Rapid splicing method for cryogenic fluid loading and unloading arm

Abstract

The invention relates to the technical field of intelligent monitoring, and particularly discloses a quick splicing method for a cryogenic fluid loading and unloading arm, which comprises the following steps of: firstly, acquiring a vibration signal in the operation process of equipment, and extracting key time domain characteristic parameters including but not limited to a vibration peak value, an average value, a standard deviation, kurtosis and the like from the vibration signal; the current working state of equipment is evaluated according to the change trend of the parameters, for abnormal signals exceeding a normal range, the system can automatically trigger an early warning mechanism, spectral analysis is further carried out to determine the specific position and property of a fault, in addition, the invention further provides a fault positioning algorithm based on characteristic frequency recognition, and the fault positioning algorithm is applied to the field of fault positioning. According to the technical scheme, the root cause causing the performance reduction of the equipment can be recognized more accurately, the operation safety and stability of the loading and unloading arm equipment are greatly improved, meanwhile, the production interruption risk caused by sudden failures is reduced, and remarkable economic benefits and social values are achieved.

Description

Technical Field

[0001] The invention relates to the field of intelligent monitoring technology, and in particular to a rapid splicing method for a cryogenic fluid loading and unloading arm. Background Art

[0002] Cryogenic fluid loading and unloading arms undertake important material transportation tasks in the industrial production process. Their safety and reliability directly affect the stable operation of the entire production system. With the continuous improvement of the level of industrial automation, how to effectively monitor the loading and unloading arms in real time and provide fault warnings has become a technical problem that needs to be solved urgently. Traditional monitoring methods often rely on regular manual inspections, which are not only inefficient but also easy to miss potential safety hazards. In recent years, with the development of sensor technology and data analysis methods, intelligent monitoring technology based on vibration signals has gradually become a research hotspot.

[0003] At present, the common low-temperature fluid loading and unloading arm monitoring systems on the market mainly adopt the following technical solutions: First, direct monitoring based on vibration signals, through vibration sensors installed at key parts of the loading and unloading arm to collect vibration data in real time, and then use simple threshold judgment to identify whether the equipment is in an abnormal state. The advantage of this solution is that it is simple to implement, but the disadvantage is that the false alarm rate is high and it is difficult to accurately identify the specific fault type; the second is predictive maintenance based on machine learning, combining historical data to train models, and using models to predict possible future faults. Although it can improve the accuracy of early warning, it has high requirements on the quality and quantity of data, and the model training cycle is long; the third is spectrum analysis, which uses fast Fourier transform (FFT) to convert time domain signals into frequency domain signals, and diagnoses faults by analyzing spectrum characteristics. This method can effectively identify abnormal conditions at specific frequencies, but under complex working conditions, the spectrum characteristics may not be obvious enough, resulting in diagnosis difficulties.

[0004] In view of the shortcomings of the prior art, the present invention proposes a novel rapid splicing method for cryogenic fluid loading and unloading arms, which not only combines the advantages of time domain analysis and frequency domain analysis of vibration signals, but also introduces a comprehensive scoring mechanism and a multi-level early warning system, which can accurately locate the source of the fault at the first time and issue a warning signal in time; compared with traditional methods, the present invention has the characteristics of high precision, and can more comprehensively reflect the operating status of the equipment by comprehensively evaluating various time domain characteristics of vibration signals; at the same time, it has the advantage of fast response speed, and adopts fast Fourier transform technology, which can complete the whole process from signal acquisition to fault diagnosis in a short time; in addition, the present invention has strong applicability, is suitable for cryogenic fluid loading and unloading arms of various types and working conditions, and has wide practical value. Summary of the invention

[0005] The purpose of the present invention is to provide a rapid splicing method for a cryogenic fluid loading and unloading arm. The technical scheme of the present invention is as follows: first, the monitoring period is divided into a plurality of time subunits of equal length, and each time subunit is further subdivided into time small units of equal length. At the middle moment of each time small unit, the vibration signal value of the equipment is collected, and then all the collected vibration signal values ​​are integrated to extract the time domain characteristics of the vibration signal; then, based on the extracted time domain characteristics, the vibration score of the equipment is calculated, and the equipment status signal is generated accordingly. For abnormal signals, their time domain characteristics are further analyzed to generate early warning signals; then, all vibration signals are converted into frequency domain signals, a spectrum diagram is constructed, and the characteristic frequencies and their corresponding abnormal peaks in the spectrum diagram are identified; finally, according to the unique spectrum characteristics corresponding to different types of mechanical failures, the location of the failure is accurately determined through matching analysis, and a corresponding warning signal is issued.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A rapid splicing method for a cryogenic fluid loading and unloading arm comprises the following steps:

[0008] Collect the vibration signal value of the loading and unloading arm equipment to obtain the time domain characteristics of the vibration signal;

[0009] Among them, the time domain characteristics of the vibration signal include vibration peak value, average value, standard deviation and kurtosis;

[0010] Process the time domain characteristics of the vibration signal to obtain the vibration score of the equipment and generate the equipment status signal;

[0011] Among them, the device status signal includes a device abnormality signal and a device normal signal;

[0012] Based on the abnormal signal of the equipment, the time domain characteristics of the vibration signal are analyzed separately to generate corresponding warning signals;

[0013] Based on the early warning signal, all vibration signals are integrated to construct a spectrum diagram;

[0014] The characteristic frequency is identified on the spectrum graph to obtain abnormal peaks with abnormally high amplitudes;

[0015] According to the unique spectrum characteristics corresponding to different types of mechanical failures, the location of the failure is obtained through matching analysis, and the corresponding warning signal is issued at the same time.

