) represents the envelope spectrum, abs(H(x)) represents the envelope signal after envelope extraction (such as Hilbert transform, wavelet transform, etc.) of the frequency domain signal, FFT represents Fourier transform, G f represents the power spectrum, C p It stands for inverse Fourier transform and IFFT stands for inverse Fourier transform.

[0051] In one embodiment of the present application, the expression for calculating the time-frequency domain characteristics of the vibration signal is as follows:

[0052]

[0053] Where, STFT in equation (20) represents short-time Fourier transform, t c represents the time point, f represents the frequency, x[n] represents the time domain signal of the vibration signal, N is the number of sampling points, n is the discrete time point index of the time domain signal, k represents the index of the frequency domain or the frequency component, j is the imaginary unit used to construct the complex number, h is the sampling interval, h(nt c ) represents the position of x(n) in time.

[0054] In one embodiment of the present invention, the fault characteristic frequency is the key information for vibration fault diagnosis of rolling mill equipment. Different types of faults will cause different vibration frequencies. These frequencies have specific characteristics and can be used as the basis for fault diagnosis. For example, the vibration frequency of rotor imbalance is usually the power frequency, and the asymmetric vibration frequency of the gear coupling (with an intermediate gear sleeve) is the double frequency. By obtaining these fault characteristic frequencies and comparing them with the frequency components in the actual vibration signal, it is possible to preliminarily determine whether there is a fault in the rolling mill equipment and the type and location of the fault. Therefore, if it is determined that any time domain feature, frequency domain feature, or time-frequency domain feature is abnormal, and then the abnormal feature matches the known fault characteristic frequency, the type and location of the fault in the rolling mill equipment can be determined. In this way, the generated alarm information can carry the type and location of the fault, and the diagnosis is more accurate, which helps to complete the maintenance in the early stage of the fault.

[0055] In one embodiment of the present application, characteristic thresholds corresponding to the time domain index, frequency domain feature and time-frequency domain feature of the vibration signal can be set, and the time domain index, frequency domain feature and time-frequency domain feature can be compared with the corresponding characteristic thresholds respectively. If any time domain index, frequency domain feature or time-frequency domain feature exceeds the corresponding characteristic threshold, it is used as the target feature to determine whether it is abnormal. For example, taking the characteristic threshold corresponding to the peak value as the peak threshold as an example, if the peak value exceeds the peak threshold, then the peak value is abnormal. In addition, other diagnostic methods can also be used to determine whether the time domain index, frequency domain feature or time-frequency domain feature is abnormal, without limitation.

[0056] Step S140, outputting and displaying the early warning information and the alarm information to monitor the operation status of the rolling mill.

[0057] Specifically, the warning information or alarm information is transmitted to the local server for display, and the warning information or alarm information is verified once; if the result of the one verification is that the diagnosis is correct, the warning information or alarm information is uploaded to the cloud server, and a one-to-many connection is established between the cloud server and the local server; the warning information or alarm information from the cloud server is displayed through the remote terminal, and the warning information or alarm information is verified twice.

[0058] In one embodiment of the present application, when diagnosing a low-frequency signal, the number of times N that the warning information is generated can be set. During each sampling and analysis, if any low-frequency signal is diagnosed as abnormal, a warning message for the low-frequency signal is generated, and the number of records increases by 1. However, the warning message is not sent to the cloud server at this time, and the next sampling and analysis is waited for. The warning message is not output to the local server until the number of records is greater than N.

[0059] In one embodiment of the present application, the local server and the remote terminal can be configured to implement the preset diagnostic algorithm such as the above-mentioned step S120 and step S130, which can be implemented based on machine learning or other methods, and then the local server and the remote terminal can implement primary and secondary verification, improve the reliability of abnormal and fault diagnosis, and form a multi-level network topology structure with two-way communication, so as to monitor and judge the operating status of the rolling mill equipment on the production line and remotely. In addition, the inspection of the local server and the remote terminal can also adopt or be combined with the manual verification method to further optimize the diagnosis process.

