METHOD OR DEVICE FOR USING A DATA MODEL FOR MONITORING OR EVALUATING A BELT CONVEYOR

Description

[0001] The invention relates to a method or device for using a data model to monitor or evaluate a belt conveyor and / or a plurality of belt conveyors. Both the monitoring and the evaluation of the belt conveyor or the plurality of belt conveyors involve their analysis. The present invention further relates to a computer program for carrying out the method. The invention relates in particular to a method for performance evaluation (i.e., the assessment of performance) of a belt conveyor or a plurality of belt conveyors in the mining industry, i.e., in mining, using a digital twin. The digital twin is an example of a data model or an example of the use of a data model.

[0002] From the article by Ziegler M.: "Digital twin based method to monitor and optimize belt conveyor maintenance and operation", Proceedings of the 2019 Coal Operators Conference, Mining Engineering, 01.02.2019, pages 124-132, a method for analyzing a belt conveyor is known, wherein a data model is used, the data model being a digital representation of the belt conveyor, a first function value of the belt conveyor being determined, the first function value and a second function value being used to analyze the belt conveyor, and the second function value relating to the belt conveyor or another belt conveyor.

[0003] US 2021 / 053768 A1 and US 10 384 881 B2 show similar procedures.

[0004] The method or device for using a data model to monitor or evaluate at least one belt conveyor relates in particular to condition monitoring and / or condition prediction for the at least one belt conveyor. Condition monitoring (CM) serves in particular the purpose of observing the "health" of components, machines, assets, plant sections, and / or plants and, by analyzing CM-related data, making statements about changes in this state. For this purpose, for example, Industrial Internet of Things (IIoT) platforms are used to collect data transmitted by devices, store it decentrally, visualize raw data, analyze data, and / or visualize the results of the analysis. Local solutions are also possible.

[0005] Continuous condition monitoring and analysis of belt conveyor parameters helps, for example, to predict potential damage and ideally prevent it from occurring in the first place. This applies particularly to important parameters such as vibration and power consumption. In a belt conveyor, sensors can record measured values ​​(e.g., cooling temperature, vibration, electrical voltage and current of a power converter and / or a motor, and / or torque) at various points in the system and transmit them to an evaluation unit, such as the respective condition monitoring system (CMS) and / or a controller. The CMS can also be integrated into the controller. At least part of the measurement data processing can be performed by the evaluation unit. This can include monitoring functions such as trend displays, forecasts, and the recording of raw data.

[0006] The data model can also be referred to as a "digital twin." Specifically, a digital twin is a representation of a real-world system within a digital context (IT system), for example, with the ability to "set" time (i.e., to view the system quickly or slowly from the past through the present and into the future). The data model can encompass individual systems, subsystems, or entire systems.

[0007] Belt conveyors are used primarily as a means of transport in the basic materials industry, in ports, power plants, mines, and other industrial settings. Belt conveyors are arguably the most frequently used means of transport in the raw materials industry (mining and cement) for efficiently moving bulk materials (e.g., ore, coal, gravel, cement) from one location to another. Cost pressures, demands for increased performance, and requirements for operational optimization are key drivers and requirements for the operation and operation of belt conveyors and other industrial equipment.

[0008] Solutions exist for recording and evaluating the technical aspects of a belt conveyor, such as monitoring component temperatures and identifying trends to detect component wear or determine failure probabilities. Combined with operational data, such as throughput and operating time, KPIs (Key Performance Indicators) can be calculated, which can then be used to assess system performance.

[0009] Requirements for industrial plants, such as those used or found in the mining and cement industries, can be divided into two groups. These two groups can also be described as a kind of main category.

[0010] A group includes, for example, at least one of the following mechanical engineering aspects: increasing the service life of components, avoiding failures, reducing wear, etc.

[0011] A second group includes, for example, at least one of the following process engineering aspects: avoidance of downtime, reduction of plant runtime without material transport (no empty runs), faster start-up, etc.

