Real-time Monitoring and Control System for Workshop Operation Status

Abstract

The present invention discloses a real-time monitoring and control system for workshop operation status, which relates to the technical field of workshop supervision, and includes a data acquisition and processing module, an analysis and processing module, a monitoring and warning module, and a central control module; the data acquisition and processing module is used to obtain vibration data generated by mechanical equipment, process the acquired vibration data to generate a vibration data set, and interconnect the vibration data set with the analysis and processing module; the analysis and processing module is used to perform superposition noise reduction processing on the obtained vibration data set, calculate the result of the superposition noise reduction processing to obtain a position difference value and a position difference variance, calculate the modulus length through the position difference variance, calculate the abnormal score Abn through the acquisition of the modulus length, judge the workshop operation status and the equipment operation status, and send the workshop operation status and equipment operation status data to the monitoring and warning module.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop supervision, and particularly to a real-time monitoring and control system for workshop operation status. Background Art

[0002] With the development of Industry 4.0 and intelligent manufacturing, more and more enterprises begin to adopt automated and digital tools to achieve a higher level of real-time monitoring and control. For example, big data analysis, artificial intelligence technology, and cloud computing platforms are used to process massive amounts of data, so as to make more accurate predictions and decisions;

[0003] After retrieval, the patent with the Chinese patent number CN117826717A discloses a machining workshop equipment management system and method, which has the functions of collecting the production capacity information, status information, and task information of each equipment in the machining workshop, analyzing and statistically obtaining corresponding monitoring information based on the collected information and displaying it; providing functions of adding, querying, editing, and deleting task reporting based on the information collected by the data processing module and the monitoring information; allocating tasks to the equipment in the machining workshop based on the information collected by the data processing module and the monitoring information, and analyzing and statistically obtaining corresponding monitoring information based on the production capacity information, status information, and task information of each equipment in the machining workshop, so as to be able to monitor the production capacity situation, operation status, and task completion situation of the equipment in the machining workshop in real time, realize effective monitoring and management of the equipment, ensure the reliable, safe, and stable operation of the equipment, and furthermore, based on the collected information and monitoring information, realizing task reporting and planned operation, can make task reporting and task allocation conform to the real-time situation of the equipment, and be more flexible and reasonable; however, it lacks a corresponding equipment anomaly detection method, lacks effective monitoring management and early warning for equipment anomalies, and makes the equipment lack a corresponding emergency control plan during operation;

[0004] After retrieval, the patent with Chinese patent number CN113299042B discloses a safety warning system for industrial electrical frequency conversion equipment, which analyzes the working data of the frequency conversion equipment through a data analysis module to obtain the operating temperature value and operating vibration value of the frequency conversion equipment, divides the operating temperature values higher than the temperature threshold into the abnormal set, divides the operating vibration values higher than the vibration threshold into the abnormal set, analyzes the operating temperature values and operating vibration values in the abnormal set, calculates the average temperature change value and average vibration change value of the frequency conversion equipment, and substitutes the average temperature change value and average vibration change value into the formula to calculate the operating value when the frequency conversion equipment is working; however, it lacks the capture and superimposed noise reduction processing of vibration analysis interference at different points of the equipment. There is a certain degree of interference in the acquisition of vibration data, resulting in a relatively high probability of misjudging equipment failures, and it more focuses on the threshold judgment of basic indicators (such as temperature and vibration), making it difficult to sensitively capture the subtle changes of the equipment, especially for equipment failures that require early warning and preventive maintenance;

[0005] After retrieval, the patent with Chinese patent number CN108415381A discloses a distributed remote monitoring system and monitoring method for a machining workshop, which sets fault monitoring warning points, quality inspection warning points, and tool replacement warning points for each device through a device warning control module; the device warning control module monitors the fault monitoring warning points and prompts for fault repair when the device is about to reach the fault warning point; the device warning control module monitors the quality inspection warning points and controls the device not to work and prompts for quality inspection when the processing time of the device exceeds the set value or the cumulative completed quantity exceeds the set value; the device warning control module monitors the tool replacement warning points and prompts for tool replacement when the working time of the tool on the CNC lathe exceeds the set value or the cumulative completed quantity exceeds the set value; it mainly relies on preset thresholds and counters to trigger warnings, such as prompting for tool replacement or quality inspection when a specific working time or the number of completed parts is reached; this method is more rule-based reactive maintenance and lacks dynamic assessment of the equipment operating state.