[0060] In one embodiment of the present application, the local server and the remote terminal can also display the received signal data, test results, diagnosis results, early warning information and alarm information; the local server, the cloud server and the remote terminal can establish a connection through the Transmission Control Protocol (TCP). Among them, the cloud server can simultaneously connect to multiple different local servers and remote terminals, receive the above-mentioned status monitoring data of different rolling mill equipment and information fed back by user software, and classify, store, count and distribute all information. The cloud server can realize data interaction between the local server and the remote terminal according to the account logged in by the remote terminal. The account is bound to the unique identifier of at least one local server, and the local server and the rolling mill equipment correspond one to one.

[0061] See also Figure 2 , is a block diagram of a rolling mill operation status monitoring system shown in an exemplary embodiment of the present application. Figure 2 As shown, in an exemplary embodiment, the rolling mill operation status monitoring system includes at least a data analysis platform 210, a local server 220, a cloud server 230 and a remote terminal 240, which are described in detail as follows:

[0062] The data analysis platform 210 is used to obtain various signal data of the rolling mill equipment at each monitoring point, the signal data including low-frequency signals and high-frequency signals, the low-frequency signals are temperature signals, ferromagnetic wear particle concentration signals, pressure signals, displacement signals, current signals or voltage signals, and the high-frequency signals include speed signals and vibration signals; detect whether each low-frequency signal exceeds a preset threshold, generate warning information based on the detection result, and each low-frequency signal has a preset threshold corresponding to each other; calculate time domain features, frequency domain features and time-frequency domain features according to the high-frequency signal, diagnose the time domain features, frequency domain features and time-frequency domain features, and generate alarm information based on the diagnosis results; output the warning information and alarm information to the local server;

[0063] The local server 220 is connected to the data analysis platform to display the warning information or the alarm information and perform a verification on the warning information or the alarm information; if the result of the verification is that the diagnosis is correct, the warning information or the alarm information is uploaded to the cloud server, and a one-to-many connection is established between the cloud server and the local server;

[0064] The cloud server 230 has a one-to-many connection with the local server and the remote terminal, respectively, for storing the warning information or the warning information, and forwarding it to the remote terminal;

[0065] The remote terminal 240 is used to display the early warning information or the alarm information, and perform a secondary check on the early warning information or the alarm information to achieve remote monitoring of the operation status of the rolling mill.

[0066] In one embodiment of the present application, the specific manner of generating early warning information and alarm information has been described in the embodiment of the above method and will not be repeated here.

[0067] See also Figure 3 , is a schematic diagram of the overall framework of the monitoring system shown in an exemplary embodiment of the present application. Figure 3 As shown, the data analysis platform is connected to the rolling mill equipment, and the remote terminal can be a mobile phone, computer, tablet and other terminal devices.

[0068] Specifically, the data analysis platform includes a processor, a communication module and an analog-to-digital conversion module. The processor is connected to the communication module and the analog-to-digital conversion module respectively, and the communication module also establishes a connection with a local server; the communication module is externally connected to a sensor for collecting low-frequency signals arranged on the rolling mill equipment, and is used to collect low-frequency signals and transmit them to the processor. The location where the sensor is arranged is a monitoring point; the analog-to-digital conversion module is externally connected to a sensor for collecting high-frequency signals arranged on the rolling mill equipment, and is used to collect high-frequency signals and transmit them to the processor after analog-to-digital conversion; the processor is used to diagnose and process low-frequency signals and high-frequency signals according to a preset diagnostic algorithm, generate early warning information and / or early warning information, and transmit them to the communication module for output.

[0069] In one embodiment of the present application, the communication module includes a communication interface unit and a network unit. The communication interface unit is externally connected to a sensor for collecting low-frequency signals arranged on the rolling mill equipment, and the network unit is used to establish a network connection with a local server.

[0070] In one embodiment of the present application, the communication module and the analog-to-digital conversion module are used to process the collection and transmission of low-frequency and high-frequency signals respectively in order to make full use of their respective advantages. High-frequency signals are more susceptible to noise and interference during transmission, and require a higher sampling rate and resolution to accurately capture signal details. In this way, the monitoring accuracy and real-time performance of the rolling mill equipment are improved, ensuring the stable operation of the equipment.

[0071] In one embodiment of the present application, the signal data can be displayed as a waveform on the cloud server and the remote terminal, and the preset diagnostic algorithm configured the same as the data analysis platform is supported. The preset diagnostic algorithm is used to realize the above-mentioned detection of whether each low-frequency signal exceeds the preset threshold, and to generate warning information based on the detection result; calculate the time domain characteristics, frequency domain characteristics and time-frequency domain characteristics according to the high-frequency signal, and diagnose the time domain characteristics, frequency domain characteristics and time-frequency domain characteristics, and generate alarm information based on the diagnosis result.