[0012] For the first group, various solutions can be used for monitoring and / or assessing the condition of individual components, such as recording temperatures or vibrations, particularly of different components like bearings of transport rollers, bearings of motors, electric motors, etc. This allows, for example, simple limit value monitoring to prevent immediate damage or, through appropriate analysis of this measurement data, to identify trends in order to gain insights into wear or the probability of failure of the individual component.

[0013] For the second group, data from an operational process of the industrial plant, such as throughput, energy consumption, and operating time, can be used to derive KPIs for evaluating plant performance. KPIs can be defined at the discretion of a manufacturer, operator, or person operating the industrial plant. However, this can make it more difficult to compare different plants, especially those operated by different companies.

[0014] Comparing belt conveyors can be difficult for several reasons. Belt conveyors can differ in one or more of the following aspects: topology (especially regarding the topology of the installation site), conveying capacity, components, etc. Furthermore, there are numerous other influencing factors and complex relationships that can be considered in monitoring and evaluating the system.

[0015] One of the purposes of the invention is to better analyze a belt conveyor.

[0016] A solution to the problem is achieved by a method according to claim 1, by a belt conveyor according to claim 13, or by a computer program product according to claim 15. Embodiments are found in particular according to claims 2 to 12 and 14.

[0017] A data model is used in a process for analyzing a belt conveyor. This data model is a digital representation of the belt conveyor. This digital representation, also known as a digital twin, can digitally map the belt conveyor completely or partially. It is a representation of a real belt conveyor, which may be in a commissioning phase, an operating phase, an idle phase, a repair phase (etc.), or it may be out of service or dismantled, but was previously in operation. The process determines an initial functional value of the belt conveyor. This initial value is determined primarily using the data model. The result is a standardized initial functional value. Thus, the data model allows for the generation of standardized functional values.The data model is specifically designed for a particular belt conveyor. This specific data model allows for the creation of standardized function values. Standardization thus makes different belt conveyors comparable. For the analysis of the belt conveyor, the first function value and a second function value are used. The function values ​​used for the analysis are, in particular, standardized function values. Function values ​​are therefore standardized using the data model. For example, there is a first function value that is standardized and a second function value that is standardized. Both standardized function values ​​can be further referred to as the first and second function values. Thus, there are unstandardized function values ​​and standardized function values. The determination of the first standardized function value is carried out, for example, using a first data model for a first belt conveyor.The determination of the second normalized function value is carried out, for example, using a second data model for a second belt conveyor. The determination of the second function value is therefore performed specifically using the data model or, alternatively, using a further data model for another belt conveyor. This results in a normalized second function value. The normalization allows the function values ​​to be analyzed, and in particular, compared with one another. Specifically, a comparison is made between the first normalized function value and the second normalized function value. The second function value relates, for example, to the belt conveyor (which can also be referred to as the first belt conveyor in the following) or to another belt conveyor (i.e., a second belt conveyor and / or a third belt conveyor, etc.).If the first and second function values ​​relate to the first belt conveyor, the first function value is determined, for example, before a modification of the first belt conveyor, and the second function value is determined after the modification. The modification might involve, for example, a new conveyor path. This allows normalized data from the belt conveyor to be compared before and after the modification. In another example, the load on the belt conveyor changes, particularly its weight. Because the load, along with the topology, has a significant influence, especially on the energy consumption of the belt conveyor, this energy consumption, as an example of a function value, is also topology-dependent and can be normalized with respect to the load. This makes it possible, for example, to normalize and compare the function values ​​of a belt conveyor under different loads.Therefore, a first functional value from a first load can be compared with a second functional value from a second load, since the functional values ​​are normalized. In another example, there could be a first belt conveyor and a second belt conveyor, which can be compared. This comparison is an analysis. Either the first belt conveyor and / or the second belt conveyor can be analyzed. The second belt conveyor (or multiple belt conveyors can be analyzed) is, for example, located in a different place than the first belt conveyor. A first functional value is determined for the first belt conveyor, which is normalized. A second functional value is determined for the second belt conveyor, which is normalized. Normalization makes it possible to compare the first and second functional values.The first function value of the first belt conveyor is compared with the second function values ​​of the second belt conveyor. Normalization is performed using the data model. A first data model is used as the digital representation for the first belt conveyor. A second data model is used as the digital representation for the second belt conveyor. The creation of a digital representation is the task of the data model. Through normalization, using the respective digital representations, an analysis of each belt conveyor can be performed. Without normalization, the belt conveyors would not be comparable, particularly due to their topology dependencies.