[0006] At the same time, traditional methods may rely on manual records or simple sensors, which may lead to inaccurate or untimely updated data. Since the operating conditions of workshop mechanical equipment are judged manually and lack corresponding monitoring capabilities, when abnormal situations occur, decision-makers often have difficulty taking measures quickly. Maintaining a complex monitoring system requires professional technicians. Facing the rapidly changing market demands and technological progress, traditional monitoring systems may be difficult to quickly identify the location of abnormal equipment operation, and the response methods are difficult to quickly give opinions to solve the abnormal situations during the operation of mechanical equipment;

[0007] In view of the above technical defects, a solution is proposed now. Summary of the Invention

[0008] The object of the present invention is to provide a real-time monitoring and control system for workshop operation status to solve the above-mentioned technical defects. The present invention obtains vibration data of different monitoring points generated by workshop mechanical equipment, performs superimposed noise reduction processing on the vibration data of different monitoring points, calculates the average position values of different monitoring points, reduces the influence of noise, effectively smooths out random noise through the calculation of the average position values of the monitoring points, makes the signal clearer, performs noise reduction processing on the average position values again, converts the processed frequency-domain data into time-series data, calculates the anomaly score Abn through the position difference value, determines whether the anomaly score is abnormal, obtains data warning information, sends and presents the warning information, and controls and coordinates the workshop operation.

[0009] The object of the present invention can be achieved by the following technical solutions: a real-time monitoring and control system for workshop operation status, a data acquisition and processing module, an analysis and processing module, a monitoring and warning module, and a central control module;

[0010] The data acquisition and processing module is used to obtain the vibration data generated by the mechanical equipment, process the collected vibration data, generate a vibration data set, and output the vibration data set;

[0011] The analysis and processing module performs superimposed noise reduction processing on the vibration data set, calculates the results of the superimposed noise reduction processing, obtains the position difference value and the position difference variance, calculates the modulus length through the position difference variance, calculates the anomaly score Abn, determines the workshop operation status and the equipment operation status, and outputs the workshop operation status and the equipment operation status data;

[0012] The monitoring and warning module is used to obtain the workshop operation status and the equipment operation status data, and output the obtained warning information;

[0013] The central control module is used to obtain the data transmitted by the data acquisition and processing module, the analysis and processing module, and the monitoring and warning module, and control and coordinate the workshop operation.

[0014] Preferably, the vibration data acquisition process is as follows:

[0015] Through a triaxial acceleration sensor and a high-precision timer, at least obtain vibration data of three different monitoring points, measure the vibration data of the three different points. The vibration data includes the position values of the x-axis, y-axis, and z-axis and the time stamp t. Obtain the time stamp t through the built-in high-precision timer. The time stamp t is the time point of the current moment. Obtain the position values of the x-axis, y-axis, and z-axis through the triaxial acceleration sensor, obtain the vibration data of different monitoring points, denoted as h. The vibration data includes the origin coordinates and the relative positions.

[0016] Preferably, the process of obtaining the origin coordinates and relative positions in the vibration data is as follows:

[0017] Before the mechanical equipment runs, the position values of the x-axis, y-axis, and z-axis by the triaxial acceleration sensor are recorded as the origin coordinates. When the mechanical equipment runs and generates vibrations, the triaxial acceleration sensor obtains the vibration data within a continuous time stamp t, as well as the relative positions of the x-axis, y-axis, and z-axis with respect to the origin coordinates, and packs the vibration data within the continuous time stamp t into a vibration data set.

[0018] Preferably, the process of performing superposition noise reduction processing on the vibration data set is as follows:

[0019] By obtaining the vibration data sets at different monitoring points at the time stamp t, performing superposition noise reduction processing, aligning the vibration data sets at different monitoring points in space, and calculating the average position values at different monitoring points through superposition of different monitoring points .

