A centrifuge data remote monitoring method and system based on a cloud platform

Abstract

The present application relates to the technical field of centrifuge operation monitoring, in particular to a centrifuge data remote monitoring method and system based on a cloud platform. The method comprises: obtaining the vibration intensity, noise decibel value, temperature, actual speed of the screw, liquid flow at the separation liquid discharge outlet, and humidity of the material liquid in the cylindrical area and conical area of the centrifuge during the operation of the centrifuge; evaluating the centrifugal vibration intensity according to the vibration intensity and noise decibel value at each time; obtaining the abnormal factor at each time according to the distribution characteristics of the centrifugal vibration intensity in the local time period; correcting the abnormal factor by using the temperature and temperature change characteristics in the local time period; obtaining the separation abnormal score by combining the actual speed of the screw at each time, the liquid flow, the humidity difference of the material liquid in the cylindrical area and conical area of the drum, and the corrected abnormal factor; and determining whether to give a warning for the centrifuge abnormality according to the separation abnormal score. The present application improves the accuracy of the centrifuge monitoring result.

Description

Technical Field

[0001] This invention relates to the field of centrifuge operation monitoring technology, specifically to a method and system for remote monitoring of centrifuge data based on a cloud platform. Background Technology

[0002] Horizontal screw discharge centrifuges are continuous, automated solid-liquid separation devices. Their core function is to achieve efficient separation of solid particles from the liquid phase in a suspension through centrifugal force, ultimately obtaining a low-moisture solid filter cake and a clear liquid phase. They are widely used in chemical, food, and pharmaceutical industries, replacing some drying processes and reducing solid dehydration costs. The conveying screw is a core component of the centrifuge, connecting to the rotating shaft to drive the solid material within the drum at a set speed. However, due to the long-term high-intensity operation of the conveying screw, frictional wear may occur between the screw and the rotating shaft. Over time, this can lead to abnormal operating conditions such as screw lag or reduced rotation speed during high-speed separation operations, reducing the centrifuge's lifespan. Therefore, cloud platforms are commonly used for remote monitoring of centrifuge abnormalities.

[0003] Traditional cloud platforms often monitor abnormal operating conditions of centrifuges caused by wear and tear on the centrifuge screw components by collecting real-time vibration data of the screw. However, abnormal wear between the screw and shaft reduces rotational stability and increases vibration intensity, leading to the traditional reliance on screw vibration intensity for anomaly assessment. In actual centrifuge solid-liquid separation scenarios, the complexity of the separation tasks varies. If the solid content is high, the impact on the screw blades is greater, increasing the interference with vibration analysis. This reduces the accuracy of traditional anomaly monitoring based solely on vibration performance. Summary of the Invention

[0004] To address the issue of low accuracy in existing methods for monitoring centrifuge anomalies, this invention aims to provide a cloud-based method and system for remote monitoring of centrifuge data. The specific technical solution adopted is as follows:

[0005] In a first aspect, the present invention provides a method for remote monitoring of centrifuge data based on a cloud platform, the method comprising the following steps:

[0006] The centrifuge's vibration intensity, noise level (decibels), temperature, actual screw speed, liquid flow rate at the separation liquid outlet, and humidity of the liquid in the cylindrical and conical zones of the drum are obtained during operation.

[0007] The centrifugal vibration intensity at each moment is evaluated based on the vibration intensity and noise decibel value at each moment; the anomalous factor at each moment is obtained based on the distribution characteristics of the centrifugal vibration intensity within the local time period at each moment; the anomalous factor is corrected by using the temperature at each moment and the temperature change characteristics within the local time period at each moment to obtain the corrected anomalous factor.

[0008] By combining the actual screw speed at each moment, the liquid flow rate, the difference in humidity between the liquid in the cylindrical and conical zones of the drum, and the corrected anomalous factor, a separation anomaly score is obtained at each moment.

[0009] Based on the separation anomaly score, determine whether to issue an early warning for centrifuge malfunctions.

[0010] Preferably, the step of evaluating the centrifugal vibration intensity at each moment based on the vibration intensity and noise decibel value at each moment includes:

[0011] For any given moment:

[0012] The normalized result between the difference between the vibration intensity at any given moment and the reference vibration intensity is determined as the mechanical amplitude factor at any given moment.

[0013] Calculate the first ratio between the noise decibel value at any given time and the reference noise decibel value;

[0014] By combining the mechanical amplitude factor and the first ratio, the centrifugal vibration intensity at any given moment is obtained.

[0015] Preferably, obtaining the centrifugal vibration intensity at any given moment by combining the mechanical amplitude factor and the first ratio includes:

[0016] The product of the mechanical amplitude factor and the first ratio is taken as the centrifugal vibration intensity at any given moment.

[0017] Preferably, obtaining the anomalous factor for each moment based on the distribution characteristics of centrifugal vibration intensity within a local time period includes:

[0018] For any given moment:

[0019] The centrifugal vibration intensity at all times within a local time period at any given time is curve-fitted to obtain the peak point in the fitted curve.

[0020] Calculate the first difference between the centrifugal vibration intensity at each peak point and the mean of the centrifugal vibration intensity at all times within a local time interval at any given time; based on the time interval between each peak point and any given time and the corresponding first difference, obtain the regularity of the vibration period at any given time.

