Bucket wheel machine thermal measurement point digital twin modeling and fault visualization presentation method

Abstract

This invention discloses a digital twin modeling and fault visualization method for thermal measurement points of bucket wheel excavators, relating to the field of bucket wheel excavator technology. It solves the technical problems of shallow diagnosis, black box effect, and delayed response caused by the reliance on simple threshold rules in traditional unattended bucket wheel excavator systems. This invention performs in-depth analysis of electrical parameters to obtain global state coefficients, and then uses three-dimensional laser scanning and radial basis function interpolation technology to reconstruct a continuous, high-fidelity three-dimensional temperature field from the temperature data of limited measurement points to generate an intuitive thermal image evolution sequence. Finally, it performs time-series analysis based on the thermal image evolution sequence, and uses global coefficients to adjust sensitivity to output a temperature anomaly index. A composite three-dimensional model is constructed based on the temperature anomaly index, successfully advancing digital twin technology to the engineering application level and improving the intelligence level of bucket wheel excavator condition monitoring and fault early warning.

Description

Technical Field

[0001] This invention relates to the field of bucket wheel excavator technology, and in particular to a method for digital twin modeling of thermal measurement points and visualization of faults in bucket wheel excavators. Background Technology

[0002] With the rapid development of the energy industry, especially the continued demand for coal as one of the main energy sources, bucket wheel stacker-reclaimers, as key equipment for handling large bulk materials, play a vital role in coal ports, power plants, and other scenarios. With the continuous advancement of intelligent and automated technologies, the operating efficiency, safety, and intelligence level of traditional bucket wheel stackers face an urgent need for transformation and upgrading. The industry is actively promoting the technological transformation and intelligent upgrading of bucket wheel stackers to improve operational efficiency, reduce operating costs, enhance safety performance, and achieve more efficient and environmentally friendly energy management.

[0003] In existing unmanned bucket wheel excavator systems, the fault early warning function mainly relies on simple judgment rules, such as temperature over-limit alarms or current over-limit alarms. It lacks in-depth data analysis and can only output alarm results. It cannot transparently and visually present early fault symptoms, evolution process and development prediction, resulting in response lag and failure to achieve true predictive maintenance. Summary of the Invention

[0004] Therefore, it is necessary to provide a method for digital twin modeling and fault visualization of the thermal measurement points of bucket wheel excavators to address the problems mentioned in the background technology.

[0005] The objective of this invention can be achieved through the following technical solution: a method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators, comprising the following steps:

[0006] Step 1: Obtain the electrical parameters of the bucket wheel excavator and perform in-depth analysis based on them to output global state coefficients representing the overall state of the bucket wheel excavator;

[0007] Step 2: Use a cantilever laser scanning device to perform a 3D laser scan on the bucket wheel excavator body and create a 3D model of the bucket wheel excavator body; divide the bucket wheel excavator body into several functional areas according to the functional areas, construct the temperature field in the functional area based on the temperature corresponding to the measuring point in each functional area of ​​the bucket wheel excavator, and make a thermal image evolution sequence accordingly.

[0008] Step 3: Perform time-series analysis on the functional region based on the thermal imaging evolution sequence, and adjust the sensitivity by adding a global state coefficient to obtain the temperature anomaly index. Construct a composite 3D model based on the temperature anomaly index and visualize it.

[0009] In some embodiments, electrical parameters are subjected to deep analysis to output global state coefficients:

[0010] Step 101: Extract electrical parameters, specifically current, power, and load, and denote them as I(t), P(t), and F(t) respectively, where t is the index of the acquisition time, t = 1, 2, 3...T, and T is the total number of acquisition times; according to the formula... The load factor K(t) is calculated, where I 额 P is the rated current of the bucket wheel excavator. 额 α represents the rated power of the bucket wheel excavator; α, β, and γ are weighting coefficients.

[0011] Step 102: Construct a two-dimensional rectangular coordinate system with time as the horizontal axis and load factor as the vertical axis. Plot K(t) in the coordinate system according to its corresponding acquisition time t and load factor, and connect them sequentially with a smooth curve to obtain the load factor curve. Perform image feature analysis on the load factor curve to extract feature parameters, including operating stability, load health and overload impact.

[0012] Step 103: The global state coefficient S is obtained by formulating the operational stability A, load health B, and overload impact C. The specific calculation formula is as follows:

[0013]

[0014] Where η is the scaling factor of the global state coefficient, which takes a value greater than zero and controls the severity of the overall penalty.

[0015] In some embodiments, the process of extracting operational stability is as follows:

[0016] The load factor K(t) at the time of data collection is calculated using the formula... Calculate the standard deviation of the load factor curve, where The mean value of the load factor K(t) at each data acquisition time is used to obtain the operational stability. The normalization formula is as follows:

[0017] In some embodiments, the process of extracting load health status is as follows:

[0018] A healthy range is preset. Two straight lines parallel to the horizontal axis are drawn on the load factor curve graph. The load factors of these lines represent the upper and lower limits of the healthy range, respectively. The load factor curve is divided into a downward segment, a middle segment, and an upward segment based on these two lines. The downward segment is the shaded area formed by the curve and the line representing the lower limit of the healthy range; the middle segment is the shaded area formed by the curve and the lines representing the lower and upper limits of the healthy range; and the downward segment is the shaded area formed by the curve and the line representing the upper limit of the healthy range. All upward, middle, and downward segments in the curve graph are summed to obtain the upward area, middle area, and downward area, respectively, and then M[i, t]. up M mid and M down Simultaneously, the areas of the upward, middle, and downward lines are summed to obtain the total area, denoted as M. tot According to the formula and Calculate the share F of the upstream area, midstream area, and downstream area respectively. up F mid and F down Then according to the formula The load health score B is calculated, where q, p, and r are the nonlinear amplification factors corresponding to the uplink share, midlink share, and downlink share, respectively, and ζ is a constant with a value of 10. -9 This is to prevent the denominator from being zero or very small, which could lead to unstable values.