[0016] As a further solution of the present invention: the process of obtaining the time domain characteristics of the vibration signal is:

[0017] Divide the monitoring period into several time sub-units of equal length, divide the time sub-unit into several time small units of equal length, and collect the vibration signal value of the equipment when the time small unit is in the middle moment;

[0018] All vibration signal values ​​in the time sub-unit are integrated into a vibration sequence, and the vibration signal value with the largest absolute value in the vibration sequence is extracted and recorded as the unit vibration peak value;

[0019] The vibration signal in the time subunit is processed according to the mean value calculation formula to obtain the average data of the vibration signal in the time subunit;

[0020] The vibration signal in the time subunit is processed according to the standard deviation calculation formula to obtain the standard deviation of the vibration signal in the time subunit;

[0021] Based on the standard deviation of the vibration signal in the time subunit, the kurtosis EK of the vibration signal in the time subunit is calculated.

[0022] As a further solution of the present invention: the process of processing the time domain characteristics of the vibration signal is:

[0023] Process all unit vibration peaks within the monitoring period to obtain the maximum vibration peak and the minimum vibration peak;

[0024] The difference between the unit vibration peak value and the minimum vibration peak value is calculated as a ratio to the difference between the maximum vibration peak value and the minimum vibration peak value, so as to obtain the vibration peak value ratio of the vibration signal in the time subunit;

[0025] As above, the mean ratio of the vibration signal in the time subunit, the standard deviation ratio of the vibration signal in the time subunit, and the kurtosis ratio of the vibration signal in the time subunit are obtained.

[0026] As a further solution of the present invention: based on the vibration peak value ratio, mean value ratio, standard deviation ratio and kurtosis ratio of the vibration signal in the time subunit, a vibration score of the equipment is calculated;

[0027] As a further solution of the present invention: based on the vibration score of the device, comparing the vibration score of the device with a vibration score threshold of the device;

[0028] If the vibration score of the device is greater than or equal to the vibration score threshold of the device, a device abnormality signal is generated;

[0029] If the vibration score of the device is less than the vibration score threshold of the device, a device normal signal is generated.

[0030] As a further solution of the present invention: based on the abnormal signal of the equipment, all vibration peak values ​​within the monitoring period are integrated to obtain a vibration peak value group;

[0031] The vibration peak value group is processed according to the mean value calculation formula to obtain the vibration peak value mean value;

[0032] The vibration peak value group is processed according to the standard deviation calculation formula to obtain the standard deviation of the vibration peak value;

[0033] The difference between the vibration peak value and the vibration peak mean value of the vibration signal in the time subunit is calculated by ratio with the vibration peak standard deviation to obtain the statistical value of the vibration peak value;

[0034] Similarly, the statistical values ​​of the average data, the standard deviation, and the kurtosis are obtained.

[0035] As a further solution of the present invention: comparing the absolute value of the statistical value of the vibration peak value of the vibration signal in the time subunit with the statistical value threshold of the vibration peak value;

[0036] If the absolute value of the statistical value is greater than or equal to the statistical value threshold of the vibration peak value, an early warning signal of equipment bearing damage and gear meshing is generated;

[0037] Compare the statistical value of the average data of the vibration signal in the time subunit with the statistical value threshold of the average data;

[0038] If the statistical value of the average data is greater than or equal to the statistical value threshold of the average data, an equipment imbalance and misalignment warning signal is generated;

[0039] comparing a statistical value of a standard deviation of the vibration signal within a time subunit with a statistical value threshold of the standard deviation;

[0040] If the statistical value of the standard deviation is greater than or equal to the statistical value threshold of the standard deviation, an internal wear warning signal of the equipment is generated;

[0041] Comparing the statistical value of the kurtosis of the vibration signal in the time subunit with a statistical value threshold of the kurtosis;

[0042] If the statistical value of the kurtosis is greater than or equal to the statistical value threshold of the kurtosis, a local equipment failure warning signal is generated.

[0043] As a further solution of the present invention, the process of identifying characteristic frequencies of the spectrum graph and obtaining abnormal peaks with abnormally high amplitudes is as follows:

[0044] Based on the spectrum graph, the motor speed of the device is obtained to obtain the fundamental frequency of the device. By traversing the spectrum graph, the frequency peak in the spectrum graph that is the same as the fundamental frequency is obtained and recorded as the fundamental frequency peak.