[0072] Specifically, the local server is also used to construct a transmission structure framework for the rolling mill equipment, and determine component parameters based on the transmission structure framework; calculate the fault characteristic frequency of the rolling mill equipment according to the component parameters, and send it to the data analysis platform, so that the data analysis platform can diagnose whether there are abnormalities in the time domain characteristics, frequency domain characteristics, and time-frequency domain characteristics based on the fault characteristic frequency.

[0073] In one embodiment of the present application, the transmission structure framework is a layout between various components for transmitting power and torque, and mainly includes a frame, rollers, a transmission device (axle system, gears, bearings), a motor, a reduction gearbox and a lubrication station, etc.

[0074] In one embodiment of the present application, calculating the fault characteristic frequency according to the component parameters at least involves the following expression:

[0075]

[0076] f i =0.6zf Formula (25)

[0077] f o =0.4zf Formula (26)

[0078] f c =0.381~0.4zf Formula (27)

[0079]

[0080] f m =z h f g =z p f p Formula (29)

[0081] Among them, f in formula (21) to formula (29) i Indicates the bearing inner ring fault frequency, f o Indicates the bearing outer ring fault frequency, f c Indicates the bearing cage failure frequency, f b Indicates the bearing rolling element failure frequency, f mThe gear meshing frequency z is the number of rolling elements in the bearing, f is the rotation frequency, d is the rolling element diameter, D is the bearing median diameter, α is the rolling element contact angle, and z g is the number of teeth on the low-speed gear, f g is the low speed gear rotation frequency, z p is the number of teeth of the high-speed gear, f p is the high-speed gear rotation frequency. Among them, equations (25) to (28) are simplified expressions when equations (21) to (24) cannot obtain specific parameters.

[0082] In one embodiment of the present application, the local server is also used to configure the channel parameters, communication parameters, acquisition parameters, measurement point parameters and diagnostic parameters of each signal data, and send them to the data analysis platform. Among them, the communication parameters are used to describe the channel characteristics of signal transmission, such as the number of channels and isolation voltage; the communication parameters are used to describe the transmission characteristics of signal data in the communication channel, such as transmission rate, bandwidth, bandwidth and delay; the acquisition parameters may include sampling rate, bit depth, input range and accuracy; the measurement point parameters mainly include the location of the monitoring point, measurement range and sensitivity, etc.; the diagnostic parameters at least include preset thresholds and characteristic thresholds.

[0083] See also Figure 4 , is a schematic diagram of the overall process of monitoring the operation status of a rolling mill shown in an exemplary embodiment of the present application. Figure 4As shown, first, configure the transmission structure of the rolling mill system, calculate the characteristic frequency parameters and the fault characteristic frequency of the rolling mill equipment based on this, and then configure the diagnostic parameters and send them together to the corresponding data analysis platform; the data analysis platform diagnoses the low-frequency signal, including the diagnosis of temperature, ferromagnetic wear particle concentration, current / voltage, pressure and strain (acquired by strain gauge sensors, expressed in displacement or other forms). If the number of low-frequency signal anomalies is 1, a second-level warning message is sent to the plant-level server (i.e., the local server); if the number of low-frequency signal anomalies reaches 2, a first-level warning message is sent to the plant-level server; then, the data analysis platform diagnoses the high-frequency signal to determine whether the speed fluctuation is too large, that is, to determine whether the speed signal is stable. If the fluctuation is too large, the vibration signal is resampled in the angular domain and its time domain characteristics (i.e., time domain indicators), frequency domain characteristics and time-frequency domain characteristics are calculated. If the fluctuation is small, The time domain characteristics, frequency domain characteristics and time-frequency domain characteristics of the vibration signal are directly calculated to analyze whether the vibration signal is abnormal. If abnormal, a fault alarm, i.e., an alarm message, is sent to the factory-level server; the alarm message and early warning message are confirmed once (i.e., verified once) by the factory-level server. If there is a fault (i.e., the verification result is that the diagnosis is correct), the relevant signal data, early warning and alarm information are sorted out, and recorded as a diagnostic information to generate a log, which is uploaded to the cloud server for classification and storage; finally, the cloud server sends it to the user software (i.e., the remote terminal) for a second confirmation of the early warning information or alarm information. If there is no fault (i.e., the diagnostic result is a diagnostic error), it is fed back to the cloud server, the log is changed, and further fed back to the factory-level server, the diagnostic information is changed, and an update instruction is sent to the data analysis platform so that the data analysis platform cancels the erroneous early warning and alarm information and updates the corresponding diagnostic results or detection results.