[0021] By combining the centrifugal vibration intensity and the regularity of the vibration period at any given moment, the anomalous factor at any given moment is obtained;

[0022] The local time period of any given moment is a time period consisting of a preset duration with any given moment as the last moment.

[0023] Preferably, obtaining the regularity of the vibration period at any given time based on the time interval between each peak point and the corresponding first difference includes:

[0024] The negative correlation mapping value between each peak point and the time interval of any given moment is used as the time influence weight corresponding to each peak point. The negative correlation mapping values ​​of the first difference corresponding to each peak point are weighted, summed, and normalized to obtain the regularity of the vibration period at any given moment.

[0025] Preferably, the step of using the temperature at each moment and the temperature change characteristics within a local time period at each moment to correct the anomalous factor and obtain the corrected anomalous factor includes:

[0026] For any given moment:

[0027] Perform a linear fit on the temperature within a local time interval at any given moment to obtain the slope of the fitted line; calculate the first product of the slope and the temperature at any given moment.

[0028] The anomalous factor at any given time is corrected using the first product to obtain the corrected anomalous factor at any given time.

[0029] Preferably, the separation anomaly score at each moment is obtained by combining the actual screw speed at each time point, the liquid flow rate, the difference in moisture content between the cylindrical and conical zones of the drum, and the corrected anomaly factor, including:

[0030] For any given moment:

[0031] The coaxial rotation tightness at any given moment is evaluated based on the difference between the actual rotation speed and the theoretical rotation speed of the screw at any given moment.

[0032] The level of concern regarding liquid discharge at any given time is evaluated based on the difference between the liquid flow rate at any given time and the reference liquid flow rate.

[0033] Based on the difference in humidity between the liquid in the cylindrical zone and the conical zone of the drum at any given time, the coordination degree of separation efficiency between the liquid in the cylindrical and conical zones of the drum at any given time is evaluated.

[0034] By combining the coaxial rotation tightness, the concern about liquid discharge, and the coordination of separation efficiency, the degree of separation anomaly at any given moment is obtained;

[0035] The separation anomaly score at any given time point is determined by combining the separation anomaly performance level and the corrected anomaly factor.

[0036] Preferably, the step of obtaining the separation anomaly performance at any given time by comprehensively considering the coaxial rotation tightness, the feed liquid discharge concern, and the separation efficiency coordination includes:

[0037] Based on the coaxial rotation tightness, the concern about liquid discharge, and the separation efficiency coordination at any given moment, the separation anomaly performance at any given moment is obtained. The coaxial rotation tightness and the concern about liquid discharge are both positively correlated with the separation anomaly performance, while the separation efficiency coordination is negatively correlated with the separation anomaly performance.

[0038] Preferably, the step of determining whether to issue an early warning for centrifuge malfunction based on the separation anomaly score includes:

[0039] Calculate the second difference between the current separation anomaly score and the mean of the separation anomaly scores at historical normal times; use the normalized result of the second difference as the cloud monitoring early warning indicator at the current time.

[0040] If the cloud monitoring early warning indicator is greater than the preset early warning threshold, an early warning will be issued; if the cloud monitoring early warning indicator is less than or equal to the preset early warning threshold, no early warning will be issued.

[0041] Secondly, the present invention provides a cloud platform-based remote monitoring system for centrifuge data, which is used to implement the first aspect, and the system includes:

[0042] The data acquisition module is used to acquire the centrifuge's vibration intensity, noise level, temperature, actual screw speed, liquid flow rate at the separation liquid outlet, and humidity of the liquid in the cylindrical and conical zones of the drum during centrifuge operation.

[0043] The anomalous factor determination module is used to evaluate the centrifugal vibration intensity at each moment based on the vibration intensity and noise decibel value at each moment; obtain the anomalous factor at each moment based on the distribution characteristics of the centrifugal vibration intensity within a local time period at each moment; and correct the anomalous factor by using the temperature at each moment and the temperature change characteristics within a local time period at each moment to obtain the corrected anomalous factor.

[0044] The anomaly scoring module is used to obtain the separation anomaly score at each moment by combining the actual rotation speed of the screw, the liquid flow rate, the difference in humidity of the liquid in the cylindrical and conical areas of the drum, and the corrected anomaly factor.

[0045] The early warning module is used to determine whether to issue an early warning for centrifuge malfunctions based on the separation anomaly score.