[0045] Identify the frequency peaks in the spectrum graph that are integer multiples of the fundamental frequency and record them as the harmonic frequencies of the device.

[0046] As a further solution of the present invention: based on the equipment imbalance and misalignment warning signal, the fundamental frequency peak value is compared with the fundamental frequency peak value threshold value, and if the fundamental frequency peak value is greater than or equal to the fundamental frequency peak value threshold value, an equipment imbalance alarm signal is generated;

[0047] The peak values ​​at the base frequency and twice the base frequency are compared with the base frequency peak value threshold at the same time. If the peak values ​​at the base frequency and twice the base frequency are both greater than or equal to the base frequency peak value threshold, an equipment misalignment alarm signal is generated.

[0048] Based on the equipment bearing damage and gear meshing warning signals, the number and diameter of the rolling elements in the equipment bearing and the diameter of the raceway in the bearing are obtained to obtain the bearing characteristic frequency;

[0049] Based on the characteristic frequency of the bearing, if an abnormal peak corresponding to the characteristic frequency of the bearing appears in the spectrum graph, a bearing damage alarm signal is generated;

[0050] Obtain the number of teeth and speed of the driving gear in the equipment gearbox to obtain the gear meshing frequency;

[0051] Based on the gear meshing frequency, if an abnormal peak corresponding to the gear meshing frequency appears in the spectrum graph, a gear damage alarm signal is generated;

[0052] Based on the internal wear warning signal of the equipment, the gear rotation frequency is obtained;

[0053] If an abnormal peak corresponding to the gear rotation frequency appears in the spectrum graph, a gear wear alarm signal is generated;

[0054] Based on the number and diameter of the rolling elements in the equipment bearing and the diameter of the raceway in the bearing, the cage rotation frequency is obtained;

[0055] If an abnormal peak corresponding to the cage rotation frequency appears in the spectrum graph, a bearing wear alarm signal is generated;

[0056] Based on the equipment local fault warning signal, the equipment power frequency is obtained. If an abnormal peak corresponding to the equipment power frequency appears in the spectrum graph, a motor fault alarm signal is generated;

[0057] Based on the fact that the device is a hydraulic or pneumatic system, the operating frequency of the pump or compressor is obtained, and if an abnormal peak corresponding to the operating frequency of the pump or compressor appears in the spectrum graph, a pump or compressor fault signal is generated;

[0058] The operating frequency of the valve controlling the flow of the fluid is obtained, and if an abnormal peak corresponding to the operating frequency of the valve appears in the spectrum graph, a valve fault signal is generated.

[0059] As a further solution of the present invention: based on the various types of problems described in claim 9, corresponding alarm signals are generated according to actual conditions, and corresponding alarm reminders are sent to management personnel.

[0060] Beneficial effects of the present invention:

[0061] (1) The present invention realizes refined monitoring of the equipment operation status by collecting the vibration signal value of the loading and unloading arm equipment and extracting its time domain characteristics (including vibration peak value, average value, standard deviation and kurtosis). Specifically, by calculating the time domain characteristics of the vibration signal in each time subunit and calculating the ratio with the maximum and minimum values ​​in the monitoring period, a series of ratios (such as vibration peak value ratio, mean value ratio, standard deviation ratio and kurtosis ratio) are generated. This technical feature can accurately reflect the changes in the vibration characteristics of the equipment in different time periods, solves the problem that traditional monitoring methods cannot capture small changes in the equipment in real time, and makes fault warning more timely and effective. In particular, by introducing the concept of vibration score and comparing it with the preset threshold, it can quickly distinguish between normal operation and abnormal state, thereby providing a reliable basis for equipment maintenance;

[0062] (2) The present invention analyzes the time domain characteristics of the vibration signal based on the abnormal signal of the equipment separately, generates a corresponding warning signal, and further constructs a spectrum diagram to identify the characteristic frequency, thereby effectively solving the problem in the prior art that it is difficult to accurately identify the source of the fault. Specifically, by statistically analyzing the time domain characteristics of the vibration signal, it is possible to accurately determine whether the equipment has bearing damage, abnormal gear meshing, imbalance or misalignment. For example, by analyzing the statistical value of the vibration peak, it is possible to determine whether the equipment bearing is damaged or whether the gear meshing is normal; by analyzing the statistical value of the average data, it is possible to identify whether the equipment has imbalance or misalignment. This technical feature not only improves the accuracy of fault diagnosis, but also provides maintenance personnel with specific fault location information, thereby significantly improving maintenance efficiency and the reliability of equipment operation.