[0084] It should be noted that the rolling mill operation status monitoring system provided in the above embodiment and the rolling mill operation status monitoring method provided in the above embodiment belong to the same concept, wherein the contents of the operations performed by each module have been described in detail in the method embodiment and will not be repeated here.

[0085] The rolling mill operation status monitoring method provided in the present application has the following advantages: first, by integrating various types of signal data of rolling mill equipment, the temperature, oil, current, voltage, displacement, pressure, vibration and speed signals during the operation of the rolling mill are comprehensively used to monitor whether the rolling mill equipment has abnormalities and faults, and then generate early warning information and / or alarm information, which can monitor the operation status of the rolling mill equipment in a more comprehensive and accurate real-time manner; second, even in the case of unstable speed, the vibration signal can be accurately diagnosed; third, the early warning information and alarm information are output and displayed in time, which ensures the timeliness of the rolling mill operation status monitoring and helps the production enterprise to discover problems in the early stage of the abnormality of the rolling mill; fourth, primary and secondary verification are implemented on the local server and the remote terminal to improve the reliability of abnormality and fault diagnosis, and form a multi-level network topology structure with two-way intercommunication.

[0086] The present application also provides an electronic device, comprising: a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement a rolling mill operating status monitoring method as in the above embodiment.

[0087] See also Figure 5 , shows a schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application. It should be noted that, Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0088] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503, such as executing the method in the above embodiment. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, ROM 502 and RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0089] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 509 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read therefrom is installed into the storage section 508 as needed.

[0090] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 509, and / or installed from a removable medium 511. When the computer program is executed by a central processing unit (CPU) 501, various functions defined in the system of the present application are executed.

[0091] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer executes the rule engine configuration method for early warning as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0092] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, system or device. A computer program contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0093] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0094] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0095] The above embodiments are merely illustrative of the principles and effects of the present application, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for monitoring the operation status of a rolling mill, characterized in that: The method comprises: Acquire various signal data of the rolling mill equipment at each monitoring point, wherein the signal data includes low-frequency signals and high-frequency signals, wherein the low-frequency signals are temperature signals, ferromagnetic wear particle concentration signals, pressure signals, displacement signals, current signals or voltage signals, and the high-frequency signals include speed signals and vibration signals; Detecting whether each of the low-frequency signals exceeds a preset threshold, and generating warning information based on the detection result, each of the low-frequency signals corresponding to the preset threshold; Calculating time domain features, frequency domain features and time-frequency domain features according to the high-frequency signal, diagnosing the time domain features, the frequency domain features and the time-frequency domain features, and generating alarm information based on the diagnosis results; The early warning information and the alarm information are output and displayed to monitor the operation status of the rolling mill.

2. The rolling mill operation status monitoring method according to claim 1, characterized in that: After the early warning information and the alarm information are output, the method further includes: Transmitting the warning information or the alarm information to a local server for display, and verifying the warning information or the alarm information once; If the result of the one-time verification is that the diagnosis is correct, the warning information or the alarm information is uploaded to the cloud server, and a one-to-many connection is established between the cloud server and the local server; The early warning information or the alarm information from the cloud server is displayed through the remote terminal, and the early warning information or the alarm information is double-checked.

3. The rolling mill operation status monitoring method according to claim 1 or 2, characterized in that: The detecting whether each of the low-frequency signals exceeds a preset threshold and generating warning information based on the detection result includes: If any of the low-frequency signals is a temperature signal, and the temperature signal is greater than the corresponding preset temperature threshold, a first-level warning message is generated; If only any one target low-frequency signal exceeds the corresponding preset threshold, a second-level warning information is generated, the target low-frequency signal is the low-frequency signal other than the temperature signal, and the severity of the second-level warning information is lower than that of the first-level warning information; If the temperature signal is less than or equal to the preset temperature threshold, and at least two of the target low-frequency signals exceed the corresponding preset thresholds, the first-level warning information is generated.