[0046] The present invention has at least the following beneficial effects:

[0047] This invention evaluates the centrifugal vibration intensity by observing the vibration and noise performance of the conveying screw during centrifuge operation. It also provides a preliminary assessment of operational anomalies by analyzing the distribution characteristics of centrifugal vibration intensity over a localized time period at a single moment during operation. Furthermore, it dynamically corrects for anomalies by incorporating temperature and its changing trends. This approach more accurately reflects abnormal conditions in actual operation and improves the sensitivity for identifying potential faults. Considering that screw rotation lag leads to a larger speed difference between the screw and the drum, which affects the separation and drying of solid materials in the cylindrical and conical regions of the drum, the significant difference in rotational speed between the drum and the screw results in varying degrees of drying of the solid phase in the cylindrical region compared to the conical region. The dryness of the solid phase in the region varies considerably. Therefore, by comprehensively analyzing the actual screw speed, liquid flow rate, and the difference in material liquid humidity between the cylindrical and conical zones of the drum, and combining this with a corrected anomaly factor, a separation anomaly score was obtained. This invention evaluates the centrifuge's operating status by integrating multiple parameter indicators, enabling a more comprehensive capture of centrifuge anomaly characteristics. This avoids the misjudgment or omission caused by traditional methods relying solely on single vibration data. Furthermore, it uses the separation anomaly score to make early warning judgments, achieving intelligent assessment and early warning of the centrifuge's operating status. This improves the accuracy and reliability of the cloud platform's remote monitoring of the centrifuge, helps to promptly detect equipment anomalies and take intervention measures, and ensures the safe and stable operation of the equipment. Attached Figure Description

[0048] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 A flowchart illustrating a cloud-based remote monitoring method for centrifuge data provided in an embodiment of the present invention;

[0050] Figure 2 This is a structural block diagram of a cloud-based centrifuge data remote monitoring system provided in an embodiment of the present invention. Detailed Implementation

[0051] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, describes a cloud-based remote monitoring method and system for centrifuge data.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0053] The following description, in conjunction with the accompanying drawings, details a specific solution for a cloud-based remote monitoring method and system for centrifuge data provided by this invention.

[0054] An embodiment of a cloud-based method for remote monitoring of centrifuge data:

[0055] The specific scenario addressed in this embodiment is as follows: During operation of a horizontal screw discharge centrifuge, the coaxial drum and rotating screw are driven by motors. When the suspension enters the drum through the feed pipe, the highly rotating drum applies a large centrifugal force to the liquid. Due to the density difference between solids and liquids, the denser solids are rapidly adsorbed onto the inner wall of the drum under centrifugal force, forming a solid ring layer. The relatively lighter liquid is then flung through the spiral blades and discharged into the overflow plate at the rear. Simultaneously, the solids are propelled by the spiral blades to the solid discharge outlet at the front, achieving solid-liquid separation. Due to the long-term execution of high-intensity separation tasks, there may be significant frictional losses between the screw and bearings, which could lead to rotational lag between them in severe cases. This embodiment combines the actual operating parameters and real-time solid-liquid separation conditions to obtain more accurate and precise remote monitoring and early warning results.

[0056] This embodiment proposes a method for remote monitoring of centrifuge data based on a cloud platform, such as... Figure 1 As shown, a method for remote monitoring of centrifuge data based on a cloud platform in this embodiment includes the following steps:

[0057] Step S1: Obtain the vibration intensity, noise level (decibels), temperature, actual screw speed, liquid flow rate at the separation liquid outlet, and humidity of the liquid in the cylindrical and conical zones of the centrifuge during centrifuge operation.

[0058] During centrifuge operation, the vibration intensity and noise level of the centrifuge are monitored by a vibration sensor module; the temperature of the centrifuge is monitored by a temperature sensor module; the actual screw speed is monitored by a speed monitoring module, and the real-time theoretical speed is read via motor input commands; the liquid flow rate at the discharge port of the separated liquid is monitored by a flow monitoring module; and the humidity data of the liquid in the cylindrical and conical zones of the drum are read by a humidity sensor module. In this embodiment, the data reading frequency is once per second, but in specific applications, the implementer can set it according to specific circumstances. All read data undergoes data cleaning and preprocessing, which is existing technology and will not be elaborated further here. It should be noted that the vibration intensity, noise level, temperature, actual speed, liquid flow rate, and humidity mentioned later are all preprocessed data.

[0059] Furthermore, the preprocessed data is uploaded to the data acquisition system for subsequent analysis and use.

[0060] Step S2: Evaluate the centrifugal vibration intensity at each moment based on the vibration intensity and noise decibel value at each moment; obtain the anomalous factor at each moment based on the distribution characteristics of the centrifugal vibration intensity within the local time period at each moment; and correct the anomalous factor by using the temperature at each moment and the temperature change characteristics within the local time period at each moment to obtain the corrected anomalous factor.

[0061] The purpose of this embodiment is to obtain remote monitoring and early warning indicators for the centrifuge via a cloud platform by analyzing the centrifuge's operating parameters and solid-liquid separation conditions. Considering that abnormal wear between the centrifuge screw and bearings will reduce the tightness of the hardware connection between them, leading to strong vibrations and abnormal noises during centrifuge operation, the following analysis first evaluates the centrifuge vibration intensity at various times based on mechanical vibration and noise decibel effects.

[0062] The varying difficulty of the tasks performed by centrifuges in real time leads to differences in the intensity of interference in the analysis of vibration performance. For example, when the solid phase accounts for a large proportion of the solid phase in the solid-liquid separation liquid, it will cause a significant impact on the centrifuge, resulting in errors in the vibration analysis. Therefore, it is necessary to combine the abnormal vibration characteristics of the equipment itself for auxiliary matching analysis. The abnormal vibration of the equipment between the screw and the bearing should exhibit a periodic pattern. Therefore, this step combines the local performance of the centrifugal vibration intensity in the short term to preliminarily evaluate the anomalous factors of the equipment.

