A chemical centrifugal pump operation scheduling optimization system and method

Abstract

This invention specifically relates to a system and method for optimizing the operation and scheduling of chemical centrifugal pumps, belonging to the field of intelligent operation and maintenance technology for chemical equipment. It includes: sending various types of data to a data calculation unit for processing to obtain characteristic anomaly coefficients, shaft temperature anomaly coefficients, and offset anomaly coefficients corresponding to each type of data; and comprehensively processing the three coefficients to obtain bearing fault assessment coefficients. This invention overcomes the limitations of traditional single-parameter monitoring by comprehensively capturing bearing operating status information through multi-dimensional data acquisition. In data processing, it combines bearing structural parameters to calculate fault characteristic frequencies, deeply analyzes details such as rotational frequency sideband spectra, and constructs multiple quantitative indicators for comprehensive evaluation. Compared to traditional methods, it can not only accurately distinguish faults in the bearing's inner ring, outer ring, and rolling elements, but also identify complex problems such as shaft misalignment, improving fault diagnosis accuracy, effectively avoiding missed diagnoses and misdiagnoses, and providing a reliable basis for equipment maintenance.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance technology for chemical equipment, and in particular to a system and method for optimizing the operation scheduling of chemical centrifugal pumps. Background Technology

[0002] As a core fluid transport device in chemical production processes, the operating status of chemical centrifugal pumps directly affects the stability and energy consumption of the entire production system. Currently, the main technical problems encountered by chemical centrifugal pumps during operation are as follows:

[0003] Traditional fault monitoring methods rely on single-parameter detection, such as judging equipment status solely by vibration amplitude or temperature changes, making it difficult to comprehensively capture the complex fault characteristics of bearings. For example, when monitoring only vibration amplitude, it is impossible to distinguish specific faults in the inner ring, outer ring, or rolling elements of the bearing, and it is also difficult to identify vibration modulation phenomena caused by shaft misalignment or looseness, resulting in low fault diagnosis accuracy and a high risk of missed or misdiagnosed faults.

[0004] In terms of energy-saving optimization, existing technologies lack a synergistic analysis of centrifugal pump operating parameters and equipment health status. Typically, pump speed or flow rate is adjusted solely based on process requirements, without considering the impact of bearing failures on energy consumption. When bearings experience wear, misalignment, or other faults, the pump's frictional resistance increases, leading to a significant rise in energy consumption. However, because the faults are not detected and the operating strategy is not adjusted in time, serious energy waste occurs.

[0005] Existing scheduling systems lack a fault-based hierarchical handling mechanism. When a fault occurs, they either adopt an overly conservative shutdown strategy, affecting production continuity, or fail to handle serious faults in a timely manner, leading to equipment damage or even safety accidents. Furthermore, they cannot dynamically adjust operating parameters according to the fault level, making it difficult to achieve a balance between energy saving and production efficiency.

[0006] At the data processing and analysis level, existing technologies lack sufficient depth in integrating multi-source data. Vibration, temperature, displacement, and other data are analyzed independently, failing to fully explore the correlations between data, making it impossible to construct quantitative indicators that comprehensively reflect the equipment status, and hindering the accurate assessment and prediction of the centrifugal pump's operating status.

[0007] Therefore, a chemical centrifugal pump operation scheduling optimization system and method are needed to address the problems mentioned above. Summary of the Invention

[0008] The purpose of this invention is to provide a chemical centrifugal pump operation scheduling optimization system and method to solve the above-mentioned problems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A chemical centrifugal pump operation scheduling optimization system includes:

[0011] The data collection module is configured to acquire relevant data information of the centrifugal pump bearing, and to preprocess and classify the acquired data information to obtain bearing characteristic frequency data, bearing temperature data, and shaft dynamic runout data.

[0012] The data analysis and processing module is configured to send the obtained data to the data calculation unit for processing to obtain the characteristic anomaly coefficient, shaft temperature anomaly coefficient, and offset anomaly coefficient corresponding to each type of data; and to perform comprehensive processing on the three coefficients to obtain the bearing failure assessment coefficient.

[0013] The assessment and diagnosis module is configured to match the corresponding fault level based on the bearing's fault coefficient;

[0014] The optimized scheduling module is configured to perform corresponding optimized scheduling processing based on the bearing's fault level.

[0015] Preferably, in the data collection module, the acquisition methods for various types of data are as follows:

[0016] Bearing characteristic frequency data: Sensors are arranged in the vertical, horizontal and axial directions of the bearing housing; the sampling frequency is set according to the bearing speed and the expected analysis frequency range to obtain the bearing vibration frequency data;

[0017] Bearing temperature data: Temperature data of the bearing housing and adjacent pump casing are obtained by measuring the temperature using an infrared thermal imager.

[0018] Axis dynamic runout data: Displacement data of the shaft in a preset direction is obtained through displacement sensors.

[0019] Preferably, the process of obtaining the characteristic anomaly coefficients includes:

[0020] Consult the bearing model manual to obtain the bearing structural parameters, including the number of rollers, roller diameter, bearing pitch circle diameter, and contact angle; calculate the outer ring failure frequency, inner ring failure frequency, rolling element failure frequency, and amplitude of their corresponding frequency bands according to the bearing failure characteristic frequency calculation formula; and calculate the difference between the frequency band amplitude and the corresponding preset reference value to obtain the outer ring failure difference, inner ring failure difference, and rolling element failure difference.