[0020] Preferably, after obtaining the average position values at different monitoring points, the process of performing noise reduction processing is as follows:

[0021] By obtaining the average position values at different monitoring points under the actual operating state of the mechanical equipment obtaining, and organizing the average position values under the actual operating state within the continuous time stamp t into an average position vibration data set, and respectively establishing a vibration-time data set under the actual state;

[0022] At the same time, obtaining the position values under the ideal operating state of the mechanical equipment , and within the continuous time stamp t 0 organizing the position values under the ideal operating state into a vibration data set, and respectively establishing a vibration-time data set under the ideal state;

[0023] Comparing the vibration-time data set under the ideal state of the mechanical equipment with the vibration-time data set under the actual state, distinguishing the frequency ranges of the signals and noises, removing the noise part, and converting the processed frequency-domain data into time-series data.

[0024] Preferably, the process of obtaining the superposition noise reduction processing result, calculating the position difference value and the variance of the position difference is as follows:

[0025] By obtaining the difference between the average position vibration data after the superposition noise reduction processing result and the ideal position vibration data, calculating the position difference value, and the formula for calculating the position difference value is:

[0026] ;

[0027] wherein, is the position difference value; and it is obtained by calculating the position difference value, and the variance of the position difference is calculated ;

[0028] Preferably, the process of calculating the modulus length is as follows:

[0029] Obtain the data modulus lengths in the position difference variance respectively and the data modulus lengths in the position values under the ideal operating state , and the formula is:

[0030] ;

[0031]

[0032] In the formula, is the data modulus length of x, y, and z in the position difference variance, is the data modulus length of x, y, and z in the position values under the ideal operating state

[0033] Preferably, the process of calculating the abnormal score Abn and judging the workshop operation status and equipment operation status is as follows:

[0034] By obtained, calculate the abnormal score at different timestamps t, and by obtaining which is the data modulus length of x, y, and z in the variance, and the data modulus length of x, y, and z in the position values under the ideal operating state, calculate the abnormal score Abn, and the formula is:

[0035]

[0036] Among them, is the data modulus length of x, y, and z in the position difference variance, is the data modulus length of x, y, and z in the position values under the ideal operating state. When the actual vibration data set coincides with the ideal vibration data set, the abnormal score is 1, that is, the abnormal score of 1 is used as the judgment threshold to judge whether the abnormal score is abnormal:

[0037] When the abnormal score is less than or equal to 1, it means that the data point is a normal value;

[0038] When the abnormal score is greater than 1, it means that the data point is an abnormal value and a warning message is generated

[0039] Preferably, the process of sending and presenting the warning information for data is as follows:

[0040] By obtaining the warning information of the analysis and processing module, self-check the anomalies of mechanical equipment, obtain the position difference values at the continuous timestamps t where the anomaly score Abn is located, as well as the database of the mechanical equipment anomaly position difference values, compare the position difference values with the data in the database of the anomaly position difference values, automatically generate warning information, and send it to the central control module.

[0041] Preferably, the process of controlling and coordinating the workshop operations is as follows;

[0042] Obtain the monitoring and warning information. When receiving the warning information, adjust the data acquisition interval of the data acquisition and processing module and the analysis and processing frequency of the vibration data, and coordinate and manage the communication and data exchange between each sub-module. Based on the collected data, coordinate and control the data acquisition system, the data analysis platform, and the user interface.

[0043] The beneficial effects of the present invention are as follows:

[0044] (1) By obtaining the vibration data of different monitoring points generated by the workshop mechanical equipment, performing superposition noise reduction processing on the vibration data of different monitoring points, calculating the average position values of different monitoring points, reducing the influence of noise, effectively smoothing out the random noise through the calculation of the average position values of the monitoring points, making the signal clearer, and performing noise reduction processing on the average position values again, so that there are fewer interference waveforms in the noise-reduced vibration data, which is beneficial to the judgment of mechanical equipment anomalies.