[0063] Severe wear between the screw and bearing reduces the tightness of their contact during centrifuge operation. When the centrifuge performs the solid-liquid separation and drying process, the bearing's driving force on the screw is lower than normal, resulting in stronger vibrations during screw and bearing rotation. Wear between the bearing and screw leads to abnormally high decibel levels of noise; for example, each time the rotation reaches the worn area, a harsh, high-decibel noise is generated between the bearing and screw. While there is some audio signal from the contact between the centrifuge's spiral blades and the liquid, this signal is generally much lower than the decibel levels observed during hardware wear.

[0064] Based on the above characteristics, the following steps are to first evaluate the abnormality of the centrifuge by observing its vibration intensity and periodicity, and then obtain the abnormality factor. The abnormality factor is then corrected by observing the temperature change characteristics to obtain a more accurate abnormality factor.

[0065] This example illustrates the process using one specific moment as an example; the method provided in this embodiment can be used for processing at other moments as well.

[0066] Specifically, for any given moment:

[0067] The normalized result of the difference between the vibration intensity at that moment and the reference vibration intensity is determined as the mechanical amplitude factor at that moment; the ratio between the noise decibel value at that moment and the reference noise decibel value is calculated, and this ratio is recorded as the first ratio. The product of the mechanical amplitude factor and the first ratio is taken as the centrifugal vibration intensity at that moment.

[0068] As a specific example, the reference noise decibel can be obtained by statistically analyzing the distribution characteristics of the centrifuge noise decibel values ​​during a historical period that has been confirmed as the centrifuge operating normally (without abnormalities). The duration of the historical period can be 5 minutes. The maximum value of the centrifuge noise decibel value at all times within the historical period is obtained, and this maximum value is used as the reference noise decibel.

[0069] As a concrete example, the specific formula for calculating the centrifugal vibration intensity is given. The centrifugal vibration intensity at this moment can be expressed as:

[0070]

[0071] in, This indicates the intensity of the centrifugal vibration at that moment. This indicates the noise level in decibels at that moment. Indicates the reference noise level in decibels. This indicates the vibration intensity at that moment. Indicates the reference vibration intensity. This represents the normalization function.

[0072] This represents the mechanical amplitude factor at that moment. This represents the first ratio. The larger the difference between the noise decibel value monitored at this moment and the reference noise decibel value, the higher the real-time noise decibel level, and the greater the possibility that the centrifuge is in an abnormal screw wear state at that moment. The larger the ratio between the noise decibel value at this moment and the reference noise decibel value, the higher the real-time monitored vibration intensity level of the centrifuge, and the stronger the centrifuge's malfunction. When the mechanical amplitude factor is larger and the first ratio is also larger, it indicates that the centrifuge is more likely to be in an abnormal screw wear state, that is, the centrifuge vibration intensity is greater.

[0073] Considering that the solid content of the liquid to be separated may be relatively high during the centrifugation process, the interference error of the loudness and vibration analysis when the screw and the liquid come into contact is relatively large. Therefore, it is necessary to combine the abnormal vibration characteristics of the screw wear itself for auxiliary analysis. The abnormal vibration of the centrifuge is mainly manifested when the screw and the bearing are in the same position. During the rotation of the screw, the centrifugal vibration intensity of the centrifuge should show a certain periodicity. The higher the degree of conformity of the periodicity, the stronger the abnormal performance of the equipment.

[0074] The greater the intensity of the centrifugal vibration in real time, and the more significant the regularity of the vibration period, the more abnormal the real-time operation of the centrifuge is.

[0075] Based on the above characteristics, the distribution of centrifugal vibration intensity within a local time period of a single monitoring moment will be analyzed to determine the anomalous factors of a single monitoring moment.

[0076] For any given moment:

[0077] Curve fitting is performed on the centrifugal vibration intensity at all times within a local time period of the given moment to obtain the peak point in the fitted curve. Curve fitting and peak point acquisition are existing technologies and will not be elaborated upon here. The local time period of the given moment is a time period consisting of a preset duration with the given moment as the last moment. In this embodiment, the preset duration is 30 seconds; in specific applications, the implementer can set it according to specific circumstances.

[0078] Calculate the difference between the centrifugal vibration intensity at each peak point and the mean of the centrifugal vibration intensity at all times within the local time interval at that moment. Record this difference as the first difference. In this way, a corresponding first difference is obtained for each peak point.

[0079] The negative correlation mapping value between each peak point and the time interval at that moment is used as the time influence weight corresponding to each peak point. The negative correlation mapping values ​​of the first difference corresponding to each peak point are weighted, summed, and normalized to obtain the regularity of the vibration period at that moment. Combining the centrifugal vibration intensity and the regularity of the vibration period at that moment, the anomalous factor at that moment is obtained.