[0021] Each corresponding fault difference value is preset with an allowable range. Fault differences that are not within the corresponding allowable range are recorded as outer ring fault difference value, inner ring fault difference value, and rolling fault difference value.

[0022] The ring fault difference value is obtained by weighted summation of the outer ring fault difference value, inner ring fault difference value, and rolling fault difference value.

[0023] Preferably, the method further includes:

[0024] Determine the bearing's rotational frequency, determine the amplitude of the sideband spectral lines, and obtain the amplitude of each sideband spectral line sequentially based on the distance from the origin, with the center frequency as the origin.

[0025] If the amplitude of two adjacent sideband spectral lines closer to the origin is less than the amplitude farther from the origin, then the amplitude farther from the origin is marked as an abnormal amplitude; and the difference between the abnormal amplitude and the amplitude closer to the origin is calculated, and the absolute value is taken to obtain the abnormal amplitude difference value.

[0026] Sort all abnormal amplitude values ​​in descending order of numerical value, extract the largest abnormal amplitude value, and the number of amplitude values ​​between the largest abnormal amplitude value and the abnormal amplitude values ​​on both sides of it. Take the reciprocal of the number of amplitude values ​​to obtain the frequency amplitude.

[0027] The characteristic anomaly coefficient is obtained by weighting the cyclic outlier and the frequency amplitude.

[0028] Preferably, the process of obtaining the shaft temperature anomaly coefficient includes:

[0029] At preset time intervals, the bearing temperature and the temperature of the adjacent pump casing are simultaneously acquired; the highest bearing temperature value and the highest pump casing temperature value are extracted from the bearing temperature and the temperature of the adjacent pump casing, respectively; and the positions corresponding to the highest bearing temperature value and the highest pump casing temperature value are recorded as the bearing high temperature point and the pump casing high temperature point, respectively.

[0030] The difference between the highest bearing temperature and the highest pump casing temperature is calculated, and the absolute value is taken to obtain the shaft casing temperature difference value; the shaft casing temperature difference value corresponding to each time interval is obtained in sequence, and the average value of each shaft casing temperature difference value is calculated to obtain the average shaft casing temperature difference value;

[0031] The allowable range of the average shaft housing temperature difference is preset, and the shaft housing temperature difference value that is not within the allowable range of the average shaft housing temperature difference is marked as a temperature difference abnormal value;

[0032] Sort all temperature difference constants in descending order of numerical value, and extract the two largest temperature difference constants and their corresponding two sets of bearing high temperature points and pump casing high temperature points.

[0033] Connect the high-temperature point of the bearing and the high-temperature point of the pump casing with a straight line, and calculate the length of the straight line to obtain the temperature change line length; then obtain the temperature change line lengths corresponding to the two sets of high-temperature points of the bearing and the pump casing in sequence.

[0034] The two obtained temperature variation line lengths are used as the major and minor semi-axes of the ellipse, respectively. An ellipse model is constructed, and the area of ​​the ellipse model is calculated and denoted as the axial temperature anomaly coefficient.

[0035] Preferably, the process of obtaining the offset anomaly coefficient includes:

[0036] After the equipment is running stably, start the data acquisition instrument to acquire the displacement data of the bearing in the X and Y directions in real time;

[0037] The displacements in the X direction are sorted in descending order of their numerical values, and the two largest X-direction displacements are extracted. A mark is made on the bearing, and a straight line is drawn from the center of the bearing to the mark to obtain a baseline. The baselines after the two largest X-direction displacements of the bearing and the corresponding angle changes between them are obtained to obtain two angle changes in the X-direction displacement, which are used as the original first X-angle value and the original second X-angle value, respectively.

[0038] Similarly, following the above process of analyzing the bearing displacement data in the X direction, the bearing displacement data in the Y direction is analyzed to obtain the first Y angle value and the second Y angle value.

[0039] The original Y angle value and the original Y angle value corresponding to the displacement of the bearing in the Y direction when obtaining the original X angle value and the original X angle value;

[0040] When obtaining the Y angle value of square one and the Y angle value of square two, the corresponding X angle values ​​of square one and the X angle values ​​of square two are the bearing displacement in the X direction.

[0041] A total of four displacement changes of the bearing were obtained, and the positions of the four displacement changes corresponding to the marked points in three-dimensional space were obtained to obtain four spatial marked points;

[0042] Four spatial markers are simultaneously marked on a plane, and the four spatial markers are connected by straight lines to obtain a closed figure. The area of ​​the closed figure is calculated and denoted as the offset anomaly coefficient.

[0043] Preferably, after normalizing the characteristic anomaly coefficient, shaft temperature anomaly coefficient, and offset anomaly coefficient, the characteristic anomaly coefficient and shaft temperature anomaly coefficient are respectively used as two legs of a right triangle, and the remaining leg is connected to form a complete right triangle. The offset anomaly coefficient is used as the height of the right triangle to construct a triangular pyramid. The volume of the triangular pyramid is calculated and recorded as the fault assessment coefficient.

[0044] Three threshold ranges are preset, and each threshold range corresponds to a fault level. The bearing fault assessment coefficient is matched with the three threshold ranges to obtain the fault level corresponding to the bearing fault assessment coefficient, including minor fault, moderate fault and severe fault.