[0045] (2) By converting the processed frequency-domain data into time-series data, calculating the anomaly score Abn through the position difference values, thereby judging whether the anomaly score is an anomaly, obtaining the vibration data of different monitoring points generated by the workshop mechanical equipment, obtaining the operating state of the equipment, and comparing it with the vibration data registered under the ideal state to obtain the operating conditions of the mechanical equipment and making a warning in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings;

[0047] Figure 1 is the system flow block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] Example: Please refer to Figure 1 As shown, this example provides a real-time monitoring and control system for the workshop operation status, including the following modules:

[0050] The data acquisition and processing module is used to obtain the vibration data generated by mechanical equipment. The vibration data is the mechanical signal generated by the workshop mechanical equipment when it operates in a three-dimensional space. The acquired vibration data is processed, and the processed data generates a vibration data set, which is interconnected with the analysis and processing module;

[0051] Among them, for the vibration data, the vibration data at different monitoring points are obtained respectively, denoted as h, and there are at least three different monitoring points of vibration data;

[0052] The vibration data at three different points are measured by a triaxial acceleration sensor and a high-precision timer. The vibration data includes the position values of the x-axis, y-axis, and z-axis and the time stamp t. The time stamp t is obtained through the built-in high-precision timer, and the time stamp t is the time point at the current moment. The position values of the x-axis, y-axis, and z-axis are obtained through the triaxial acceleration sensor;

[0053] The triaxial acceleration sensor is fixedly installed inside the workshop mechanical equipment, and the acceleration changes in three mutually perpendicular directions during the vibration of the mechanical equipment are measured;

[0054] The high-precision timer is set inside the triaxial acceleration sensor and is interconnected with the triaxial acceleration sensor. The piezoelectric effect of the quartz crystal is used to generate a stable oscillation frequency to monitor the frequency and time data;

[0055] Before the mechanical equipment runs, the triaxial acceleration sensor records the position values of the x-axis, y-axis, and z-axis as the origin coordinates. When the mechanical equipment runs and generates vibrations, the triaxial acceleration sensor obtains the vibration data within the continuous time stamp t, as well as the relative positions of the x-axis, y-axis, and z-axis with respect to the origin coordinates. Among them, the acquisition frequency of the position values of the triaxial acceleration sensor is 30Hz, and the vibration data within the continuous time stamp t is packed into a vibration data set;

[0056] For example:

[0057] In the large-scale rice hulling machine equipment in the workshop, by determining at least three key monitoring points, including: the base of the main drive motor, the bracket of the rice hulling roller (or sand disc), and the belt or chain tensioning device, the triaxial acceleration sensor is installed. By firmly installing the triaxial acceleration sensor at the selected positions, it is ensured that they can accurately sense the vibration conditions in this area;

[0058] The base of the main drive motor: It is the connection point between the motor and the equipment, and usually has significant vibrations;

[0059] Bracket of the rice hulling roller (or abrasive disc): It is the core working component of the equipment, and its vibration condition can reflect the working state of the equipment;

[0060] Belt or chain tensioning device: It is the key part of power transmission, and its vibration condition can reflect the health status of the transmission system;

[0061] By means of the equipped high-precision timer, the acceleration sensor is set to sample at a frequency of 30 Hz, synchronize the data of each sensor, and provide accurate timestamps;

[0062] Before the rice hulling machine starts, record the initial acceleration values of each sensor in the three axial directions of X, Y, and Z as the origin coordinates. Each sampling will generate a set of data points including the timestamp t and the acceleration values of the X, Y, and Z axes; Save the vibration data set in file formats such as CSV, JSON, and XML that are easy to read and process, which is convenient for subsequent data analysis software to load, and use compression algorithms to reduce storage space occupancy and speed up data transmission speed;

[0063] Analysis and processing module, which is used to perform superposition noise reduction processing on the obtained vibration data set, calculate the superposition noise reduction processing result, obtain the position difference value, associate the position difference value with the timestamp t, analyze the change of the position difference value within the continuous timestamp t, monitor the change of the position difference value in real time, judge the workshop operation status and equipment operation status, and send the workshop operation status and equipment operation status data to the monitoring and early warning module;

[0064] By obtaining the vibration data sets at different monitoring points at the timestamp t, perform superposition noise reduction processing, use image registration technology to align the vibration data sets at different monitoring points in space, calculate the average position values of different monitoring points by superimposing different monitoring points, reduce the influence of noise, and the formula is:

[0065] ;