[0063] (3) The present invention solves the problem that the fault frequency components cannot be accurately identified in traditional fault diagnosis by identifying characteristic frequencies based on the spectrum diagram and obtaining the location of the fault through matching analysis. In specific implementation, the vibration signal is converted into a frequency domain signal through fast Fourier transform, thereby displaying the amplitudes of different frequency components. Based on this, the fundamental frequency and harmonic frequencies of the equipment can be identified, and by comparing with the preset threshold, it can be determined whether there are abnormal peaks. In addition, the characteristic frequency can be calculated according to the specific structural parameters of the equipment, and the corresponding abnormal peak can be found in the spectrum diagram, so as to accurately determine the fault type of the equipment. This technical feature not only improves the accuracy and efficiency of fault diagnosis, but also provides a scientific basis for formulating preventive maintenance plans, thereby ensuring the long-term stable operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The present invention will be further described below in conjunction with the accompanying drawings.

[0065] Figure 1 It is a flowchart of the specific steps of a rapid splicing method for a cryogenic fluid loading and unloading arm of the present invention;

[0066] Figure 2 This is a flow chart of equipment status identification in a rapid splicing method for a cryogenic fluid loading and unloading arm according to an embodiment of the present invention;

[0067] Figure 3 The present invention is a flowchart of early warning signal recognition in a rapid splicing method for a cryogenic fluid loading and unloading arm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Example 1

[0070] See also Figure 1-3 As shown, the present invention is a rapid splicing method for a cryogenic fluid loading and unloading arm, comprising the following steps:

[0071] Collect the vibration signal value of the loading and unloading arm equipment to obtain the time domain characteristics of the vibration signal;

[0072] Among them, the time domain characteristics of the vibration signal include vibration peak value, average value, standard deviation and kurtosis;

[0073] Based on the time domain characteristics of the vibration signal, the vibration score of the equipment is obtained and the equipment status signal is generated;

[0074] Among them, the device status signal includes a device abnormality signal and a device normal signal;

[0075] Based on the abnormal signal of the equipment, the time domain characteristics of the vibration signal are analyzed separately to generate corresponding warning signals;

[0076] Based on the early warning signal, all vibration signals are integrated to construct a spectrum diagram;

[0077] The characteristic frequency is identified on the spectrum graph to obtain abnormal peaks with abnormally high amplitudes;

[0078] According to the unique spectrum characteristics corresponding to different types of mechanical failures, the location of the failure is obtained through matching analysis, and the corresponding warning signal is issued at the same time.

[0079] Example 2

[0080] See also Figure 2As shown, the monitoring period is divided into several time sub-units of equal length, and the time sub-unit is divided into several time small units of equal length, and the vibration signal value of the equipment is collected when the time small unit is in the middle moment;

[0081] All vibration signal values ​​are processed to extract the time domain characteristics of the vibration signal. Specifically:

[0082] All vibration signal values ​​in the time sub-unit are integrated into a vibration sequence, and the vibration signal value with the largest absolute value in the vibration sequence is extracted and recorded as the unit vibration peak value;

[0083] Process all unit vibration peaks within the monitoring period to obtain the maximum vibration peak and the minimum vibration peak;

[0084] The difference between the unit vibration peak value and the minimum vibration peak value is calculated as a ratio to the difference between the maximum vibration peak value and the minimum vibration peak value, so as to obtain the vibration peak value ratio within the time subunit;

[0085] The vibration signal in the time subunit is processed according to the mean value calculation formula to obtain the average data of the vibration signal in the time subunit;

[0086] Integrate all the average data within the monitoring period to obtain the maximum average data and the minimum average data;

[0087] The difference between the average data and the minimum average data of the vibration signal in the time subunit is calculated as a ratio to the difference between the maximum average data and the minimum average data, so as to obtain the mean ratio of the vibration signal in the time subunit;

[0088] The vibration signal in the time subunit is processed according to the standard deviation calculation formula to obtain the standard deviation of the vibration signal in the time subunit;

[0089] Integrate all standard deviations within the monitoring period to obtain the maximum standard deviation and the minimum standard deviation;

[0090] The difference between the standard deviation and the minimum standard deviation of the vibration signal in the time subunit is calculated as a ratio to the difference between the maximum standard deviation and the minimum standard deviation, so as to obtain the standard deviation ratio of the vibration signal in the time subunit;

[0091] Based on the standard deviation of the vibration signal within the time subunit, the formula The kurtosis EK of the vibration signal in the time subunit is calculated;

[0092] Among them, PJ is the average data of the vibration signal in the time subunit, CC is the standard deviation of the vibration signal in the time subunit, and x i is the device signal value of the i-th time unit in the time subunit, i = 1, 2, ..., N;

[0093] Integrate all the kurtosis within the monitoring period to obtain the maximum kurtosis and the minimum kurtosis;

[0094] The kurtosis ratio of the vibration signal in the time subunit is obtained by calculating the ratio of the difference between the kurtosis and the minimum kurtosis of the vibration signal in the time subunit to the difference between the maximum kurtosis and the minimum kurtosis;

[0095] The vibration score ZD of the equipment is calculated by the formula FS = b1 × FB + b2 × PB + b3 × CB + b4 × EB;

[0096] Wherein, FB is the vibration peak ratio of the vibration signal in the time subunit, PB is the mean ratio, CB is the standard deviation ratio, EB is the kurtosis ratio, b1, b2, b3 and b4 are the weight coefficients of the vibration peak ratio, mean ratio, standard deviation ratio and kurtosis ratio respectively;

[0097] comparing a vibration score of the device to a vibration score threshold of the device;

[0098] If the vibration score of the device is greater than or equal to the vibration score threshold of the device, it means that there is an abnormality in the device, and a device abnormality signal is generated;

[0099] If the vibration score of the device is less than the vibration score threshold of the device, it means that there is no abnormality in the device and a normal signal of the device is generated.