[0045] Preferably, in the event of a minor fault: the equipment is kept running continuously while the fault is controlled to progress and energy consumption is reduced;

[0046] In the case of a moderate failure: prioritize ensuring production continuity by switching equipment or reducing load to prevent the failure from escalating to a severe failure;

[0047] In case of severe failure: immediately stop the operation of the faulty pump and initiate the emergency shutdown procedure to prevent equipment damage or safety accidents.

[0048] A method for optimizing the operation and scheduling of chemical centrifugal pumps includes the following steps:

[0049] Multi-dimensional data acquisition: Collect bearing-related information, perform noise reduction and filtering preprocessing, and establish a benchmark database;

[0050] Anomaly coefficient calculation: The corresponding anomaly coefficient is calculated based on the collected bearing-related information;

[0051] Fault level assessment: The three abnormal coefficients are normalized to obtain fault assessment coefficients, and then matched with fault levels.

[0052] Tiered scheduling execution: corresponding measures are taken for different fault levels.

[0053] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0054] 1. This invention overcomes the limitations of traditional single-parameter monitoring by comprehensively capturing bearing operating status information through multi-dimensional data acquisition. In data processing, it calculates fault characteristic frequencies by combining bearing structural parameters, deeply analyzes details such as rotational frequency sideband spectra, and constructs multiple quantitative indicators for comprehensive evaluation. Compared with traditional methods, it can not only accurately distinguish faults in the bearing inner ring, outer ring, and rolling elements, but also identify complex problems such as shaft misalignment. The accuracy of fault diagnosis is improved, effectively avoiding missed diagnoses and misdiagnoses, and providing a reliable basis for equipment maintenance.

[0055] 2. This invention achieves energy-saving optimization by combining equipment health status with operating parameters; it formulates differentiated scheduling strategies for different fault levels; in the case of minor faults, it precisely reduces energy consumption through frequency conversion speed regulation to reduce unnecessary energy waste; in the case of moderate faults, it reasonably switches the standby pump and optimizes parameters such as flow rate and speed to reduce overall energy consumption while ensuring production. Attached Figure Description

[0056] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0057] Figure 1 This is a system structure diagram of the present invention;

[0058] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0059] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0060] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0061] Example 1

[0062] Its specific implementation method is combined with the appendix Figure 1 and attached Figure 2 Please provide a detailed explanation.

[0063] Appendix Figure 1 This invention provides a structural block diagram of a chemical centrifugal pump operation scheduling optimization system, which shows the connection relationship between the data collection module, data analysis and processing module, evaluation and diagnosis module, and optimization scheduling module, and marks the main functional interaction flow of each module.

[0064] Appendix Figure 2 The flowchart of a chemical centrifugal pump operation scheduling optimization method provided in this embodiment of the invention shows the complete steps from multi-dimensional data acquisition to closed-loop optimization iteration.

[0065] In this embodiment, it includes:

[0066] The data collection module is configured to acquire relevant data information of the centrifugal pump bearing, and to preprocess and classify the acquired data information to obtain bearing characteristic frequency data, bearing temperature data, and shaft dynamic runout data.

[0067] The specific methods for obtaining various types of data are as follows:

[0068] Bearing characteristic frequency data: Sensors are arranged in the vertical, horizontal and axial directions of the bearing housing to ensure that the sensors are in close contact with the bearing housing. If necessary, magnetic bases or bolts are used for fixing. The sampling frequency is set according to the bearing speed and the expected analysis frequency range to obtain the bearing vibration frequency data.

[0069] Bearing temperature data: Temperature is measured using an infrared thermal imager after the equipment has been running stably for a preset period of time, so that the bearing and pump housing temperatures reach a thermal equilibrium state, avoiding the influence of temperature instability during equipment startup on the measurement results; temperature data of the bearing housing and adjacent pump housing are obtained.

[0070] Shaft dynamic runout data: Displacement data of the shaft in a preset direction is obtained through a displacement sensor;

[0071] The data analysis and processing module is configured to send the obtained data to the data calculation unit for processing to obtain the characteristic anomaly coefficient, shaft temperature anomaly coefficient, and offset anomaly coefficient corresponding to each type of data; and to perform comprehensive processing on the three coefficients to obtain the bearing failure assessment coefficient.

[0072] The process of obtaining the characteristic anomaly coefficients includes:

[0073] Consult the bearing model manual to obtain the bearing structural parameters, including the number of rollers. Roller diameter Bearing pitch circle diameter Contact angle ;Calculate the failure frequency of the outer ring, the failure frequency of the inner ring, and the failure frequency of the rolling elements according to the bearing failure characteristic frequency calculation formula.

[0074] The number of rollers directly affects the calculation base for the fault characteristic frequencies of various bearing components;

[0075] For example, the formula for the failure frequency of the outer ring needs to be multiplied by the number of rollers to reflect the frequency of contact between the rollers and the outer ring raceway. If the number of rollers is incorrect, it will lead to a deviation in the calculation of the failure frequency, and thus misjudge the location of the failure (such as misjudging an inner ring failure as an outer ring failure).

[0076] The roller diameter and the bearing pitch circle diameter determine the motion radius and contact mechanical characteristics of the bearing rolling elements; the roller diameter affects the rotation frequency of the rolling elements, while the pitch circle diameter is related to the overall rotational dynamics of the bearing; when the roller diameter wears down, the failure frequency of the rolling elements will shift, and the degree of roller wear can be judged by comparing theoretical values ​​with measured values.