[0019] In some embodiments, the process of extracting overload impact intensity is as follows:

[0020] Overload impact intensity is used to quantify the instantaneous impact and cumulative fatigue damage experienced by a bucket wheel excavator under overload conditions, i.e., K(t) > 1.1. The calculation formula is as follows:

[0021]

[0022] Where λ is the scaling factor for the overload impact, m is the amplitude amplification exponent, Δt is the sampling time interval, and M tot The total area is the sum of the areas of the upper row, the middle row, and the lower row, and ζ is a constant with a value of 10. -9 .

[0023] In some embodiments, a temperature field is constructed within the functional region:

[0024] Step 201: Each functional area has several measuring points, and a temperature sensor is installed at each measuring point to monitor the temperature at each point; the temperature corresponding to each measuring point in each functional area is recorded as T. i , where i = 1, 2, 3...N, i represents the index of any measurement point within the functional area, and N is the total number of measurement points within the functional area;

[0025] Step 202: Separate the geometric model of the functional area from the overall three-dimensional model of the bucket wheel excavator, generate a regular three-dimensional grid within the functional area, and construct a temperature field by extending the temperature of discrete measuring points to the entire functional area. The radial basis function distance weighting method is used to calculate the weight of the influence of measuring point i on grid point P, where grid point P represents other grid points in the functional area excluding the measuring points.

[0026] Step 204: The base grid point P will serve as the basic unit for temperature field reconstruction, carrying the calculated temperature value; the temperature value D(P) of each grid point P within the functional region is obtained by weighted averaging of the temperatures at all measuring points, using the following formula:

[0027]

[0028] Step 203: Convert the temperature values ​​of each grid point within the functional area into an intuitive visualization and create a thermal image evolution sequence.

[0029] In some embodiments, the weight calculation process for the influence of the grid point P on the measured point i is as follows:

[0030] weight w i The formula for calculating (P) is:

[0031]

[0032] Where Y i Let be the three-dimensional coordinates of measurement point i, and φ be the basis function. d is the distance between point P and the measuring point, i.e., d = ||PY i ||.

[0033] In some embodiments, the process of creating a thermal image evolution sequence is as follows:

[0034] A preset temperature-color mapping table is used to convert the temperature of grid point P and the temperature of measuring point i into color values. The mapped color values ​​are then filled into the functional area according to the three-dimensional coordinates of point P and measuring point i to generate a thermal map with smooth temperature gradient. This allows the discrete measuring point temperatures to be reconstructed into a continuous and intuitive three-dimensional temperature field. The thermal maps of the bucket wheel machine at each acquisition time are sorted according to their corresponding acquisition time to generate a thermal image evolution sequence of the bucket wheel machine within the time period T.

[0035] In some embodiments, the process of generating a composite 3D model is as follows:

[0036] Step 301: Select any functional area and define any grid point Q within it, where P, i ∈ Q, i.e., Q represents any grid point within the functional area, including measuring point i and other grid points P. Pre-set each functional area in the bucket wheel excavator corresponds to a warning temperature. Extract the temperature of grid point Q within the functional area at the acquisition time t, and divide it by the warning temperature corresponding to the functional area to obtain the contribution value of the abnormal temperature at grid point Q. If the contribution value > 1, the contribution value of the grid point is marked as a valid contribution value. Summate all valid contribution values ​​within the functional area to obtain the total contribution value of the abnormal temperature of the functional area at the acquisition time t, denoted as [missing value]. Where n = 1, 2, 3, ..., n is a positive integer, representing the index of any functional area in the bucket wheel engine;

[0037] Step 302: Construct a two-dimensional rectangular coordinate system with time as the horizontal axis and the total contribution of abnormal temperature as the vertical axis. Input the total contribution of abnormal temperature at each acquisition time into the coordinate system to form several contribution points. Connect the contribution points sequentially with a smooth curve to obtain the curve of the total contribution of abnormal temperature. Perform time series analysis on the curve of the total contribution of abnormal temperature and adjust the sensitivity with a global state coefficient to obtain the temperature anomaly index.

[0038] Step 303: An abnormal range is preset. When the temperature anomaly index is greater than the upper limit of the abnormal range, the corresponding position of the functional area in the composite 3D model is marked in red, and a temperature alarm is issued. When the temperature anomaly index is within the abnormal range, the corresponding position of the functional area in the composite 3D model is marked in yellow. When the temperature anomaly index is less than the lower limit of the abnormal range, the corresponding position of the functional area in the composite 3D model is marked in green. The thermal image evolution sequence of the bucket wheel machine and the composite 3D model after color processing are visualized. The curve of the total abnormal temperature contribution of each functional area, the maximum total abnormal temperature contribution, the average abnormal contribution rate, the contribution slope, and the temperature anomaly index are stored as attribute data of each functional area in the corresponding functional area position in the composite 3D model.