[0100] Example 3

[0101] Based on the abnormal equipment signal generated above, since the time domain characteristics of the vibration signal are a series of statistical indicators extracted from the original vibration signal of the equipment, these indicators can be used to understand the operating status of the equipment. By analyzing whether these indicators are abnormal, the abnormal factors of the equipment can be determined, specifically:

[0102] Based on the vibration peak value of the vibration signal in the time subunit, all vibration peak values ​​in the monitoring period are integrated to obtain a vibration peak value group;

[0103] The vibration peak value group is processed according to the mean value calculation formula to obtain the vibration peak value mean value;

[0104] The vibration peak value group is processed according to the standard deviation calculation formula to obtain the standard deviation of the vibration peak value;

[0105] The difference between the vibration peak value and the vibration peak mean value of the vibration signal in the time subunit is calculated by ratio with the vibration peak standard deviation to obtain the statistical value of the vibration peak value;

[0106] comparing the absolute value of the statistical value of the vibration peak value of the vibration signal within the time subunit with the statistical value threshold of the vibration peak value;

[0107] If the absolute value of the statistical value is greater than or equal to the statistical value threshold of the vibration peak value, it means that the vibration peak value is abnormal, and an early warning signal of equipment bearing damage and gear meshing is generated;

[0108] It should be noted that the vibration peak of the equipment can reflect the presence of transient events or impacts in the vibration signal. Peak data with abnormally high absolute values ​​indicates that there is a fault or impact event in the equipment, such as bearing damage or gear meshing abnormality;

[0109] Based on the average data of the vibration signal in the time subunit, all the average data in the monitoring period are integrated to obtain an average data group;

[0110] The average data group is processed according to the mean calculation formula to obtain the mean of the average data;

[0111] The average data group is processed according to the standard deviation calculation formula to obtain the standard deviation of the average data;

[0112] The statistical value of the average data is obtained by calculating the ratio of the difference between the average data of the vibration signal in the time subunit and the mean of the average data to the standard deviation of the average data point;

[0113] Compare the statistical value of the average data of the vibration signal in the time subunit with the statistical value threshold of the average data;

[0114] If the statistical value of the average data is greater than or equal to the statistical value threshold of the average data, it means that the average data is abnormal, and an equipment imbalance and misalignment warning signal is generated;

[0115] It should be noted that the average data of the vibration signal is usually used to determine whether the vibration signal has an obvious DC component. Ideally, the average data of the vibration signal should be zero, indicating that the amplitude of the vibration signal in the positive and negative directions is roughly equal. If the average data deviates significantly from zero, it indicates that the equipment has misalignment or imbalance problems.

[0116] Based on the standard deviation of the vibration signal in the time subunit, all standard deviations in the monitoring period are integrated to obtain a standard deviation group of the vibration signal;

[0117] The standard deviation group of the vibration signal is processed according to the mean calculation formula to obtain the mean of the standard deviation of the vibration signal;

[0118] The standard deviation group of the vibration signal is processed according to the standard deviation calculation formula to obtain the standard deviation of the vibration signal standard deviation;

[0119] The difference between the standard deviation of the vibration signal in the time subunit and the mean of the standard deviation of the vibration signal is calculated by ratio with the standard deviation of the standard deviation of the vibration signal to obtain a statistical value of the standard deviation;

[0120] comparing a statistical value of a standard deviation of the vibration signal within a time subunit with a statistical value threshold of the standard deviation;

[0121] If the statistical value of the standard deviation is greater than or equal to the statistical value threshold of the standard deviation, it means that the standard deviation is abnormal, and an early warning signal of internal wear of the equipment is generated;

[0122] It should be noted that the larger the standard deviation, the greater the change in the amplitude of the vibration, indicating that the equipment has a large vibration or instability. When the equipment is worn inside, the standard deviation will increase.