[0077] The contact angle reflects the proportion of radial and axial loads borne by the bearing and affects the angle correction term in the failure frequency calculation formula; abnormal contact angle (such as angle changes caused by installation deviation) will cause uneven distribution of axial force and accelerate the wear of the inner or outer ring.

[0078] The formulas for calculating the characteristic frequencies of bearing failures include:

[0079] Outer ring failure frequency:

[0080] Inner ring failure frequency:

[0081] Rolling element failure frequency:

[0082] Calculate the amplitude of the frequency bands corresponding to the outer ring fault frequency, inner ring fault frequency, and rolling element fault frequency, and calculate the difference between the frequency band amplitude and the corresponding preset reference value to obtain the outer ring fault difference, inner ring fault difference, and rolling fault difference.

[0083] Each corresponding fault difference value is preset with an allowable range. Fault differences that are not within the corresponding allowable range are recorded as outer ring fault difference value, inner ring fault difference value, and rolling fault difference value.

[0084] The ring difference value is obtained by weighted summation of the outer ring fault difference value, the inner ring fault difference value, and the rolling fault difference value.

[0085] The severity of the fault can be quantified by comparing the amplitude difference between the current operating state and the normal state; for example, if the difference in the outer ring fault exceeds the allowable range, it indicates that the outer ring fault has affected the vibration characteristics.

[0086] Baseline establishment: Vibration data is usually collected after the equipment is running for a new time or after a major overhaul, and used as a reference baseline for a fault-free state;

[0087] Preset weighting factors for outer ring fault difference value, inner ring fault difference value, and rolling fault difference value. Multiply the outer ring fault difference value, inner ring fault difference value, and rolling fault difference value with their corresponding weighting factors and sum them to obtain the ring difference value.

[0088] The weights of each bearing component are assigned according to their importance (e.g., the inner ring has a higher weight than the outer ring because the inner ring is in direct contact with the shaft), highlighting the impact of critical component failures on the overall bearing.

[0089] For example, if the weight of the fault difference value of the inner ring is set to 0.5, the weight of the outer ring to 0.3, and the weight of the rolling element to 0.2, when an abnormality occurs in the inner ring, the magnitude difference value will increase significantly, and a warning will be triggered first.

[0090] Determine the bearing's rotational frequency. In the spectrum diagram, the sideband spectral lines, spaced by the rotational frequency, exhibit a characteristic of symmetrical distribution around a central frequency, with intervals equal to the rotational frequency on both sides. Specifically, in the spectrum diagram, besides the central frequency peak, a series of equally spaced peaks appear on both sides, with the interval value equal to the rotational frequency. Determine the amplitude of the sideband spectral lines, and using the central frequency as the origin, obtain the amplitude of each sideband spectral line sequentially based on its distance from the origin. In the following content, the amplitude of the sideband spectral lines will be abbreviated as amplitude.

[0091] Explanation of the symmetrical distribution characteristics of the frequency shift sideband spectral lines:

[0092] Physical essence: When a bearing has faults such as misalignment or looseness, the vibration signal will produce a modulation effect, resulting in sidebands in the spectrum that are spaced by the revolution frequency;

[0093] Typical case: When the fit between the shaft and the bearing is loose, the rotational centrifugal force changes periodically, causing the vibration signal frequency to be frequency-modulated, forming sideband spectral lines;

[0094] If the amplitude of two adjacent sideband spectral lines closer to the origin is less than the amplitude farther from the origin, then the amplitude farther from the origin is marked as an abnormal amplitude; and the difference between the abnormal amplitude and the amplitude closer to the origin is calculated, and the absolute value is taken to obtain the abnormal amplitude difference value.

[0095] Under normal circumstances, the amplitude of the sideband spectral lines should decrease as they move away from the center frequency (the amplitude is greater closer to the origin); if an amplitude distribution of "smaller near and larger far" occurs, it indicates that the vibration signal has nonlinear characteristics (such as impact load), which may correspond to local defects in the bearing.

[0096] The larger the abnormal amplitude difference, the higher the impact intensity caused by the fault. For example, when a rolling element peels off, it will generate a violent impact, causing the amplitude difference to increase sharply.

[0097] Sort all abnormal amplitude values ​​in descending order of numerical value, extract the largest abnormal amplitude value, and the number of amplitude values ​​between the largest abnormal amplitude value and the abnormal amplitude values ​​on both sides of it. Take the reciprocal of the number of amplitude values ​​to obtain the frequency amplitude.

[0098] The characteristic anomaly coefficient is obtained by weighting the cyclic outlier with the frequency amplitude.

[0099] Preset weighting factors for cyclic outliers and frequency amplitudes, and then sum them up to obtain the feature anomaly coefficients after multiplying the cyclic outliers and frequency amplitudes with their corresponding weighting factors respectively.

[0100] The process of obtaining the shaft temperature anomaly coefficient includes:

[0101] At preset time intervals, the bearing temperature and the temperature of the adjacent pump casing are simultaneously acquired; the highest bearing temperature value and the highest pump casing temperature value are extracted from the bearing temperature and the temperature of the adjacent pump casing, respectively; and the positions corresponding to the highest bearing temperature value and the highest pump casing temperature value are recorded as the bearing high temperature point and the pump casing high temperature point, respectively.