[0039] In some embodiments, the temperature anomaly index generation process is as follows:

[0040] Extract the largest total abnormal temperature contribution from the total abnormal temperature contribution curve, calculate the area enclosed by the total abnormal temperature contribution curve and the horizontal axis using calculus to obtain the contribution area, and then divide the contribution area by the total time of the total abnormal temperature contribution curve to obtain the average abnormal contribution rate; perform linear fitting on all contribution points in the total abnormal temperature contribution curve to obtain the fitted line, and calculate the contribution slope of the fitted line.

[0041] The largest abnormal temperature contribution to the total value Average abnormal contribution rate H n Contribution slope k n The temperature anomaly index of each functional area is calculated and analyzed using a formula based on the global state coefficient S. This yields the temperature anomaly index of the bucket wheel excavator in each functional area. The specific calculation formula is as follows:

[0042]

[0043] in The peak contribution benchmark value for the functional area represents the upper limit of the acceptable peak contribution in this application scenario; This is the baseline value for the average contribution rate of the functional area, representing the upper limit of the average contribution rate that is allowed in this application scenario; This is the baseline value for the slope change of the functional area, representing the upper limit of the slope that is allowed in this application scenario.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] 1. This invention extracts three major indicators—operational stability, load health, and overload impact—through in-depth analysis of electrical parameters, and calculates and integrates them to generate a global state coefficient, providing a basis for subsequent steps and enabling a better understanding of the overall state background of the bucket wheel excavator when local anomalies occur.

[0046] 2. This invention utilizes three-dimensional laser scanning and radial basis function interpolation algorithms to reconstruct a continuous and intuitive three-dimensional temperature field from finite and discretely distributed temperature data from measurement points, and generates a thermal image evolution sequence. This clearly presents the temperature distribution of each functional area of ​​the bucket wheel machine, improving the comprehensiveness and accuracy of temperature status monitoring. At the same time, the generated thermal image evolution sequence provides maintenance personnel with a dynamic perspective for fault tracing, clearly demonstrating the entire process of the generation, development, and spread of temperature anomalies.

[0047] 3. This invention extracts the total value of the maximum abnormal temperature contribution, the average abnormal contribution rate, and the contribution slope by analyzing the changing trend and degree of temperature anomaly contribution. It then uses a global state coefficient to adjust the sensitivity and calculate the temperature anomaly index, ultimately generating a composite three-dimensional model. This accurately assesses the multi-dimensional characteristics of local overheating in bucket wheel excavators. Furthermore, the global coefficient links the local and the overall system, issuing a higher-level warning for local anomalies when the overall equipment condition is poor, which is more in line with the physical laws of fault chain reactions. This achieves intelligent, dynamic, and visual accurate early warning of local fault risks in bucket wheel excavators. Attached Figure Description

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

[0049] Figure 1 This is a schematic diagram of the principle of the present invention;

[0050] Figure 2 This is a segmented diagram of the load factor curve of the present invention. Detailed Implementation

[0051] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0052] In existing unmanned bucket wheel excavator systems, fault early warning functions mainly rely on simple judgment rules (such as temperature over-limit alarms or current over-limit alarms), which are too simplistic and lack in-depth data analysis. They can only output alarm results and cannot transparently and visually present early fault symptoms, evolution processes, and development predictions, resulting in response lag and failing to achieve true predictive maintenance. To solve this technical problem, the present invention adopts the following approach. It should be noted that the implementation process of the present invention is all during the operation of the bucket wheel excavator.

[0053] like Figure 1 As shown, the method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators includes the following steps:

[0054] Step 1: Obtain the electrical parameters of the bucket wheel excavator, including current, power, and load (in percentage form). Perform in-depth analysis based on these parameters to comprehensively quantify the excavator's operating status and output global state coefficients. Specifically:

[0055] Extract electrical parameters, specifically current, power, and load, and denote them as I(t), P(t), and F(t), respectively, where t is the index of the acquisition time, t = 1, 2, 3...T, and T is the total number of current acquisition times; according to the formula... The load factor K(t) is calculated, where I 额 P is the rated current of the bucket wheel excavator. 额The rated power of the bucket wheel excavator is denoted as K(t). α, β, and γ are weighting coefficients, the specific values ​​of which can be determined by subjective assignment, and satisfy α+β+γ=1. For example, the current I(t) best reflects the torque change in real time, and its weight is set to 0.5. The power P(t) is a comprehensive performance index, with the next highest weight, and its value is 0.3. The load F(t) is usually strongly correlated with the current and can be used for verification, so its weight can be lower, and its value is 0.2. When K(t)=1, it means that the bucket wheel excavator is operating under rated conditions at the current moment. When K(t)>1, it means that the bucket wheel excavator is operating under overload conditions at the current moment. When K(t)<1, it means that the bucket wheel excavator is operating under light load conditions.