[0123] Based on the kurtosis of the vibration signal in the time subunit, all kurtosis in the monitoring period are integrated to obtain a kurtosis group;

[0124] The kurtosis group is processed according to the mean calculation formula to obtain the mean of the kurtosis;

[0125] The kurtosis group is processed according to the standard deviation calculation formula to obtain the standard deviation of the kurtosis;

[0126] The kurtosis of the vibration signal in the time subunit is calculated by calculating the ratio of the difference between the kurtosis mean and the standard deviation of the kurtosis to obtain the statistical value of the kurtosis;

[0127] Comparing the statistical value of the kurtosis of the vibration signal in the time subunit with a statistical value threshold of the kurtosis;

[0128] If the kurtosis statistical value is greater than or equal to the kurtosis statistical value threshold, it means that the kurtosis is abnormal, and a local equipment failure warning signal is generated;

[0129] It should be noted that the higher the kurtosis of the vibration signal, the higher the frequency of occurrence of sharp peaks in the signal. The increase in kurtosis is related to local faults. When a local fault occurs in the equipment, sharp peaks will be generated in the vibration signal. These peaks appear as an increase in kurtosis in the time domain.

[0130] Example 4

[0131] Based on the above-mentioned abnormal signals of various types of equipment, the vibration frequency and amplitude of the equipment are analyzed through the spectrum characteristics of the vibration signal to see if there is an abnormal peak at a specific frequency. Specifically:

[0132] Obtain the vibration signal of the device when the time unit in the time subunit is in the middle moment;

[0133] Processing all vibration signals within the monitoring period to obtain spectrum data of the vibration signals within the monitoring period;

[0134] The spectrum data of the vibration signal is converted into a graph, with the horizontal axis representing frequency and the vertical axis representing amplitude;

[0135] It should be noted that the fast Fourier transform algorithm can convert the vibration signal into a frequency domain signal, thereby displaying the amplitude of different frequency components. The spectrum diagram constructed by the spectrum can display the intensity of each frequency component in the device;

[0136] Based on the spectrum diagram, analyze and identify the fundamental frequency and harmonic frequencies of the device, specifically:

[0137] Get the motor speed Z of the device through the formula Get the fundamental frequency f0 of the device, and traverse the spectrum graph to get the frequency peak closest to the fundamental frequency f0 in the spectrum graph, which is recorded as the fundamental frequency peak;

[0138] Identify the frequency peak that is an integer multiple of the fundamental frequency f0 in the spectrum graph and record it as the harmonic frequency nf0 of the device;

[0139] Wherein, the harmonic frequency is an integer multiple of the fundamental frequency, n is the multiple, and n is a positive integer greater than 1, n=2, 3, ..., n, when the fundamental frequency is f0, then 2f0, 3f0, ..., nf0 are the harmonic frequencies;

[0140] Based on the equipment imbalance and misalignment warning signals, the fundamental frequency peak value is compared with the fundamental frequency peak value threshold. If the fundamental frequency peak value is greater than or equal to the fundamental frequency peak value threshold, it means that the peak value at the fundamental frequency is an abnormal peak value, and an equipment imbalance alarm signal is generated;

[0141] The peak values ​​at the base frequency and twice the base frequency are compared with the base frequency peak value threshold at the same time. If the peak values ​​at the base frequency and twice the base frequency are both greater than or equal to the base frequency peak value threshold, it means that the peak values ​​at the base frequency and twice the base frequency are both abnormal peak values, and an equipment misalignment alarm signal is generated.

[0142] Obtain peaks in the spectrum that are inconsistent with the fundamental frequency and the fundamental frequency harmonic frequency, calculate the difference between these peaks and the fundamental frequency peak, and if the difference is greater than the peak difference threshold, mark the peak as an abnormal peak;

[0143] Based on the equipment bearing damage and gear meshing warning signals, the number N and diameter d of the rolling elements in the equipment bearing and the diameter D of the raceway in the bearing are obtained;

[0144] By formula The bearing characteristic frequency Gd is calculated;

[0145] Based on the bearing characteristic frequency Gd, if an abnormal peak corresponding to the bearing characteristic frequency Gd appears in the spectrum diagram, it means that the equipment is bearing damaged, and a bearing damage alarm signal is generated;

[0146] Obtain the number of teeth Nc and the speed Zsc of the driving gear in the equipment gearbox;

[0147] By formula The gear meshing frequency Cl is calculated;

[0148] Based on the gear meshing frequency Cl, if an abnormal peak corresponding to the gear meshing frequency Cl appears in the spectrum diagram, it means that the equipment is gear damaged, and a gear damage alarm signal is generated;

[0149] Based on the internal wear warning signal of the equipment, the formula Get the gear rotation frequency fz;

[0150] Based on the gear rotation frequency fz, if an abnormal peak corresponding to the gear rotation frequency fz appears in the spectrum diagram, it means that the equipment is gear worn, and a gear wear alarm signal is generated;

[0151] Based on the number N and diameter d of the rolling elements in the equipment bearing and the diameter D of the bearing raceway, the formula The cage rotation frequency Bc is calculated;

[0152] Based on the cage rotation frequency Bc, if an abnormal peak corresponding to the cage rotation frequency Bc appears in the spectrum diagram, it means that the equipment is wearing the bearing and the parts connected to the bearing, and a bearing wear alarm signal is generated;

[0153] Based on the equipment local fault warning signal, the equipment power frequency is obtained. If an abnormal peak corresponding to the equipment power frequency appears in the spectrum diagram, it means that the equipment motor has a fault, and a motor fault alarm signal is generated;

[0154] Based on the fact that the device is a hydraulic or pneumatic system, the operating frequency of the pump or compressor is obtained. If an abnormal peak corresponding to the operating frequency of the pump or compressor appears in the spectrum diagram, it means that the pump or compressor of the device is faulty, and a pump or compressor fault signal is generated;

[0155] Obtain the operating frequency of the valve that controls the flow of fluid. If an abnormal peak corresponding to the operating frequency of the valve appears in the spectrum, it means that the equipment valve is faulty and a valve fault signal is generated;

[0156] Based on the above types of problems, corresponding alarm signals are generated according to the actual situation, and targeted alarm reminders are sent to management personnel.