[0102] This system enables dynamic monitoring of temperature data, capturing real-time temperature trends during equipment operation. Synchronous data collection at fixed time intervals (e.g., every 10 minutes) avoids misjudgments due to time asynchrony, ensuring data comparability and analytical validity.

[0103] For example, when a chemical centrifugal pump is running continuously, the heat generated by the bearings due to poor lubrication and wear may gradually accumulate. By collecting data at regular intervals, abnormal temperature fluctuations can be detected in time to prevent the fault from escalating.

[0104] Maximum bearing temperature and high temperature point: Internal bearing faults (such as increased friction between rollers and raceways, deterioration of grease) can lead to localized overheating. The maximum temperature value can directly reflect the degree of bearing abnormality; the high temperature point can pinpoint the specific location of the fault. For example, high temperature at the top of the bearing housing may correspond to insufficient lubrication in the upper part.

[0105] Pump casing maximum temperature and high temperature point: Pump casing temperature is affected by factors such as bearing heat conduction and medium flow. Its maximum temperature value and location can help determine the heat transfer path and the thermal state of the pump casing structure. For example, a high temperature on the side of the pump casing near the bearing may indicate abnormal heat conduction in the bearing;

[0106] The difference between the highest bearing temperature and the highest pump casing temperature is calculated, and the absolute value is taken to obtain the shaft casing temperature difference value; the shaft casing temperature difference value corresponding to each time interval is obtained in sequence, and the average value of each shaft casing temperature difference value is calculated to obtain the average shaft casing temperature difference value;

[0107] The temperature difference between the bearing and the pump casing reflects the thermal difference between them. Under normal circumstances, the temperature difference is within a stable range. If the temperature difference suddenly increases, it may indicate a bearing failure (such as wear or misalignment leading to frictional heat generation) or abnormal heat dissipation from the pump casing.

[0108] The average temperature difference between the shaft and housing serves as a benchmark for long-term temperature differences and is used for subsequent anomaly detection. For example, by continuously monitoring for a week and calculating the average daily temperature difference, the long-term trend of temperature differences can be identified.

[0109] The allowable range of the average shaft housing temperature difference is preset, and the shaft housing temperature difference value that is not within the allowable range of the average shaft housing temperature difference is marked as a temperature difference abnormal value;

[0110] The preset allowable range for the average temperature difference (e.g., ±2℃) marks temperature difference values ​​that exceed this range as abnormal values, quickly filtering out temperature data that deviates significantly from the normal state and narrowing down the scope of troubleshooting.

[0111] Sort all temperature difference constants in descending order of numerical value, and extract the two largest temperature difference constants and their corresponding two sets of bearing high temperature points and pump casing high temperature points.

[0112] After sorting the constant temperature difference values ​​in descending order, the two largest values ​​are extracted to focus on the most severe temperature anomaly events; the corresponding high-temperature points of the bearings and pump casings can locate the key areas of thermal anomalies in the equipment, providing accurate clues for fault diagnosis;

[0113] Connect the high-temperature point of the bearing and the high-temperature point of the pump casing with a straight line, and calculate the length of the straight line to obtain the temperature change line length; then obtain the temperature change line lengths corresponding to the two sets of high-temperature points of the bearing and the pump casing in sequence.

[0114] The length of the straight line connecting the bearing and the high-temperature point of the pump casing quantifies the distance of the heat conduction path between the two points. The longer the line, the more complex the heat transfer path, which may indicate heat dissipation obstacles or decreased heat conduction efficiency.

[0115] The two obtained temperature change line lengths are used as the major and minor semi-axis of the ellipse, respectively. An ellipse model is constructed, and the area of ​​the ellipse model is calculated and denoted as the axial temperature anomaly coefficient.

[0116] The process of obtaining the offset anomaly coefficients includes:

[0117] After the equipment is running stably, start the data acquisition instrument to acquire the displacement data of the bearing in the X and Y directions in real time;

[0118] Displacement data during stable equipment operation can reflect the shaft runout characteristics under normal working conditions, avoiding interference from transient fluctuations during startup and shutdown. Two-dimensional displacement acquisition in the X and Y directions can comprehensively capture the shaft's offset trajectory in the plane, providing a foundation for subsequent spatial analysis;

[0119] The displacements in the X direction are sorted in descending order of their numerical values, and the two largest X-direction displacements are extracted. A mark is made on the bearing, and a straight line is drawn from the center of the bearing to the mark to obtain a baseline. The baselines after the two largest X-direction displacements of the bearing and the corresponding angle changes between them are obtained to obtain two angle changes in the X-direction displacement, which are used as the original first X-angle value and the original second X-angle value, respectively.

[0120] Abnormal shaft runout typically manifests as large-amplitude deviations. Extracting the two largest displacement values ​​allows us to focus on the most significant deviation event. By analyzing extreme deviation cases, we can more sensitively identify potential faults (such as shaft misalignment, bearing wear, etc.) and avoid interference from small-amplitude fluctuations.

[0121] Angle changes can help determine the directionality of the offset, and when combined with the amplitude, they can more accurately locate the fault type (such as radial offset, axial tilt, etc.).