[0056] A two-dimensional rectangular coordinate system is constructed with time as the x-axis and load factor as the y-axis. K(t) is plotted in the coordinate system according to its corresponding acquisition time t and load factor, and connected sequentially using a smooth curve to obtain a load factor curve. Image feature analysis is performed on the load factor curve to extract feature parameters, and formulaic calculations are then performed to output the global state coefficient S. The feature parameters include operational stability, load health, and overload impact. Specifically:

[0057] (1) Operational stability A: According to the formula Calculate the standard deviation of the load factor curve, where The mean value of the load factor K(t) at each data acquisition time is used to obtain the operational stability. The normalization formula is as follows: It should be noted that the closer the value of the operational stability A is to 1, the smoother the load coefficient curve, the smoother the operation of the bucket wheel excavator, and the less impact and fatigue. The smaller the value, the more severe the load fluctuation, and the bucket wheel excavator may be troubled by problems such as jamming, impact load, or unstable control.

[0058] (2) Load Health Level B: Based on safety and efficiency, a health range is preset. Those skilled in the art set the health range to [0.8, 1.1]. Within this range, the bucket wheel excavator operates close to its rated capacity, meaning that the invested energy, losses (depreciation), and labor costs yield the highest return on output. When the value is below the lower limit of the health range (0.8), the bucket wheel excavator's capacity is not fully utilized. Although the risk of mechanical wear and failure is low, the operating efficiency is low. When the value is above the upper limit of the health range (1.1), it indicates that the bucket wheel excavator is continuously operating under a load exceeding its design capacity, leading to overheating of the motor windings, overload wear of gears and bearings, increased structural stress, etc., which will significantly accelerate equipment aging, shorten its service life, and greatly increase the probability of sudden failures. Figure 2As shown, two straight lines parallel to the horizontal axis are drawn on the load factor curve graph. The load factors of the lines represent the upper and lower limits of the healthy range, respectively. The load factor curve is divided into a downward part, a middle part, and an upward part based on the two straight lines. The downward part is the shaded area formed by the curve and the line representing the lower limit of the healthy range; the middle part is the shaded area formed by the curve and the lines representing the lower and upper limits of the healthy range; and the downward part is the shaded area formed by the curve and the line representing the upper limit of the healthy range. All upward, middle, and downward parts in the curve graph are summed to obtain the upward area, the middle area, and the downward area, respectively, and M is calculated. up M mid and M down Simultaneously, the areas of the upward, middle, and downward lines are summed to obtain the total area, denoted as M. tot According to the formula

[0059] and Calculate the share F of the upstream area, midstream area, and downstream area respectively. up F mid and F down Then according to the formula The load health score B is calculated, where q, p, and r are the nonlinear amplification factors corresponding to the uplink share, midlink share, and downlink share, respectively, to control the amplification effect of each share. q is set to 1.4; when q > 1, the impact of the uplink share on the denominator is amplified by the nonlinear factor q, resulting in a stronger penalty effect. r is set to 1.0; when p > 1, it indicates a larger F... mid This will be further amplified, causing the load health B to approach 1 more quickly; its value is 1.1. ζ is a constant with a value of 10. -9 To prevent the denominator from being zero or very small, which would lead to numerical instability; as can be seen from the calculation formula of load health B, the closer the load health is to 1, the more it indicates that the load distribution is mainly in the middle range (i.e., the healthy range), and the better the balance between capacity, energy consumption and wear and tear is achieved; the closer the load health is to 0, the more it indicates that the proportion is dominated by upward or downward, especially upward, which will significantly reduce the healthy load health B.

[0060] (3) Overload impact degree C: The overload impact degree is used to quantify the instantaneous impact and cumulative fatigue damage degree of the bucket wheel excavator under overload conditions, i.e., K(t) > 1.1. The calculation formula is as follows:

[0061]

[0062] Where λ is the scaling factor of the overload impact, a constant greater than zero, used to control the rate of change of the entire exponential function, ensuring that the overload impact C value is reasonably distributed within the range of 0 to 1; m is the amplitude amplification exponent, with a value of 2, ensuring that high-amplitude overloads will affect the results and effectively capture the impact load; Δt is the sampling time interval, (K(t)-1.1) represents the overload amplitude, and ζ is a constant with a value of 10. -9 This formula calculates the relative proportion of the weighted overload area to the total operating area, and maps it to the range of 0-1 using an exponential function. The weighted overload area is a power of the overload amplitude (K(t)-1.1). m The summation of terms multiplied by time emphasizes the penalty for high-amplitude overloads; when there is no overload (i.e., all K(t) ≤ 1.1, the summation term is 0, the overload impact C = 0, indicating no overload impact; when an overload occurs, the summation term... The overload impact C value begins to increase; when the overload is very severe (large amplitude and long duration), the exponential term approaches 0, and the overload impact C value approaches 1, indicating that an extremely severe overload impact has been suffered; through a double nonlinear mapping using the exponential term m and the exponential function exp(...), m amplifies the contribution of high-amplitude overloads, while the exponential function ensures that the overload impact C value is very sensitive to changes in the cumulative overload. Initially, it increases rapidly during accumulation, and as the cumulative damage intensifies, the overload impact C value gradually approaches 1 (saturation), which conforms to the physical law of equipment damage accumulation.

[0063] The global state coefficient S is obtained by formulating the operational stability, load health, and overload impact. The specific calculation formula is as follows:

[0064]

[0065] Where η is the scaling factor of the global state coefficient, which takes a value greater than zero. It controls the severity of the overall penalty. The larger η is, the more sensitive S is to state changes. The natural exponential function exp(...) is used to perfectly map the global state coefficient S to the range of 0-1, and accurately output the global state coefficient. This formula integrates and represents the key indicators of the bucket wheel excavator state in three different dimensions. Through the square term and the exponential function, it responds to the deterioration process of the bucket wheel excavator with increasingly intense responses, which is in line with the law of equipment failure development.