[0080] As a concrete example, the specific formula for calculating the anomalous factor is given. The anomalous factor at this moment can be expressed as:

[0081]

[0082] in, This represents the anomalous factor at that moment. This represents the centrifugal vibration intensity at that moment, where N represents the number of peak points. It represents the difference between the centrifugal vibration intensity at the nth peak point and the mean of the centrifugal vibration intensity at all times within the local time interval at that moment, which is also the first difference corresponding to the nth peak point; Let represent the time interval between the nth peak point and that moment, and let e represent the natural constant. This indicates the preset first zero-prevention parameter. This represents the normalization function.

[0083] This represents the negative correlation mapping value of the first difference corresponding to the nth peak point; This represents the time-related weight of the nth peak point.

[0084] The preset first zero-prevention parameter is introduced in the calculation formula of the anomalous factor to prevent the denominator from being 0. In this embodiment, the preset first zero-prevention parameter is 0.001. In specific applications, it can be set according to specific circumstances.

[0085] The greater the centrifugal vibration intensity at that moment, the greater the time influence weight corresponding to the peak value, and the smaller the first difference corresponding to the peak point, the more abnormal the centrifuge operation is at that moment, i.e., the greater the anomalous factor.

[0086] When a centrifuge experiences severe wear between the screw and bearing, the heat accumulation due to long-term high-frequency contact friction is stronger than usual. Therefore, the greater the temperature rise during equipment operation, the more attention should be paid to the abnormal performance of the centrifuge. At the same time, considering that if the wear of the screw and bearing is severe, rotational lag may occur between the screw and bearing when performing tasks requiring fine separation and high rotation speed. In addition, the wear exacerbates the temperature rise of the equipment. Therefore, the abnormal coefficient of the equipment will be corrected based on the temperature rise and the collected real-time temperature values.

[0087] For any given moment:

[0088] A linear fit is performed on the temperature within a local time interval at that moment to obtain the slope of the fitted line. The x-axis of the fitted line represents the time, and the y-axis represents the temperature. Linear fitting is an existing technique and will not be elaborated further here. The product of the slope and the temperature at that moment is denoted as the first product. The first product is used to correct the anomalous factor at that moment to obtain the corrected anomalous factor at that moment.

[0089] In this embodiment, the specific calculation formula for the corrected anomalous factor is given, and the corrected anomalous factor at this moment can be expressed as:

[0090]

[0091] in, This represents the anomalous factor after correction at that moment. This indicates the temperature at that moment. This represents the slope of the fitted line representing the temperature over a local time interval at that moment. This represents the hyperbolic tangent function.

[0092] The steeper the slope of the fitted straight line, the steeper the temperature rise within a local time interval at that moment. When the temperature is higher at that moment and the temperature rise within a local time interval is steeper, it indicates stronger equipment wear, so the centrifuge's anomalous coefficient needs to be appropriately increased. Conversely, when the temperature is lower at that moment and the temperature rise within a local time interval is lower, it indicates weaker equipment wear, so the centrifuge's anomalous coefficient needs to be appropriately decreased.

[0093] Through the above methods, the anomalous factors at a single monitoring moment were corrected, and the corrected anomalous factors were obtained.

[0094] Step S3: Combine the actual screw speed at each moment, the liquid flow rate, the difference in humidity of the liquid in the cylindrical and conical zones of the drum, and the corrected anomalous factor to obtain the separation anomaly score at each moment.

[0095] The lag in screw rotation leads to a greater speed difference between the screw and the drum, which in turn affects the separation and drying performance of solid materials in the cylindrical and conical regions of the drum. For example, a greater speed difference in the cylindrical region means that the solid rings are not fully separated and dried before being pushed and conveyed, resulting in poor drying efficiency. Conversely, a greater speed difference in the conical region increases the pressure resistance of the screw on the solid rings, leading to a faster drying rate in the conical region. Therefore, a real-time separation anomaly score of the centrifuge is obtained by analyzing the material separation performance.

[0096] When the wear between the centrifuge screw and bearing is excessive, a lag in coaxial rotation may occur between the screw and bearing, potentially leading to a loss of rotational speeds. When screw speed lag occurs due to wear, the speed difference between the centrifuge drum and the screw further increases (normally, the drum speed is higher than the screw speed). This large speed difference causes the spiral blades driven by the screw to advance faster against the inner wall of the drum, thus interfering with the separation and drying of the liquid within the drum and resulting in abnormal liquid flow at the centrifuge outlet.

[0097] The greater the rotational lag between the centrifuge screw and bearing, the greater the difference between the drum speed and the screw speed. This results in a stronger propulsion rate of the solid phase adhering to the inner wall of the drum by the screw in the cylindrical region. Consequently, the solid phase on the inner wall is pushed into the conical region of the drum before sufficient solid-liquid separation, leading to a lower drying level in the cylindrical region. Furthermore, the greater the difference between the drum speed and the screw speed, the greater the pressure exerted by the screw on the solid phase on the inner wall of the drum in the conical region. This results in a greater tendency for solid-liquid separation of the solid phase on the inner wall, and thus a higher drying level in the conical region.

[0098] The large difference in rotational speed between the drum and the screw leads to a significant deviation between the degree of drying of the solid phase in the cylindrical region of the drum and the degree of drying of the solid phase in the conical region of the drum. In other words, the greater the difference in the drying efficiency of the solid phase in the cylindrical and conical regions of the drum, the stronger the screw wear performance.