[0122] Similarly, following the above process of analyzing the bearing displacement data in the X direction, the bearing displacement data in the Y direction is analyzed to obtain the first Y angle value and the second Y angle value.

[0123] Specifically: Sort the displacements in the Y direction in descending order of their numerical values, and extract the two largest Y-direction displacements; mark a point on the bearing, and connect the bearing center with the mark point with a straight line to obtain a baseline; obtain the baselines after the two largest Y-direction displacements of the bearing, and the corresponding angle changes between the baselines, to obtain two angle changes in the Y-direction displacement, which are respectively used as the first Y-angle value and the second Y-angle value.

[0124] The original Y angle value and the original Y angle value corresponding to the displacement of the bearing in the Y direction when obtaining the original X angle value and the original X angle value;

[0125] When obtaining the Y angle value of square one and the Y angle value of square two, the corresponding X angle values ​​of square one and the X angle values ​​of square two are the bearing displacement in the X direction.

[0126] A total of four displacement changes of the bearing were obtained, and the positions of the four displacement changes corresponding to the marked points in three-dimensional space were obtained to obtain four spatial marked points;

[0127] The four spatial points represent the positions of the axis under different offset states, covering the direction and magnitude of the maximum offset;

[0128] The area of ​​the largest enclosed figure can quantify the "spatial range" of axis offset. The larger the area, the higher the randomness or abnormality of the axis jump.

[0129] By analogy: if the shaft is operating normally, the offset trajectory should be close to a regular shape (such as an ellipse) with a small area; if there is a fault, the offset trajectory may diverge and the area will increase significantly.

[0130] Four spatial markers are simultaneously marked on a plane, and the four spatial markers are connected by straight lines to obtain a closed figure, where the closed figure is the largest area closed figure that can be formed by the four spatial markers; calculate the area of ​​the closed figure and denot it as the offset anomaly coefficient.

[0131] After normalizing the characteristic anomaly coefficient, shaft temperature anomaly coefficient, and offset anomaly coefficient, the characteristic anomaly coefficient and shaft temperature anomaly coefficient are respectively used as two legs of a right triangle. The remaining leg is connected to form a complete right triangle. The offset anomaly coefficient is used as the height of the right triangle to construct a triangular pyramid. The volume of the triangular pyramid is calculated and denoted as the fault assessment coefficient.

[0132] The assessment and diagnosis module is configured to match the corresponding fault level based on the bearing's fault coefficient;

[0133] Three threshold ranges are preset, and each threshold range corresponds to a fault level. The bearing fault assessment coefficient is matched with the three threshold ranges to obtain the fault level corresponding to the bearing fault assessment coefficient, including minor fault, moderate fault and severe fault.

[0134] The optimized scheduling module is configured to perform corresponding optimized scheduling processing based on the bearing's fault level;

[0135] In case of minor faults: maintain continuous operation of equipment while controlling the development of the fault and reducing energy consumption; reduce pump speed through variable frequency speed regulation to reduce radial load on the bearing; increase the sampling frequency of bearing vibration, temperature and displacement; when the daily increase of the characteristic abnormal coefficient or shaft temperature abnormal coefficient exceeds the preset standard, trigger a workshop-level early warning to prompt technicians to conduct on-site inspection.

[0136] In the case of a moderate failure: prioritize ensuring production continuity by switching equipment or reducing load to prevent the failure from escalating to a severe failure; start the standby centrifugal pump (confirm that the standby pump is in good condition beforehand) and achieve a "soft switch" through the PLC control system; gradually open the standby pump outlet valve while reducing the main pump flow rate to maintain stable system pressure. Once the standby pump is running stably (vibration ≤ 2.5 mm / s, temperature ≤ 70℃), shut down the main pump to complete the switch; arrange for a vibration analyst to conduct offline spectrum analysis of the bearing with a portable spectrum analyzer to identify the faulty component;

[0137] In case of severe failure: immediately stop the operation of the faulty pump, initiate the emergency shutdown procedure to prevent equipment damage or safety accidents; lock and tagged the stopped pump, release the residual pressure inside the pump (if the pressure drops below 0.1MPa), and check the concentration of flammable gases in the surrounding area; arrange for maintenance personnel to repair the centrifugal pump.

[0138] Example 2

[0139] Please see Figure 2 A method for optimizing the operation and scheduling of chemical centrifugal pumps includes the following steps:

[0140] Multi-dimensional data acquisition: Comprehensive collection of bearing characteristic frequency, temperature, shaft dynamic runout and other information, and preprocessing such as noise reduction and filtering to establish a benchmark database;

[0141] Anomaly coefficient calculation: Structural parameters are obtained based on the bearing model manual, fault characteristic frequencies are calculated, measured amplitudes are compared with reference values, and characteristic anomaly coefficients are obtained by combining rotational frequency sideband spectrum analysis; shaft temperature anomaly coefficients are calculated by the shaft housing temperature difference and the location of high temperature points; a spatial model is constructed based on the maximum displacement and angle changes in the X and Y directions of the shaft to obtain the offset anomaly coefficients;

[0142] Fault level assessment: The three coefficients of abnormal characteristics, abnormal shaft temperature, and abnormal offset are normalized to obtain the fault assessment coefficient, which is then compared with the preset threshold to classify the fault level as minor, moderate, or severe.