[0066] By deeply analyzing electrical parameters to extract three major indicators—operational stability, load health, and overload impact—and then formulaically calculating and fusing them to generate global state coefficients, a crucial basis can be provided for subsequent steps, enabling an understanding of the overall state background of the bucket wheel excavator when local anomalies occur.

[0067] Step two: A cantilever laser scanning device is used to perform a 3D laser scan of the bucket wheel excavator body to obtain high-precision point cloud data. Based on this high-precision point cloud data, the bucket wheel excavator body is modeled to obtain a 3D model. The bucket wheel excavator body is divided into several functional areas according to their functional zones, including: the bucket wheel drive motor area, the bucket wheel reduction gearbox area, the suspension belt drive motor area, the pitch hydraulic area, the slewing bearing area, and the cable joint area. The temperature field within each functional area is constructed based on the temperature of the measuring points in each functional area, and a thermal image evolution sequence is created accordingly.

[0068] Step 201: Set a number of measuring points for each functional area. Those skilled in the art will set a number of measuring points for each functional area according to the importance of its corresponding function. It should be noted that different functional areas have different levels of importance for their functions. For example, the bucket wheel reducer area is a critical piece of equipment in the bucket wheel excavator. Serious problems in this area will directly lead to equipment shutdown, significant asset losses, or safety accidents. A temperature sensor is installed at each measuring point to monitor its temperature. Temperature is one of the most direct and important parameters characterizing the operating status of mechanical equipment. Most mechanical and electrical faults are accompanied by abnormal temperature rises, especially for large, expensive, and continuously operating equipment like bucket wheel excavators, where temperature is a crucial monitoring parameter. The temperature corresponding to each measuring point in each functional area is denoted as T. i , where i = 1, 2, 3, ..., N, i represents the index of any measurement point within the functional area, and N is the total number of measurement points within the functional area;

[0069] Step 202: Separate the geometric model of the functional area (such as the gearbox housing area) from the overall 3D model of the bucket wheel excavator. The surface of this model is usually composed of a large number of tiny triangular facets. Generate a regular 3D mesh lattice within the functional area. To extend the temperature of discrete measuring points to the entire functional area and construct a temperature field, it is necessary to define the influence domain of each measuring point, that is, the range and degree of influence of the temperature value of the measuring point on the surrounding area. The radial basis function distance weighting method is used to achieve this goal. For other mesh points P in the area excluding the measuring points, the weight w of the influence of measuring point i is... i The formula for calculating (P) is:

[0070]

[0071] Where Y i Let be the three-dimensional coordinates of measurement point i, and φ be the basis function. d is the distance between point P and the measuring point, i.e., d = ||PY i ||;Calculate the relative influence of each measuring point on grid point P. The sum of the weights of all measuring points on the same grid point is always 1 to ensure the rationality of the temperature calculation.

[0072] Step 204, based on the weight w of the grid point P mentioned above. i (P) Calculation: Grid point P will serve as the basic unit for temperature field reconstruction, carrying the calculated temperature value; the temperature value D(P) of each grid point P within the functional area can be obtained by weighted averaging of the temperatures of all measuring points:

[0073]

[0074] As can be seen from the calculation process of D(P) for each grid point, in the area near the measuring point, the temperature value is close to the actual measured value of the measuring point; in the area between two measuring points, the temperature shows a smooth transition; and in the area far away from all measuring points, the temperature value is the average of the temperatures of all measuring points.

[0075] Step 203: Convert the temperature values ​​of each grid point within the functional area into an intuitive visual display: First, perform color mapping, using a preset temperature-color mapping table to match the temperature D(P) of grid point P with the temperature T of the measuring point. i The temperature is converted into color values, typically using a gradient from blue to red, where blue represents low temperature, green represents normal temperature, yellow represents warning temperature, orange represents alarm temperature, and red represents dangerous high temperature. The mapped color values ​​are then filled into the functional areas according to the three-dimensional coordinates of point P and measuring point i to generate a heat map with a smooth temperature gradient. This reconstructs the discrete measuring point temperatures into a continuous and intuitive three-dimensional temperature field, making the temperature distribution and hotspot locations readily apparent, thus improving the intuitiveness and accuracy of bucket wheel excavator condition monitoring and fault diagnosis. The heat maps of the bucket wheel excavator at each acquisition time are sorted chronologically according to their corresponding acquisition times to generate a thermal image evolution sequence of the bucket wheel excavator within the time period T. This sequence clearly shows the dynamic changes, development, and evolution of the temperature field in each functional area of ​​the bucket wheel excavator over time.

[0076] By utilizing three-dimensional laser scanning and radial basis function interpolation algorithms, finite and discretely distributed temperature data from measurement points are reconstructed into a continuous and intuitive three-dimensional temperature field, generating a thermal image evolution sequence. This clearly presents the temperature distribution of each functional area of ​​the bucket wheel machine, improving the comprehensiveness and accuracy of temperature status monitoring. At the same time, the generated thermal image evolution sequence provides maintenance personnel with a dynamic perspective for fault tracing, clearly demonstrating the entire process of the generation, development, and spread of temperature anomalies.