[0066] Among them, is the average position value after superimposing the vibration data at different monitoring points. H is the total number of monitoring points, h is the number of the current monitoring point, h = 1, 2…, H, is the position value at the monitoring point h. By calculating the average position value of the monitoring point, the random noise is effectively smoothed, making the signal clearer, which is beneficial to subsequent data analysis and reduces the range of the vibration data at different monitoring points at the same timestamp t;

[0067] After obtaining the average position values of different monitoring points, perform noise reduction processing on the average position values of different monitoring points;

[0068] By obtaining the average position values at different monitoring points under the actual operating state of the mechanical equipment and organizing the average position values under the actual operating state within consecutive time stamps t into an average position vibration data set, respectively establishing vibration-time data sets under the actual state, that is, the relationships between the position values on the x-axis, y-axis, and z-axis and the time stamp t: the x-t relationship, the y-t relationship, and the z-t relationship; at the same time, obtaining the position values of the mechanical equipment under the ideal operating state and within consecutive time stamps t 0 organizing the position values under the ideal operating state into a vibration data set, respectively establishing vibration-time data sets under the ideal state, that is, the x 0 axis, y 0 axis, z 0 axis of the position values and the time stamp t 0 relationship: the x 0 -t 0 relationship, the y 0 -t 0 relationship, and the z 0 -t 0 relationship;

[0069] Compare the vibration-time data sets under the ideal state and the actual state of the mechanical equipment, perform discrete Fourier transforms respectively, use the numpy.fft.fft function to perform the Fourier transform, convert the data in the time domain into the frequency domain, view the spectrum to determine the location of the main components of the signal. Usually, noise will appear in the higher frequency part. Compare with the ideal vibration data set to distinguish the frequency ranges of the signal and the noise, remove the noise part, and convert the processed frequency domain data into time series data;

[0070] Discrete Fourier transform: A linear transformation method that converts a time series signal of finite length into its spectral representation, revealing the distribution of the signal over different frequency components to analyze the characteristics of the signal;

[0071] numpy.fft.fft function: A function in the NumPy library used to perform one-dimensional discrete Fourier transform (DFT). It accepts a sequence of complex or real numbers as input and returns the DFT result of the sequence. The result of the DFT is also a sequence of complex numbers, containing the complex values of the input signal over different frequency components. The modulus of the complex value represents the amplitude of the corresponding frequency component, and the phase represents the phase angle of these components;

[0072] Code example:

[0073] Obtain a set of ideal vibration-time data sets and actual vibration-time data sets during the operation of the rice huller, read the data file, and take Python language as an example to perform calculations using the NumPy library:

[0074] ```python

[0075] import pandas as pd

[0076] # Load data in the ideal state

[0077] ideal_data = pd.read_csv('ideal_vibration.csv')

[0078] # Load data in the actual state

[0079] actual_data = pd.read_csv('actual_vibration.csv')

[0080] # Perform Fourier transform on the ideal data

[0081] ideal_fft = np.fft.fft(ideal_data['vibration'])

[0082] # Perform Fourier transform on the actual data

[0083] actual_fft = np.fft.fft(actual_data['vibration'])

[0084] Python

[0085] # Calculate the power spectral density of the ideal data

[0086] ideal_psd = np.abs(ideal_fft)**2

[0087] # Calculate the power spectral density of the actual data

[0088] actual_psd = np.abs(actual_fft)**2

[0089] ```python

[0090] import matplotlib.pyplot as plt

[0091] # Create an array of frequencies

[0092] freqs = np.fft.fftfreq(len(ideal_data))

[0093] plt.figure(figsize=(10, 5))

[0094] plt.plot(freqs[:len(freqs) / / 2], ideal_psd[:len(freqs) / / 2])

[0095] plt.title('Ideal State Power Spectral Density')

[0096] plt.xlabel('Frequency [Hz]')

[0097] plt.ylabel('Power spectral density')

[0098] plt.show()

[0099] plt.figure(figsize=(10, 5))

[0100] plt.plot(freqs[:len(freqs) / / 2], actual_psd[:len(freqs) / / 2])

[0101] plt.title('Actual State Power Spectral Density')