[0157] Working principle of the present invention: The purpose of the present invention is to provide a rapid splicing method for a cryogenic fluid loading and unloading arm. The technical scheme of the present invention is as follows: First, the monitoring period is divided into a plurality of time subunits of equal length, and each time subunit is further subdivided into time small units of equal length. At the middle moment of each time small unit, the vibration signal value of the equipment is collected, and then all the collected vibration signal values ​​are integrated to extract the time domain characteristics of the vibration signal; then, based on the extracted time domain characteristics, the vibration score of the equipment is calculated, and the equipment status signal is generated accordingly. For abnormal signals, their time domain characteristics are further analyzed to generate early warning signals; then, all vibration signals are converted into frequency domain signals, a spectrum diagram is constructed, and the characteristic frequencies and their corresponding abnormal peaks in the spectrum diagram are identified; finally, according to the unique spectrum characteristics corresponding to different types of mechanical failures, the location of the failure is accurately determined through matching analysis, and a corresponding warning signal is issued.

[0158] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A rapid splicing method for a cryogenic fluid loading and unloading arm, characterized in that: The following steps are involved: Collect the vibration signal value of the loading and unloading arm equipment to obtain the time domain characteristics of the vibration signal; Among them, the time domain characteristics of the vibration signal include vibration peak value, average value, standard deviation and kurtosis; Process the time domain characteristics of the vibration signal to obtain the vibration score of the equipment and generate the equipment status signal; Among them, the device status signal includes a device abnormality signal and a device normal signal; Based on the abnormal signal of the equipment, the time domain characteristics of the vibration signal are analyzed separately to generate corresponding warning signals; Based on the early warning signal, all vibration signals are integrated to construct a spectrum diagram; The characteristic frequency is identified on the spectrum graph to obtain abnormal peaks with abnormally high amplitudes; According to the unique spectrum characteristics corresponding to different types of mechanical failures, the location of the failure is obtained through matching analysis, and the corresponding warning signal is issued at the same time.

2. A rapid splicing method for a cryogenic fluid loading and unloading arm according to claim 1, characterized in that: The process of obtaining the time domain features of the vibration signal is as follows: Divide the monitoring period into several time sub-units of equal length, divide the time sub-unit into several time small units of equal length, and collect the vibration signal value of the equipment when the time small unit is in the middle moment; All vibration signal values ​​in the time sub-unit are integrated into a vibration sequence, and the vibration signal value with the largest absolute value in the vibration sequence is extracted and recorded as the unit vibration peak value; The vibration signal in the time subunit is processed according to the mean value calculation formula to obtain the average data of the vibration signal in the time subunit; The vibration signal in the time subunit is processed according to the standard deviation calculation formula to obtain the standard deviation of the vibration signal in the time subunit; Based on the standard deviation of the vibration signal in the time subunit, the kurtosis EK of the vibration signal in the time subunit is calculated.

3. A rapid splicing method for a cryogenic fluid loading and unloading arm according to claim 2, characterized in that: The process of processing the time domain characteristics of the vibration signal is: Process all unit vibration peaks within the monitoring period to obtain the maximum vibration peak and the minimum vibration peak; The difference between the unit vibration peak value and the minimum vibration peak value is calculated as a ratio to the difference between the maximum vibration peak value and the minimum vibration peak value, so as to obtain the vibration peak value ratio of the vibration signal in the time subunit; As above, the mean ratio of the vibration signal in the time subunit, the standard deviation ratio of the vibration signal in the time subunit, and the kurtosis ratio of the vibration signal in the time subunit are obtained.

4. A rapid splicing method for a cryogenic fluid loading and unloading arm according to claim 3, characterized in that: The vibration score of the equipment is calculated based on the vibration peak ratio, mean ratio, standard deviation ratio and kurtosis ratio of the vibration signal within the time subunit.

5. A rapid splicing method for a cryogenic fluid loading and unloading arm according to claim 4, characterized in that: Based on the vibration score of the device, comparing the vibration score of the device to a vibration score threshold of the device; If the vibration score of the device is greater than or equal to the vibration score threshold of the device, a device abnormality signal is generated; If the vibration score of the device is less than the vibration score threshold of the device, a device normal signal is generated.