[0099] If the coaxial rotation tightness of the centrifuge is higher in real time, and the attention to the discharge of the liquid is greater, and the coordination of the separation efficiency of the liquid in the cylindrical and conical areas of the centrifuge drum is smaller, it further reflects the stronger abnormal performance of the centrifuge. Therefore, the separation anomaly score will be determined by combining the actual screw speed, liquid flow rate, the difference in humidity of the liquid in the cylindrical and conical areas of the drum, and the corrected anomalous factor.

[0100] For any given moment:

[0101] As a specific example, firstly, based on the difference between the actual and theoretical screw speeds at that moment, the coaxial rotation tightness is evaluated; based on the difference between the liquid flow rate and the reference liquid flow rate at that moment, the feed discharge concern is evaluated; based on the difference between the moisture content of the feed liquid in the cylindrical and conical regions of the drum at that moment, the separation efficiency coordination between the cylindrical and conical regions of the drum is evaluated. Then, by combining the coaxial rotation tightness, feed discharge concern, and separation efficiency coordination, the separation anomaly performance at that moment is obtained. Finally, the separation anomaly score at that moment is determined by combining the separation anomaly performance score and the corrected anomaly factor.

[0102] As a specific example, the degree of separation anomaly at that moment is obtained based on the tightness of coaxial rotation, the concern about liquid discharge, and the coordination of separation efficiency. Both the tightness of coaxial rotation and the concern about liquid discharge are positively correlated with the degree of separation anomaly, while the coordination of separation efficiency is negatively correlated with the degree of separation anomaly.

[0103] In this embodiment, a specific formula for calculating the separation anomaly score is given, and the separation anomaly score at this moment can be expressed as:

[0104]

[0105] in, This indicates the separation anomaly score at that moment. This indicates the actual rotational speed of the screw at that moment. Indicates the theoretical rotational speed. This indicates the liquid flow rate at that moment. Indicates the reference liquid flow rate. This indicates the humidity of the liquid material within the cylindrical zone of the drum at that moment. This indicates the humidity of the liquid material within the cone-shaped zone at that moment. This represents the anomalous factor after correction at that moment. This indicates the preset second zero-prevention parameter. This indicates the preset third zero protection parameter.

[0106] The introduction of preset second and third zero-prevention parameters in the anomaly scoring is to prevent the denominator from being 0. In this embodiment, the values ​​of both preset second and third zero-prevention parameters are 0.001. In specific applications, the implementer can set them according to the specific circumstances. The theoretical rotational speed is set by the implementer according to the specific circumstances.

[0107] The reference liquid flow rate can be obtained by statistically analyzing the distribution characteristics of the centrifuge noise decibel values ​​during historical periods when the centrifuge has been confirmed to be operating normally (without abnormalities), calculating the average liquid flow rate at all times during the historical period, and using this average value as the reference liquid flow rate.

[0108] This indicates the tightness of coaxial rotation at that moment. This indicates the level of attention given to the discharge of liquid material at that moment. This indicates the degree of coordination between the separation efficiency of the cylindrical and conical regions of the drum at that moment. This indicates the degree of separation anomaly at that moment. The greater the coaxial rotation tightness, the higher the concern about liquid discharge, the lower the coordination of liquid separation efficiency between the cylindrical and conical regions of the drum, and the larger the corrected anomaly factor at that moment, the greater the possibility of an abnormal operation of the separator at that moment, i.e., the higher the separation anomaly score at that moment.

[0109] Using the above methods, the separation anomaly score for a single monitoring moment can be obtained.

[0110] Step S4: Determine whether to issue an early warning for centrifuge malfunction based on the separation anomaly score.

[0111] Separation anomaly scores were obtained at each time point through steps S1-S3. Next, based on the relationship between the current separation anomaly score and the separation anomaly scores at historical normal times of the centrifuge, it will be determined whether the centrifuge has experienced an anomaly, and then it will be determined whether to issue an alert.

[0112] Specifically, the difference between the current separation anomaly score and the mean of the historical normal separation anomaly scores is calculated and recorded as the second difference. The normalized result of the second difference is used as the cloud monitoring early warning indicator for the current moment. The mean of the historical normal separation anomaly scores is the average of the separation anomaly scores at all moments within the historical period where the centrifuges have been confirmed to be operating normally (without anomalies). As a specific example, the max-min normalization method is used for data normalization. Other existing data normalization methods can also be used for other examples; the max-min normalization method is an existing method and will not be elaborated further here.

[0113] As a specific example, if the cloud monitoring early warning indicator is greater than the preset early warning threshold, an early warning will be issued; if the cloud monitoring early warning indicator is less than or equal to the preset early warning threshold, no early warning will be issued. The preset early warning threshold can be 0.65.