[0102] plt.xlabel('Frequency [Hz]')

[0103] plt.ylabel('Power spectral density')

[0104] plt.show()

[0105] ```

[0106] Use the `fft.fft()` function of NumPy to perform Fourier transforms on two sets of data. It should be noted that at this time, the results of the Fourier transforms contain complex numbers, and the real and imaginary parts correspond to sine and cosine components;

[0107] By observing the spectrogram, use image registration technology to spatially align the vibration datasets at different monitoring points. By superimposing different monitoring points, calculate the average position values of different monitoring points to reduce the influence of noise;

[0108] Then use the inverse Fourier transform (`np.fft.ifft()`) to convert the processed frequency-domain data back to the time domain;

[0109] By obtaining the average position vibration data after processing the superimposed noise reduction result and calculating the difference from the ideal position vibration data, the position difference value is calculated. The position difference value includes: the vibration generated by the mechanical equipment due to external interference and the resonance generated by external noise. The formula for obtaining the position difference value is:

[0110] ;

[0111] where is the position difference value;

[0112] By obtaining the position difference value, the position difference variance is calculated. The formula is:

[0113] ;

[0114] where is the variance, which reflects the fluctuation of the position difference value. t is the timestamp, H - 1 is the sample calculated from the previous timestamp t, and T is the maximum subscript value of {t 1 , t 2 , t 3 ...t T} within the continuous timestamp t. t = 1, 2, 3...T, is the square of the difference value at each timestamp t minus the average position vibration data, which eliminates the influence of positive and negative values, magnifies the degree of difference, and obtains ;

[0115] Example:

[0116] Regularly collect the vibration data of a large rice huller during operation and compare it with the predefined ideal vibration data. By calculating the difference between the actual data and the ideal data and its statistical characteristics, an anomaly score is obtained. When the score exceeds the preset threshold, it indicates that the equipment may have an anomaly, which may be caused by wear, imbalance, looseness, bearing failure, or other mechanical problems. At this time, the system should automatically alarm to prompt the maintenance personnel to conduct inspections and repairs;

[0117] Wear: As the wear of components such as bearings and gears intensifies, more high - frequency components will appear in the vibration signal. At the same time, wear will lead to increased friction, thereby causing an increase in the vibration intensity;

[0118] Imbalance: It causes a significant vibration every time the rotating component makes one revolution. This vibration is periodic and its frequency is proportional to the rotational speed; the vibration amplitude caused usually increases with the increase in rotational speed;

[0119] Looseness: Looseness can cause broadband vibration in the entire structure. This vibration does not have obvious frequency characteristics but contains a large number of frequency components. The vibration caused by looseness is usually random and has no fixed periodicity;

[0120] Bearing fault: It usually generates specific frequency components in the vibration signal, such as the ball pass frequency outer race (BPFO), the inner race fault frequency (BPFI), etc. Bearing faults are often accompanied by impulsive vibration, manifested as spikes in the vibration signal;

[0121] By acquiring and calculating the anomaly scores at different timestamps t. By obtaining the data modulus lengths of x, y, and z in the position difference variance and the data modulus lengths of x, y, and z in the position values under the ideal operating state, calculate the anomaly score Abn. The formula is:

[0122] ;

[0123] Among them, is the data modulus length of x, y, and z in the position difference variance, is the data modulus length of x, y, and z in the position values under the ideal operating state. When the actual vibration data set coincides with the ideal vibration data set, the anomaly score is 1. That is, the anomaly score of 1 is used as the judgment threshold to determine whether the anomaly score is an anomaly:

[0124] When the anomaly score is less than or equal to 1, it indicates that the data point is a normal value;

[0125] When the anomaly score is greater than 1, it indicates that the data point is an abnormal value and a warning message is generated;

[0126] and The modulus lengths are obtained and represented by the Euclidean norm. The formula is:

[0127] ;

[0128] ;

[0129] The monitoring and warning module is used to acquire the warning information, send and present the warning information, and send the warning information to the central control module;