[0143] Tiered scheduling execution: corresponding measures are taken for different fault levels. For minor faults, speed is reduced by frequency conversion speed regulation, data acquisition frequency is increased and early warning is set; for moderate faults, the backup pump is started for soft switching and offline spectrum analysis is arranged to confirm the faulty component; for severe faults, the machine is shut down immediately, and safety operations such as tagging and locking, pressure release, etc. are performed and maintenance is arranged.

[0144] Closed-loop optimization iteration: Record the effect data of each fault assessment and scheduling measure, analyze and optimize the threshold and weighting factor of each coefficient calculation, formulate preventive maintenance strategies based on historical fault data, dynamically adjust operating parameters, and achieve continuous optimization of energy saving and fault prevention.

[0145] The above formulas are derived from software simulations using a large amount of data and are selected to be close to the actual values. The influence weight factors and specific coefficient values ​​in the formulas are set by those skilled in the art based on the actual situation and can be adjusted and modified in the future.

[0146] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0147] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.).

[0148] The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium. A semiconductor medium can be a solid-state drive (SSD).

[0149] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0150] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatus and methods can be implemented in other ways.

[0151] For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be indirect couplings or communication connections between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0152] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0153] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0154] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0155] The aforementioned storage media include various media that can store program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0156] The above description of the embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A chemical centrifugal pump operation scheduling optimization system, characterized in that, include: The data collection module is configured to acquire relevant data information of the centrifugal pump bearing, and to preprocess and classify the acquired data information to obtain bearing characteristic frequency data, bearing temperature data, and shaft dynamic runout data. The data analysis and processing module is configured to send the obtained data to the data calculation unit for processing to obtain the characteristic anomaly coefficient, shaft temperature anomaly coefficient, and offset anomaly coefficient corresponding to each type of data; and to perform comprehensive processing on the three coefficients to obtain the bearing failure assessment coefficient. Among them, the amplitude difference value corresponding to the failure frequency of each component of the bearing is calculated and weighted to obtain the anomaly value. Combined with the frequency amplitude obtained by sideband spectral line analysis, the characteristic anomaly coefficient is obtained after weighted calculation. By obtaining the highest temperature and location of the bearing and pump housing, the temperature difference between the bearing and housing is calculated and abnormal values ​​are marked. The length of the line connecting the two highest abnormal values ​​is taken as the semi-axis of the ellipse, and the area of ​​the ellipse is calculated to obtain the shaft temperature abnormality coefficient. By acquiring the bearing's X and Y displacement data, analyzing the change in the baseline angle corresponding to the maximum displacement, four spatial marker points are obtained. After connecting them into a closed figure, the area is calculated to obtain the offset anomaly coefficient. After normalizing the characteristic, shaft temperature, and offset anomaly coefficients, a right triangle is constructed with the characteristic and shaft temperature anomaly coefficients as the right angle sides, and a triangular pyramid is constructed with the offset anomaly coefficient as the height. The volume of this pyramid is calculated to obtain the bearing failure assessment coefficient. The assessment and diagnosis module is configured to match the corresponding fault level based on the bearing's fault coefficient; The optimized scheduling module is configured to perform corresponding optimized scheduling processing based on the bearing's fault level.

2. The chemical centrifugal pump operation scheduling optimization system according to claim 1, characterized in that, In the data collection module, the specific methods for acquiring various types of data are as follows: Bearing characteristic frequency data: Sensors are arranged in the vertical, horizontal and axial directions of the bearing housing; the sampling frequency is set according to the bearing speed and the expected analysis frequency range to obtain the bearing vibration frequency data; Bearing temperature data: Temperature data of the bearing housing and adjacent pump casing are obtained by measuring the temperature using an infrared thermal imager. Axis dynamic runout data: Displacement data of the shaft in a preset direction is obtained through displacement sensors.

3. The chemical centrifugal pump operation scheduling optimization system according to claim 2, characterized in that, The process of obtaining the characteristic anomaly coefficients includes: Consult the bearing model manual to obtain the bearing structural parameters, including the number of rollers, roller diameter, bearing pitch circle diameter, and contact angle; calculate the outer ring failure frequency, inner ring failure frequency, rolling element failure frequency, and amplitude of their corresponding frequency bands according to the bearing failure characteristic frequency calculation formula; and calculate the difference between the frequency band amplitude and the corresponding preset reference value to obtain the outer ring failure difference, inner ring failure difference, and rolling element failure difference. Each corresponding fault difference value is preset with an allowable range. Fault differences that are not within the corresponding allowable range are recorded as outer ring fault difference value, inner ring fault difference value, and rolling fault difference value. The ring fault difference value is obtained by weighted summation of the outer ring fault difference value, inner ring fault difference value, and rolling fault difference value.

4. The chemical centrifugal pump operation scheduling optimization system according to claim 3, characterized in that, Also includes: Determine the bearing's rotational frequency, determine the amplitude of the sideband spectral lines, and obtain the amplitude of each sideband spectral line sequentially based on the distance from the origin, with the center frequency as the origin. If the amplitude of two adjacent sideband spectral lines closer to the origin is less than the amplitude farther from the origin, then the amplitude farther from the origin is marked as an abnormal amplitude; and the difference between the abnormal amplitude and the amplitude closer to the origin is calculated, and the absolute value is taken to obtain the abnormal amplitude difference value. Sort all abnormal amplitude values ​​in descending order of numerical value, extract the largest abnormal amplitude value, and the number of amplitude values ​​between the largest abnormal amplitude value and the abnormal amplitude values ​​on both sides of it. Take the reciprocal of the number of amplitude values ​​to obtain the frequency amplitude. The characteristic anomaly coefficient is obtained by weighting the cyclic outlier and the frequency amplitude.