[0077] Step 3: Analyze temperature change trends based on the thermal imaging evolution sequence, and adjust the sensitivity using the global state coefficient of the bucket wheel excavator to generate temperature anomaly indices representing the temperature risk of each functional area. Then, generate a composite 3D model based on the temperature anomaly indices of each functional area of ​​the bucket wheel excavator; specifically:

[0078] Step 301: Select any functional region and assign any grid point Q to it, where P, i ∈ Q, i.e., Q represents any grid point within the functional region, including measuring point i and other grid points P. Preset a warning temperature (i.e., the maximum allowable operating temperature of each functional region) for each functional region in the bucket wheel excavator. It should be noted that the warning temperatures are set by those skilled in the art based on the design limits of the equipment or components corresponding to each functional region (e.g., motor insulation class, bearing lubrication limit, temperature resistance of sealing materials, etc.), combined with the physical failure modes of each functional region (the sensitivity and damage mechanisms of components in different functional regions differ). Extract the temperature corresponding to grid point Q at acquisition time t within the functional region and divide it by the warning temperature corresponding to this functional region to obtain the contribution value of the abnormal temperature at this grid point. If the contribution value > 1, the contribution value of this grid point is marked as a valid contribution value. Summate all valid contribution values ​​within the functional region to obtain the total abnormal temperature contribution value of this functional region at acquisition time t, denoted as . Where n = 1, 2, 3, ..., n is a positive integer, representing the index of any functional area in the bucket wheel excavator; it should be noted that, as can be seen from the calculation process of the total contribution of abnormal temperature, This is a dimensionless constant; the larger this value, the more severe the temperature anomaly in the functional area at this acquisition time.

[0079] Step 302: Plot time on the x-axis and calculate the contribution of abnormal temperatures to the total value. A two-dimensional rectangular coordinate system is constructed with the vertical axis as the ordinate. The total value of the temperature anomaly contribution at each acquisition time is input into the coordinate system to form several contribution points. A smooth curve is used to connect the contribution points sequentially to obtain a curve of the total anomaly temperature contribution. The maximum total anomaly temperature contribution value is extracted from the curve and denoted as . The maximum total abnormal temperature contribution represents the most severe instantaneous overheating intensity experienced by the functional area during this period. An extremely high peak value usually indicates that the functional area has briefly experienced extremely high thermal stress, which may be a precursor to a serious failure. The contribution area is obtained by calculating the area enclosed by the curve of the total abnormal temperature contribution and the horizontal axis (i.e., the time axis) using calculus. Then, the contribution area is divided by the total time (T) of the curve of the total abnormal temperature contribution to obtain the average abnormal contribution rate, denoted as H. n The average abnormal contribution rate represents the degree of abnormal contribution per unit time, quantifying the persistence and cumulative effect of thermal stress. A larger abnormal contribution indicates that the equipment is in a long-term, stable overheating state. Even if the peak value is not high, continuous action will lead to material aging and lubrication failure. A linear fit is performed on all contribution points in the total abnormal temperature contribution curve to obtain a fitted line. The contribution slope of the fitted line is calculated and denoted as k. n Contribution slope k nIndicates the trend and rate of change of anomalous contributions; k n >0 indicates that the overheating situation is worsening rapidly, k n =0 indicates that the overheating situation is in a steady state, k n <0 indicates that the overheating situation is easing; the maximum abnormal temperature contributes to the total value. Average abnormal contribution rate H n Contribution slope k n The temperature anomaly index of each functional area is calculated and analyzed using a formula based on the global state coefficient S. This yields the temperature anomaly index of the bucket wheel excavator in each functional area, which is then annotated in the 3D model of the bucket wheel excavator to generate a composite 3D model. The specific calculation formula is as follows:

[0080]

[0081] in The peak contribution benchmark value for the functional area represents the upper limit of the acceptable peak contribution in this application scenario; This is the baseline value for the average contribution rate of the functional area, representing the upper limit of the average contribution rate that is allowed in this application scenario; The slope change benchmark value of the functional area represents the upper limit of the slope that can be allowed in this application scenario. The formula integrates abnormal peak value, average level and trend of change to avoid misjudgment by a single indicator. The multiplicative structure is adopted to regard peak intensity, persistence and development rate as evidence of mutual amplification. The smaller the global state coefficient S, the worse the overall state of the bucket wheel machine. In this context, the same local anomaly is amplified (more likely to lead to chain failure). This formula integrates the global state coefficient of the bucket wheel machine, which is a leap from isolated local judgment to system-wide linkage judgment, and realizes dynamic perception of the risk of local temperature anomaly.

[0082] Step 303: An abnormal range is preset, which is set to [1.2, 1.8] in this field. When the temperature anomaly index is greater than the upper limit of the abnormal range, it indicates that the temperature of this functional area is seriously abnormal. In this case, the corresponding position of the functional area in the composite 3D model is marked in red, and a temperature alarm is issued. When the temperature anomaly index is within the abnormal range, the corresponding position of the functional area in the composite 3D model is marked in yellow. When the temperature anomaly index is less than the lower limit of the abnormal range, the corresponding position of the functional area in the composite 3D model is marked in green. The thermal image evolution sequence of the bucket wheel machine and the composite 3D model after color processing are visualized, and the curve of the total abnormal temperature contribution of each functional area and the maximum total abnormal temperature contribution are displayed. Average abnormal contribution rate H n Contribution slope k n and temperature anomaly index V nThe attribute data of each functional area is stored in the corresponding functional area location in the composite 3D model; through the visualization of the bucket wheel machine status and deep data fusion, the thermal image evolution sequence and the composite 3D model based on temperature anomaly index color mapping are synchronously visualized and displayed, providing an intuitive spatial distribution and spatiotemporal evolution process of the bucket wheel machine's health status.