[0130] By obtaining the early warning information of the analysis and processing module, self-check the anomalies of mechanical equipment. Once receiving the early warning information of anomalies sent by the analysis and processing module, obtain the position difference values at the continuous timestamps t where the anomaly score Abn is located, and the database of the mechanical equipment anomaly position difference values. Compare the position difference values with the data in the database of the anomaly position difference values. Convert the vibration signal into the frequency domain, then compare the characteristic frequencies, amplitudes, etc., and directly analyze on the time series, compare the statistical features such as peak values, root mean square values, kurtosis coefficients, etc., and calculate the energy distribution of the signal, especially compare the energy proportion in different frequency bands;

[0131] When the position difference values match the data in the database of the mechanical equipment anomaly position difference values, obtain the names of the mechanical equipment anomalies in the database, and match the solution methods and decision-making deployments of the mechanical equipment in the database, and implement the response decisions in the database. At the same time, the system will automatically generate early warning information, which will be sent to relevant control systems or personnel, including various forms such as email notifications, text messages, and mobile application push notifications, to ensure that relevant personnel can receive the alarms in a timely manner. In addition to sending early warning information, the monitoring and early warning module clearly displays the status of the equipment and early warning information through the user interface, and records the early warning events, formulates a data backup plan, and regularly backs up the early warning information to external storage media or remote servers for later analysis and report generation;

[0132] The central control module is used to obtain the data transmitted by the data acquisition and processing module, the analysis and processing module, and the monitoring and early warning module, and control and coordinate the workshop operations;

[0133] The central control module can provide decision support functions. By obtaining the early warning information, before receiving the early warning information sent by the early warning module, it monitors the data information of each module in real time and monitors the abnormal data in the workshop in real time. After receiving the early warning information sent by the early warning module, it reduces the data acquisition interval of the data acquisition and processing module and increases the analysis and processing frequency of the vibration data. By obtaining the collected data, it collects the vibration data in real time, and adds the data with continuous timestamps t to increase the collection of the mechanical equipment vibration data;

[0134] Usually, a three-axis acceleration sensor and a high-precision timer stop collecting vibration data after collecting data with consecutive timestamps t, and continue to collect vibration data after a period of time E. When a warning message is obtained, the interval of E is shortened and the number of vibration data collected is increased, so that sufficient vibration data can be obtained in a smaller time interval. By monitoring the sufficient vibration data in real time, it is judged whether there is an abnormal situation in the mechanical equipment. Through negative feedback adjustment of the analysis and processing module, the data collection module is regulated, and the communication and data exchange between each module are coordinated and managed to ensure the efficient operation of the entire system. The system receives information from various data sources, and coordinates the data collection system, the data analysis platform, and the user interface based on the collected data to ensure the smooth operation of the entire system.

[0135] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. The real-time monitoring and control system for workshop operation status is characterized by: It includes data acquisition and processing module, analysis and processing module, monitoring and early warning module and central control module; A data acquisition and processing module is used to acquire vibration data generated by mechanical equipment, process the acquired vibration data, generate a vibration data set, and output the vibration data set; The analysis and processing module performs superposition noise reduction processing on the vibration data set, and calculates the superposition noise reduction processing results to obtain the position difference value and the position difference variance, and calculates the modulus through the position difference variance, calculates the abnormal score Abn, determines the workshop operation status and equipment operation status, and outputs the workshop operation status and equipment operation status data; The monitoring and early warning module is used to obtain data on workshop operation status and equipment operation status, and output the obtained early warning information; The central control module is used to obtain data transmitted by the data acquisition and processing module, the analysis and processing module, and the monitoring and early warning module, and to control and coordinate the workshop operations; The process of superposition denoising of vibration data set is as follows: By obtaining the vibration data sets of different monitoring points at timestamp t, superposition and noise reduction processing are performed, the vibration data sets of different monitoring points are spatially aligned, and the average position values ​​of different monitoring points are calculated by superposition of different monitoring points. ; After obtaining the average position values ​​of different monitoring points, the noise reduction process is as follows: By obtaining the average position values ​​of different monitoring points under the actual operating state of the mechanical equipment The average position values ​​under the actual operating state within the continuous time stamp t are obtained and sorted into average position vibration data sets, and vibration-time data sets under the actual state are established respectively; At the same time, obtain the position value of the mechanical equipment under the ideal operating state , and organize the position values ​​under the ideal operating state within the continuous time stamp t0 into a vibration data set, and establish the vibration-time data set under the ideal state respectively; The vibration-time data set of the mechanical equipment under ideal conditions is compared with the vibration-time data set under actual conditions, the frequency range of the signal and noise is distinguished, the noise part is removed, and the processed frequency domain data is converted into time series data.