4. The rolling mill operation status monitoring method according to claim 1 or 2, characterized in that: The calculating the time domain feature, the frequency domain feature and the time-frequency domain feature according to the high-frequency signal, and diagnosing the time domain feature, the frequency domain feature and the time-frequency domain feature, comprises: Obtaining the fault characteristic frequency of the rolling mill equipment, and determining whether the rotation speed signal is stable; If the speed signal is stable, then calculating the vibration signal in the time domain and the frequency domain to determine the time domain feature, the frequency domain feature and the time-frequency domain feature; If the speed signal is not stable, resample the vibration signal, perform time domain and frequency domain calculations on the resampled vibration signal, and determine the time domain feature, the frequency domain feature, and the time-frequency domain feature; The fault characteristic frequency is combined to diagnose whether there is an abnormality in the time domain characteristic, the frequency domain characteristic and the time-frequency domain characteristic.

5. A rolling mill operation status monitoring system, characterized in that: The system includes: a data analysis platform, a local server, a cloud server and a remote terminal; A data analysis platform, used for obtaining various signal data of rolling mill equipment at various monitoring points, wherein the signal data include low-frequency signals and high-frequency signals, wherein the low-frequency signals are temperature signals, ferromagnetic wear particle concentration signals, pressure signals, displacement signals, current signals or voltage signals, and the high-frequency signals include speed signals and vibration signals; detecting whether each of the low-frequency signals exceeds a preset threshold, generating early warning information based on the detection result, wherein each of the low-frequency signals has a one-to-one correspondence with the preset threshold; calculating time domain features, frequency domain features and time-frequency domain features according to the high-frequency signals, and diagnosing the time domain features, the frequency domain features and the time-frequency domain features, and generating alarm information based on the diagnosis results; outputting the early warning information and the alarm information to the local server; The local server is connected to the data analysis platform to display the warning information or the alarm information and perform a verification on the warning information or the alarm information; if the result of the verification is that the diagnosis is correct, the warning information or the alarm information is uploaded to the cloud server, and a one-to-many connection is established between the cloud server and the local server; The cloud server establishes a one-to-many connection with the local server and the remote terminal, respectively, for storing the warning information or the warning information, and forwarding it to the remote terminal; The remote terminal is used to display the early warning information or the alarm information, and perform a secondary check on the early warning information or the alarm information to achieve remote monitoring of the operating status of the rolling mill.

6. The rolling mill operation status monitoring system according to claim 5, characterized in that: The data analysis platform includes a processor, a communication module and an analog-to-digital conversion module, wherein the processor is connected to the communication module and the analog-to-digital conversion module respectively, and the communication module is also connected to the local server; The communication module is externally connected to a sensor for collecting low-frequency signals arranged on the rolling mill equipment, and is used to collect the low-frequency signals and transmit them to the processor, and the location where the sensor is arranged is the monitoring point; The analog-to-digital conversion module is externally connected to the sensor for collecting high-frequency signals arranged on the rolling mill equipment, and is used to collect the high-frequency signals and transmit them to the processor after analog-to-digital conversion; The processor is used to perform diagnostic processing on the low-frequency signal and the high-frequency signal according to a preset diagnostic algorithm, generate the warning information and / or the warning information, and transmit them to the communication module for output.

7. The rolling mill operation status monitoring system according to claim 5 or 6, characterized in that: The local server is also used to construct a transmission structure framework of the rolling mill equipment, determine component parameters based on the transmission structure framework; calculate the fault characteristic frequency of the rolling mill equipment according to the component parameters, and send it to the processor, so that the processor can diagnose whether there is an abnormality in the time domain characteristics, the frequency domain characteristics and the time-frequency domain characteristics in combination with the fault characteristic frequency.

8. The rolling mill operation status monitoring system according to claim 5 or 6, characterized in that: The local server is also used to configure the channel parameters, communication parameters, acquisition parameters, measurement point parameters and diagnosis parameters of each signal data, and send them to the data analysis platform.

9. An electronic device, characterized in that: include: processor, memory, and communications bus; The communication bus is used to connect the processor and the memory; The processor is configured to execute the computer program stored in the memory to implement the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is used to make a computer execute the method according to any one of claims 1 to 4.

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