[0114] As another specific example, preset first and second warning thresholds are set, with the first warning threshold being less than the second warning threshold. The first warning threshold can be 0.65, and the second warning threshold can be 0.88. If the current cloud monitoring warning indicator is less than or equal to the first warning threshold, the equipment is considered to be operating normally and no intervention is required. If the current cloud monitoring warning indicator is greater than the first warning threshold but less than or equal to the second warning threshold, the equipment is considered to be in an abnormal operating state, and the cloud platform sends a second-level warning to the equipment management terminal. If the current cloud monitoring warning indicator is greater than the second warning threshold, the equipment is considered to be in an abnormal operating state with severe wear on the bearings and screws. In this case, the cloud platform sends a first-level warning to the equipment management terminal and, under safe conditions, shuts down the equipment, reminding staff to conduct inspection and maintenance.

[0115] Thus, the method provided in this embodiment has been used to complete the real-time monitoring of centrifuge malfunctions.

[0116] This embodiment evaluates the centrifugal vibration intensity by observing the vibration and noise performance of the conveying screw during centrifuge operation. It also provides a preliminary assessment of operational anomalies by analyzing the distribution characteristics of centrifugal vibration intensity over a localized time period at a single moment during operation. Furthermore, it dynamically corrects for anomalies by incorporating temperature and its changing trends. This approach more accurately reflects abnormal conditions during actual operation and improves the sensitivity for identifying potential faults. Considering that screw rotation lag can lead to a larger speed difference between the screw and the drum, thus affecting the separation and drying of solid materials in the cylindrical and conical regions of the drum, the significant difference in rotational speed between the drum and the screw results in varying degrees of drying of the solid phase in the cylindrical region compared to the conical region. The dryness of the solid phase in the region varies considerably. Therefore, a separation anomaly score was obtained by comprehensively analyzing the actual screw speed, liquid flow rate, and the difference in material liquid humidity between the cylindrical and conical zones of the drum, combined with a corrected anomaly factor. This embodiment evaluates the centrifuge's operating status by integrating multiple parameters, which can more comprehensively capture centrifuge anomaly characteristics and avoid the misjudgment or omission caused by relying solely on single vibration data in traditional methods. Furthermore, early warning judgments are made based on the separation anomaly score, realizing intelligent assessment and early warning of the centrifuge's operating status. This improves the accuracy and reliability of the cloud platform's remote monitoring of the centrifuge, helps to promptly detect equipment anomalies and take intervention measures, and ensures the safe and stable operation of the equipment.

[0117] An embodiment of a cloud-based centrifuge data remote monitoring system:

[0118] See Figure 2The diagram illustrates a structural block diagram of a cloud-based centrifuge data remote monitoring system according to an embodiment of the present invention. The system may include a data acquisition module, an anomaly factor determination module, an anomaly scoring module, and an early warning module.

[0119] The data acquisition module is used to acquire the centrifuge's vibration intensity, noise level, temperature, actual screw speed, liquid flow rate at the separation liquid outlet, and humidity of the liquid in the cylindrical and conical zones of the drum during centrifuge operation.

[0120] The anomalous factor determination module is used to evaluate the centrifugal vibration intensity at each moment based on the vibration intensity and noise decibel value at each moment; obtain the anomalous factor at each moment based on the distribution characteristics of the centrifugal vibration intensity within a local time period at each moment; and correct the anomalous factor by using the temperature at each moment and the temperature change characteristics within a local time period at each moment to obtain the corrected anomalous factor.

[0121] The anomaly scoring module is used to obtain the separation anomaly score at each moment by combining the actual rotation speed of the screw, the liquid flow rate, the difference in humidity of the liquid in the cylindrical and conical areas of the drum, and the corrected anomaly factor.

[0122] The early warning module is used to determine whether to issue an early warning for centrifuge malfunctions based on the separation anomaly score.

[0123] It should be understood that Figure 2 The structural block diagram and modules of the cloud-based centrifuge data remote monitoring system shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-described methods and apparatus can be implemented using computer-executable instructions and / or included in processor control code, for example, on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this specification can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, or by a combination of the above-described hardware circuits and software (e.g., firmware).

[0124] For more details about the above modules, please refer to other parts of this manual; they will not be repeated here.