[0083] By analyzing the changing trends and degrees of temperature anomaly contributions, the maximum total abnormal temperature contribution, average anomaly contribution rate, and contribution slope are extracted. A global state coefficient is introduced for sensitivity adjustment to calculate and analyze the temperature anomaly index, and based on this, a composite three-dimensional model is finally generated. This accurately assesses the multi-dimensional characteristics of local overheating in bucket wheel excavators. Furthermore, by introducing a global coefficient, the local and overall conditions are correlated. That is, when the overall condition of the equipment is poor, a higher level of early warning is issued for local anomalies, which is more in line with the physical laws of fault chain occurrence. This achieves intelligent, dynamic, and visual accurate early warning of local fault risks in bucket wheel excavators.

[0084] As can be seen from the specific implementation process of the above examples: First, this invention extracts three indicators—operational stability, load health, and overload impact—through in-depth analysis of electrical parameters, and integrates them to generate a global state coefficient characterizing the overall operating state of the bucket wheel excavator. Using three-dimensional laser scanning and radial basis function interpolation technology, the temperature data from limited measuring points is reconstructed into a continuous, high-fidelity three-dimensional temperature field to generate an intuitive thermal image evolution sequence, solving the problems of blind spots and incomplete monitoring in temperature monitoring. Second, this invention performs time-series analysis on the temperature field of each functional area, extracting the total value of the largest abnormal temperature contribution, the average abnormal contribution rate, and the contribution slope. A global state coefficient is introduced for sensitivity adjustment, and a temperature anomaly index that dynamically reflects the risk of local faults is calculated and analyzed. This considers not only the local overheating itself but also whether the bucket wheel excavator as a whole can withstand such overheating, achieving risk perception based on both the overall and local aspects. Finally, all analysis results are integrated into a composite three-dimensional model: risk levels are intuitively marked using red, yellow, and green colors, and process data and result data are bound to the model as attribute data, forming a digital twin that is both intuitively interactive and contains a rich data kernel.

[0085] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators, characterized in that, Includes the following steps: Step 1: Obtain the electrical parameters of the bucket wheel excavator and perform in-depth analysis based on them to output global state coefficients representing the overall state of the bucket wheel excavator; Step two: A three-dimensional laser scan of the bucket wheel excavator body is performed using a cantilever laser scanning device to obtain a three-dimensional model of the excavator body. The excavator body is divided into several functional areas according to their functional zones. Based on the temperatures corresponding to the measuring points in each functional area, a temperature field is constructed within that functional area, and a thermal imaging evolution sequence is created accordingly. The specific construction of the temperature field within each functional area is as follows: Step 201: Each functional area has several measuring points, and a temperature sensor is installed at each measuring point to monitor the temperature at each point; the temperature corresponding to each measuring point in each functional area is recorded as T. i , where i = 1, 2, 3, ..., N, i represents the index of any measurement point within the functional area, and N is the total number of measurement points within the functional area; Step 202: Separate the geometric model of the functional area from the overall three-dimensional model of the bucket wheel excavator, generate a regular three-dimensional grid within the functional area, and construct a temperature field by extending the temperature of discrete measuring points to the entire functional area. The radial basis function distance weighting method is used to calculate the weight of the influence of measuring point i on grid point P, where grid point P represents other grid points in the functional area besides the measuring points. Step 204: The base grid point P will serve as the basic unit for temperature field reconstruction, carrying the calculated temperature value; the temperature value of each grid point P within the functional area will be obtained by weighted averaging of the temperatures of all measuring points. Step 203: Convert the temperature values ​​of each grid point within the functional area into an intuitive visualization and create a thermal image evolution sequence; The weight calculation process for the influence of grid point P on measured point i is as follows: weight w i (P) The calculation formula is: ； Where Y i Let i be the three-dimensional coordinates of the measurement point. For basis functions, d is the distance between point P and the measuring point, i.e., d = ; Step 3: Perform time-series analysis on the functional region based on the thermal imaging evolution sequence, and adjust the sensitivity by adding a global state coefficient to obtain the temperature anomaly index. Construct a composite 3D model based on the temperature anomaly index and visualize it.

2. The method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators according to claim 1, characterized in that, Electrical parameters are analyzed in depth to output global state coefficients: Step 101: Extract electrical parameters, including current, power, and load, and denote them as I(t), P(t), and F(t), respectively, where t is the index of the acquisition time, t = 1, 2, 3, ..., T, and T is the total number of acquisition times; according to the formula... The load factor K(t) is calculated, where I 额 P is the rated current of the bucket wheel excavator. 额 α represents the rated power of the bucket wheel excavator; α, β, and γ are weighting coefficients. Step 102: Construct a two-dimensional rectangular coordinate system with time as the horizontal axis and load factor as the vertical axis. Plot K(t) in the coordinate system according to its corresponding acquisition time t and load factor, and connect them sequentially with a smooth curve to obtain the load factor curve. Perform image feature analysis on the load factor curve to extract feature parameters, including operating stability, load health and overload impact. Step 103: The global state coefficients are obtained by formulating the operational stability, load health, and overload impact.