5. The chemical centrifugal pump operation scheduling optimization system according to claim 4, characterized in that, The process of obtaining the shaft temperature anomaly coefficient includes: At preset time intervals, the bearing temperature and the temperature of the adjacent pump casing are simultaneously acquired; the highest bearing temperature value and the highest pump casing temperature value are extracted from the bearing temperature and the temperature of the adjacent pump casing, respectively; and the positions corresponding to the highest bearing temperature value and the highest pump casing temperature value are recorded as the bearing high temperature point and the pump casing high temperature point, respectively. The difference between the highest bearing temperature and the highest pump casing temperature is calculated, and the absolute value is taken to obtain the shaft casing temperature difference value; the shaft casing temperature difference value corresponding to each time interval is obtained in sequence, and the average value of each shaft casing temperature difference value is calculated to obtain the average shaft casing temperature difference value; The allowable range of the average shaft housing temperature difference is preset, and the shaft housing temperature difference value that is not within the allowable range of the average shaft housing temperature difference is marked as a temperature difference abnormal value; Sort all temperature difference constants in descending order of numerical value, and extract the two largest temperature difference constants and their corresponding two sets of bearing high temperature points and pump casing high temperature points. Connect the high-temperature point of the bearing and the high-temperature point of the pump casing with a straight line, and calculate the length of the straight line to obtain the temperature change line length; then obtain the temperature change line lengths corresponding to the two sets of high-temperature points of the bearing and the pump casing in sequence. The two obtained temperature variation line lengths are used as the major and minor semi-axes of the ellipse, respectively. An ellipse model is constructed, and the area of ​​the ellipse model is calculated and denoted as the axial temperature anomaly coefficient.

6. The chemical centrifugal pump operation scheduling optimization system according to claim 5, characterized in that, The process of obtaining the offset anomaly coefficients includes: After the equipment is running stably, start the data acquisition instrument to acquire the displacement data of the bearing in the X and Y directions in real time; The displacements in the X direction are sorted in descending order of their numerical values, and the two largest X-direction displacements are extracted. A mark is made on the bearing, and a straight line is drawn from the center of the bearing to the mark to obtain a baseline. The baselines after the two largest X-direction displacements of the bearing and the corresponding angle changes between them are obtained to obtain two angle changes in the X-direction displacement, which are used as the original first X-angle value and the original second X-angle value, respectively. Similarly, following the above process of analyzing the bearing displacement data in the X direction, the bearing displacement data in the Y direction is analyzed to obtain the first Y angle value and the second Y angle value. The original Y angle value and the original Y angle value corresponding to the displacement of the bearing in the Y direction when obtaining the original X angle value and the original X angle value; When obtaining the Y angle value of square one and the Y angle value of square two, the corresponding X angle values ​​of square one and the X angle values ​​of square two are the bearing displacement in the X direction. A total of four displacement changes of the bearing were obtained, and the positions of the four displacement changes corresponding to the marked points in three-dimensional space were obtained to obtain four spatial marked points; Four spatial markers are simultaneously marked on a plane, and the four spatial markers are connected by straight lines to obtain a closed figure. The area of ​​the closed figure is calculated and denoted as the offset anomaly coefficient.

7. The chemical centrifugal pump operation scheduling optimization system according to claim 6, characterized in that, After normalizing the characteristic anomaly coefficient, shaft temperature anomaly coefficient, and offset anomaly coefficient, the characteristic anomaly coefficient and shaft temperature anomaly coefficient are respectively used as two legs of a right triangle. The remaining leg is connected to form a complete right triangle. The offset anomaly coefficient is used as the height of the right triangle to construct a triangular pyramid. The volume of the triangular pyramid is calculated and denoted as the fault assessment coefficient. Three threshold ranges are preset, and each threshold range corresponds to a fault level. The bearing fault assessment coefficient is matched with the three threshold ranges to obtain the fault level corresponding to the bearing fault assessment coefficient, including minor fault, moderate fault and severe fault.

8. The chemical centrifugal pump operation scheduling optimization system according to claim 7, characterized in that, In case of minor malfunctions: maintain continuous operation of the equipment while controlling the development of the malfunction and reducing energy consumption; In the case of a moderate failure: prioritize ensuring production continuity by switching equipment or reducing load to prevent the failure from escalating to a severe failure; In case of severe failure: immediately stop the operation of the faulty pump and initiate the emergency shutdown procedure to prevent equipment damage or safety accidents.

9. A method for optimizing the operation scheduling of a chemical centrifugal pump, and a system for optimizing the operation scheduling of a chemical centrifugal pump according to any one of claims 1-8, characterized in that, Includes the following steps: Multi-dimensional data acquisition: Collect bearing-related information, perform noise reduction and filtering preprocessing, and establish a benchmark database; Anomaly coefficient calculation: The corresponding anomaly coefficient is calculated based on the collected bearing-related information; Fault level assessment: The three abnormal coefficients are normalized to obtain fault assessment coefficients, and then matched with fault levels. And take corresponding measures for different fault levels.

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