[0125] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for remote monitoring of centrifuge data based on a cloud platform, characterized in that, The method includes the following steps: The centrifuge's vibration intensity, noise level (decibels), temperature, actual screw speed, liquid flow rate at the separation liquid outlet, and humidity of the liquid in the cylindrical and conical zones of the drum are obtained during operation. The centrifugal vibration intensity at each moment is evaluated based on the vibration intensity and noise decibel value at each moment; the anomalous factor at each moment is obtained based on the distribution characteristics of the centrifugal vibration intensity within the local time period at each moment; the anomalous factor is corrected by using the temperature at each moment and the temperature change characteristics within the local time period at each moment to obtain the corrected anomalous factor. By combining the actual screw speed at each moment, the liquid flow rate, the difference in humidity between the liquid in the cylindrical and conical zones of the drum, and the corrected anomalous factor, a separation anomaly score is obtained at each moment. Based on the separation anomaly score, determine whether to issue an early warning for centrifuge malfunctions; The method of obtaining the anomalous factor at each moment based on the distribution characteristics of centrifugal vibration intensity within a local time period includes: For any given moment: The centrifugal vibration intensity at all times within a local time period at any given time is curve-fitted to obtain the peak point in the fitted curve. Calculate the first difference between the centrifugal vibration intensity at each peak point and the mean of the centrifugal vibration intensity at all times within a local time interval at any given time; based on the time interval between each peak point and any given time and the corresponding first difference, obtain the regularity of the vibration period at any given time. By combining the centrifugal vibration intensity and the regularity of the vibration period at any given moment, the anomalous factor at any given moment is obtained; The local time period at any given moment is a time period consisting of a preset duration with the given moment as the last moment; The process of obtaining the separation anomaly scores at each time point includes: For any given moment: The coaxial rotation tightness at any given moment is evaluated based on the difference between the actual rotation speed and the theoretical rotation speed of the screw at any given moment. The level of concern regarding liquid discharge at any given time is evaluated based on the difference between the liquid flow rate at any given time and the reference liquid flow rate. Based on the difference in humidity between the liquid in the cylindrical zone and the conical zone of the drum at any given time, the coordination degree of separation efficiency between the liquid in the cylindrical and conical zones of the drum at any given time is evaluated. By combining the coaxial rotation tightness, the concern about liquid discharge, and the coordination of separation efficiency, the degree of separation anomaly at any given moment is obtained; The separation anomaly score at any given time point is determined by combining the separation anomaly performance level and the corrected anomaly factor. The step of determining whether to issue an early warning for centrifuge malfunctions based on the separation anomaly score includes: Calculate the second difference between the current separation anomaly score and the mean of the separation anomaly scores at historical normal times; use the normalized result of the second difference as the cloud monitoring early warning indicator at the current time. If the cloud monitoring early warning indicator is greater than the preset early warning threshold, an early warning will be issued; if the cloud monitoring early warning indicator is less than or equal to the preset early warning threshold, no early warning will be issued.

2. The method for remote monitoring of centrifuge data based on a cloud platform according to claim 1, characterized in that, The evaluation of the centrifugal vibration intensity at each moment based on the vibration intensity and noise decibel value includes: For any given moment: The normalized result between the difference between the vibration intensity at any given moment and the reference vibration intensity is determined as the mechanical amplitude factor at any given moment. Calculate the first ratio between the noise decibel value at any given time and the reference noise decibel value; By combining the mechanical amplitude factor and the first ratio, the centrifugal vibration intensity at any given moment is obtained.

3. The method for remote monitoring of centrifuge data based on a cloud platform according to claim 2, characterized in that, The step of combining the mechanical amplitude factor and the first ratio to obtain the centrifugal vibration intensity at any given moment includes: The product of the mechanical amplitude factor and the first ratio is taken as the centrifugal vibration intensity at any given moment.

4. The method for remote monitoring of centrifuge data based on a cloud platform according to claim 1, characterized in that, The step of obtaining the regularity of the vibration period at any given moment based on the time interval between each peak point and the corresponding first difference includes: The negative correlation mapping value between each peak point and the time interval of any given moment is used as the time influence weight corresponding to each peak point. The negative correlation mapping values ​​of the first difference corresponding to each peak point are weighted, summed, and normalized to obtain the regularity of the vibration period at any given moment.

5. The method for remote monitoring of centrifuge data based on a cloud platform according to claim 1, characterized in that, The process of obtaining the corrected anomalous factor by utilizing the temperature at each moment and the temperature change characteristics within a local time period at each moment includes: For any given moment: Perform a linear fit on the temperature within a local time interval at any given moment to obtain the slope of the fitted line; calculate the first product of the slope and the temperature at any given moment. The anomalous factor at any given time is corrected using the first product to obtain the corrected anomalous factor at any given time.

6. The method for remote monitoring of centrifuge data based on a cloud platform according to claim 1, characterized in that, The degree of separation anomaly at any given time is obtained by comprehensively considering the coaxial rotation tightness, the concern about liquid discharge, and the coordination of separation efficiency, including: Based on the coaxial rotation tightness, the concern about liquid discharge, and the separation efficiency coordination at any given moment, the separation anomaly performance at any given moment is obtained. The coaxial rotation tightness and the concern about liquid discharge are both positively correlated with the separation anomaly performance, while the separation efficiency coordination is negatively correlated with the separation anomaly performance.

7. A cloud-based centrifuge data remote monitoring system, the system being used to implement the method of claim 1, characterized in that, The system includes: The data acquisition module is used to acquire the centrifuge's vibration intensity, noise level, temperature, actual screw speed, liquid flow rate at the separation liquid outlet, and humidity of the liquid in the cylindrical and conical zones of the drum during centrifuge operation. The anomalous factor determination module is used to evaluate the centrifugal vibration intensity at each moment based on the vibration intensity and noise decibel value at each moment; obtain the anomalous factor at each moment based on the distribution characteristics of the centrifugal vibration intensity within a local time period at each moment; and correct the anomalous factor by using the temperature at each moment and the temperature change characteristics within a local time period at each moment to obtain the corrected anomalous factor. The anomaly scoring module is used to obtain the separation anomaly score at each moment by combining the actual rotation speed of the screw, the liquid flow rate, the difference in humidity of the liquid in the cylindrical and conical areas of the drum, and the corrected anomaly factor. The early warning module is used to determine whether to issue an early warning for centrifuge malfunctions based on the separation anomaly score.

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