2. The real-time monitoring and control system for workshop operation status according to claim 1 is characterized in that: The vibration data collection process is as follows: Vibration data of at least three different monitoring points are obtained through a three-axis acceleration sensor and a high-precision timer. The vibration data includes the position values ​​and timestamp t of the x-axis, y-axis, and z-axis. The timestamp t is obtained through the built-in high-precision timer. The timestamp t is the time point at the moment. The position values ​​of the x-axis, y-axis, and z-axis are obtained through the three-axis acceleration sensor. The vibration data of different monitoring points are obtained, denoted as h. The vibration data includes the origin coordinates and the relative position.

3. The real-time monitoring and control system for workshop operation status according to claim 2 is characterized in that: The process of obtaining the origin coordinates and relative position in the vibration data is as follows: Before the mechanical equipment is operated, the three-axis accelerometer records the position values ​​of the x-axis, y-axis and z-axis as the origin coordinates. When the mechanical equipment generates vibration during operation, the three-axis accelerometer obtains the vibration data within the continuous timestamp t, as well as the relative positions of the x-axis, y-axis and z-axis relative to the origin coordinates, and packages the vibration data within the continuous timestamp t into a vibration data set.

4. The real-time monitoring and control system for workshop operation status according to claim 1 is characterized in that: The process of obtaining the superposition noise reduction processing results and calculating the position difference value and the position difference variance is as follows: The position difference value is calculated by obtaining the average position vibration data after superimposing the noise reduction processing results and the difference between the ideal position vibration data. The position difference value calculation formula is: in, is the position difference value; and the position difference variance is calculated by calculating the position difference value .

5. The real-time monitoring and control system for workshop operation status according to claim 4 is characterized in that: The process of calculating the modulus length is as follows: Get the data modulus length in the position difference variance respectively and the data modulus length of the position value under the ideal operating state , the formula is: In the formula, is the data modulus length in x, y and z in the position difference variance, It is the data modulus length of x, y and z in the position value under the ideal operating condition.

6. The real-time monitoring and control system for workshop operation status according to claim 1 is characterized in that: The process of calculating the abnormal score Abn and judging the workshop operation status and equipment operation status is as follows: Through Obtain and calculate the outlier scores for different timestamps t by obtaining The anomaly score Abn is calculated by the mode length of the data in x, y and z in the variance and the mode length of the data in x, y and z in the position value under the ideal operating state. The formula is: in, is the data modulus length in x, y and z in the position difference variance, is the data modulus length of x, y and z in the position value under the ideal operating state. When the actual vibration data set coincides with the ideal vibration data set, the anomaly score is 1, that is, the anomaly score of 1 is used as the judgment threshold to judge whether the anomaly score is abnormal: When the anomaly score is less than or equal to 1, it means that the data point is a normal value; When the anomaly score is greater than 1, it indicates that the data point is an outlier and a warning message is generated.

7. The real-time monitoring and control system for workshop operation status according to claim 1 is characterized in that: The process of sending and presenting early warning information is as follows: By obtaining the warning information from the analysis and processing module, the mechanical equipment is self-checked for abnormalities, the position difference value at the continuous timestamp t where the abnormal score Abn is located, and the database of abnormal position difference values ​​of the mechanical equipment are obtained, the position difference value is compared with the data of the abnormal position difference value in the database, and the warning information is automatically generated and sent to the central control module.

8. The real-time monitoring and control system for workshop operation status according to claim 1 is characterized in that: The process of control and coordination of workshop operations is as follows; Obtain monitoring and early warning information. When receiving the early warning information, adjust the data collection interval of the data acquisition and processing module and the analysis and processing frequency of the vibration data, coordinate and manage the communication and data exchange between the sub-modules, and coordinate and control the data acquisition system, data analysis platform and user interface based on the collected data.

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