6. A rapid splicing method for a cryogenic fluid loading and unloading arm according to claim 5, characterized in that: Based on the abnormal signal of the equipment, all vibration peak values ​​within the monitoring period are integrated to obtain a vibration peak value group; The vibration peak value group is processed according to the mean value calculation formula to obtain the vibration peak value mean value; The vibration peak value group is processed according to the standard deviation calculation formula to obtain the standard deviation of the vibration peak value; The difference between the vibration peak value and the vibration peak mean value of the vibration signal in the time subunit is calculated by ratio with the vibration peak standard deviation to obtain the statistical value of the vibration peak value; Similarly, the statistical values ​​of the average data, the standard deviation, and the kurtosis are obtained.

7. A rapid splicing method for a cryogenic fluid loading and unloading arm according to claim 6, characterized in that: comparing the absolute value of the statistical value of the vibration peak value of the vibration signal within the time subunit with the statistical value threshold of the vibration peak value; If the absolute value of the statistical value is greater than or equal to the statistical value threshold of the vibration peak value, an early warning signal of equipment bearing damage and gear meshing is generated; Compare the statistical value of the average data of the vibration signal in the time subunit with the statistical value threshold of the average data; If the statistical value of the average data is greater than or equal to the statistical value threshold of the average data, an equipment imbalance and misalignment warning signal is generated; comparing a statistical value of a standard deviation of the vibration signal within a time subunit with a statistical value threshold of the standard deviation; If the statistical value of the standard deviation is greater than or equal to the statistical value threshold of the standard deviation, an internal wear warning signal of the equipment is generated; Comparing the statistical value of the kurtosis of the vibration signal in the time subunit with a statistical value threshold of the kurtosis; If the statistical value of the kurtosis is greater than or equal to the statistical value threshold of the kurtosis, a local equipment failure warning signal is generated.

8. A rapid splicing method for a cryogenic fluid loading and unloading arm according to claim 7, characterized in that: The process of identifying the characteristic frequency of the spectrum and obtaining the abnormal peak with abnormally high amplitude is as follows: Based on the spectrum graph, the motor speed of the device is obtained to obtain the fundamental frequency of the device. By traversing the spectrum graph, the frequency peak in the spectrum graph that is the same as the fundamental frequency is obtained and recorded as the fundamental frequency peak. Identify the frequency peaks in the spectrum graph that are integer multiples of the fundamental frequency and record them as the harmonic frequencies of the device.

9. A rapid splicing method for a cryogenic fluid loading and unloading arm according to claim 7, characterized in that: Based on the equipment imbalance and misalignment warning signals, the fundamental frequency peak value is compared with the fundamental frequency peak value threshold value. If the fundamental frequency peak value is greater than or equal to the fundamental frequency peak value threshold value, an equipment imbalance alarm signal is generated; The peak values ​​at the base frequency and twice the base frequency are compared with the base frequency peak value threshold at the same time. If the peak values ​​at the base frequency and twice the base frequency are both greater than or equal to the base frequency peak value threshold, an equipment misalignment alarm signal is generated. Based on the equipment bearing damage and gear meshing warning signals, the number and diameter of the rolling elements in the equipment bearing and the diameter of the raceway in the bearing are obtained to obtain the bearing characteristic frequency; Based on the characteristic frequency of the bearing, if an abnormal peak corresponding to the characteristic frequency of the bearing appears in the spectrum graph, a bearing damage alarm signal is generated; Obtain the number of teeth and speed of the driving gear in the equipment gearbox to obtain the gear meshing frequency; Based on the gear meshing frequency, if an abnormal peak corresponding to the gear meshing frequency appears in the spectrum graph, a gear damage alarm signal is generated; Based on the internal wear warning signal of the equipment, the gear rotation frequency is obtained; If an abnormal peak corresponding to the gear rotation frequency appears in the spectrum graph, a gear wear alarm signal is generated; Based on the number and diameter of the rolling elements in the equipment bearing and the diameter of the raceway in the bearing, the cage rotation frequency is obtained; If an abnormal peak corresponding to the cage rotation frequency appears in the spectrum graph, a bearing wear alarm signal is generated; Based on the equipment local fault warning signal, the equipment power frequency is obtained. If an abnormal peak corresponding to the equipment power frequency appears in the spectrum graph, a motor fault alarm signal is generated; Based on the fact that the device is a hydraulic or pneumatic system, the operating frequency of the pump or compressor is obtained, and if an abnormal peak corresponding to the operating frequency of the pump or compressor appears in the spectrum graph, a pump or compressor fault signal is generated; The operating frequency of the valve controlling the flow of the fluid is obtained, and if an abnormal peak corresponding to the operating frequency of the valve appears in the spectrum graph, a valve fault signal is generated.

10. A rapid splicing method for a cryogenic fluid loading and unloading arm according to claim 9, characterized in that: Based on the various types of problems described in claim 9, corresponding alarm signals are generated according to actual conditions, and corresponding alarm reminders are sent to management personnel.

Images