3. The method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators according to claim 2, characterized in that, The process of extracting operational stability: The load factor K(t) at the time of data collection is calculated using the formula... Calculate the standard deviation of the load factor curve, where The mean value of the load factor K(t) at each data acquisition time is used to obtain the operational stability A by normalization. The formula for normalization is: .

4. The method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators according to claim 3, characterized in that, The process of extracting load health status: A healthy range is preset. Two straight lines parallel to the horizontal axis are drawn on the load factor curve graph. The load factors of the lines represent the upper and lower limits of the healthy range, respectively. The load factor curve is divided into a downward part, a middle part, and an upward part based on the two straight lines. The downward part is the shaded area formed by the curve and the line representing the lower limit of the healthy range; the middle part is the shaded area formed by the curve and the lines representing the lower and upper limits of the healthy range; and the downward part is the shaded area formed by the curve and the line representing the upper limit of the healthy range. All upward, middle, and downward parts in the curve graph are summed to obtain the upward area, the middle area, and the downward area, respectively. The total area is then summed. Divide the upstream area, midstream area, and downstream area by the total area to obtain the respective shares of the upstream area, midstream area, and downstream area. Then, perform formulaic calculation and analysis to obtain the load health status.

5. The method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators according to claim 4, characterized in that, The process of extracting overload impact intensity: Overload impact intensity is used to quantify the instantaneous impact and cumulative fatigue damage experienced by a bucket wheel excavator under overload conditions, i.e., K(t) > 1.

1. The calculation formula is as follows: Where λ is the scaling factor for the overload impact, m is the amplitude amplification exponent, and Δt is the sampling time interval. For the total area, It is a constant.

6. The method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators according to claim 1, characterized in that, The process of creating a thermal image evolution sequence: A preset temperature-color mapping table is used to convert the temperature of grid point P and the temperature of measuring point i into color values. The mapped color values ​​are then filled into the functional area according to the three-dimensional coordinates of point P and measuring point i to generate a thermal map with smooth temperature gradient. This allows the discrete measuring point temperatures to be reconstructed into a continuous and intuitive three-dimensional temperature field. The thermal maps of the bucket wheel machine at each acquisition time are sorted according to their corresponding acquisition time to generate a thermal image evolution sequence of the bucket wheel machine within the time period T.

7. The method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators according to claim 1, characterized in that, The process of generating composite 3D models: Step 301: Select any functional area and set any grid point Q in it, where P, i∈Q, that is, Q represents any grid point in the functional area, including measurement point i and other grid points P; Each functional area in the pre-set bucket wheel excavator corresponds to a warning temperature. The temperature of grid point Q in the functional area at the acquisition time t is extracted and divided by the warning temperature corresponding to the functional area to obtain the contribution value of the abnormal temperature of grid point Q. If the contribution value is greater than 1, it means that the contribution value of the grid point is marked as a valid contribution value. All valid contribution values ​​in the functional area are summed to obtain the total contribution value of the abnormal temperature of the functional area at the acquisition time t. Step 302: Construct a two-dimensional rectangular coordinate system with time as the horizontal axis and the total contribution of abnormal temperature as the vertical axis. Input the total contribution of abnormal temperature at each acquisition time into the coordinate system to form several contribution points. Connect the contribution points sequentially with a smooth curve to obtain the curve of the total contribution of abnormal temperature. Perform time series analysis on the curve of the total contribution of abnormal temperature and adjust the sensitivity with a global state coefficient to obtain the temperature anomaly index. Step 303: An abnormal range is preset. When the temperature abnormality index is greater than the upper limit of the abnormal range, the corresponding position of the functional area in the composite 3D model is marked in red and a temperature alarm is issued; when the temperature abnormality index is within the abnormal range, the corresponding position of the functional area in the composite 3D model is marked in yellow; when the temperature abnormality index is less than the lower limit of the abnormal range, the corresponding position of the functional area in the composite 3D model is marked in green. The thermal image evolution sequence of the bucket wheel excavator and the composite 3D model after color processing are visualized. The total abnormal temperature contribution curve of each functional area, the maximum total abnormal temperature contribution, the average abnormal contribution rate, the contribution slope, and the temperature abnormality index are stored as attribute data of each functional area in the corresponding functional area position in the composite 3D model.

8. The method for digital twin modeling and fault visualization of thermal measurement points of bucket wheel excavators according to claim 7, characterized in that, The process of generating the temperature anomaly index: Extract the largest total abnormal temperature contribution from the total abnormal temperature contribution curve, calculate the area enclosed by the total abnormal temperature contribution curve and the horizontal axis using calculus to obtain the contribution area, and then divide the contribution area by the total time of the total abnormal temperature contribution curve to obtain the average abnormal contribution rate; perform linear fitting on all contribution points in the total abnormal temperature contribution curve to obtain the fitted line, and calculate the contribution slope of the fitted line. The temperature anomaly index of the functional area is calculated and analyzed by formulating the total value of the maximum abnormal temperature contribution, the average abnormal contribution rate, the contribution slope and the global state coefficient. Thus, the temperature anomaly index of the bucket wheel excavator in each functional area can be obtained.

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