[0018] To analyze at least one belt conveyor, the first function value is normalized using the data model. This normalization reduces, in particular, the topology dependency of the function values. Normalization can also eliminate the topology dependency from the function value(s). Thus, normalization using the data model(s) makes it possible, for example, to compare or analyze two belt conveyors that overcome different height profiles and / or transport different loads.

[0019] To analyze a first and a second belt conveyor, and in particular to compare them, a first function value of the first belt conveyor is normalized using the first data model for the first belt conveyor. This normalization reduces, in particular, any topology dependence of the function values. Similarly, a second function value of the second belt conveyor is normalized using the second data model for the second belt conveyor. This normalization also reduces, in particular, any topology dependence of the function values. Thus, the first function value belongs to the first belt conveyor, and the second function value belongs to the second belt conveyor, making the function values ​​comparable through normalization. For example, this allows...Belt conveyors that differ from each other in terms of the height profile to be overcome are compared with each other with regard to their energy consumption and / or efficiency, since the height profile has been standardized.

[0020] Furthermore, for example, an additional belt conveyor can be operated concurrently with the first belt conveyor, as may have been the case in the past, although the additional belt conveyor may have been decommissioned or dismantled. A belt conveyor that has been modified and, in its modified form, represents the first belt conveyor can also be considered an additional belt conveyor. A belt conveyor that transports a different material and / or a different quantity of material can also be considered an additional belt conveyor. This applies particularly to the same belt conveyor being used for different purposes at different times, i.e., transporting a different material. The operating values ​​are determined at specific intervals and / or the operating values ​​pertain to different belt conveyors.Different belt conveyors are, for example, belt conveyors that are located in a different place or operated at different times. Thus, a belt conveyor after a modification differs from its initial state, allowing for a comparison between the first and second belt conveyors. Examples of time intervals used to determine the functional values ​​are: every minute, every hour (especially for process information), every month (especially for a long-term evaluation of implemented measures such as changing parts, using a better bearing, using a more powerful motor, using a different conveyor belt, etc.), every year (especially for reasons as in the sub-item "monthly" and / or especially for a determination depending on the season, temperature, snow, rain, etc.)

[0021] The function value can also refer to a single value or a multitude of values, which are determined / measured hourly, for example, to identify curves over time that reveal trend developments.

[0022] In one embodiment of the method, an initial functional value is determined that relates the required energy to the transport task performed and allows for an evaluation (benchmark value) of the belt conveyor. The required energy specifically refers to the drive energy of all drives of the belt conveyor for transporting a load or a selection thereof. Since belt conveyors typically vary considerably due to differing topologies, center distances, and lift heights, this method is preferably only suitable for evaluating the specific belt conveyor under consideration. In the medium and long term, changes in this functional value can be used to infer changes in the behavior of this belt conveyor. Otherwise, a second belt conveyor should be similar to or identical to the first with regard to topology, center distances, lift height, etc.A change in behavior that may be observed over time in the first belt conveyor can be caused by wear and tear, temperature influences, deterioration of the functionality of individual components and / or similar factors.

[0023] The analysis of the belt conveyor therefore primarily concerns benchmarking, i.e., a benchmarking process for belt conveyors (MC) using measured values ​​from the respective belt conveyors during the ongoing operation of a system that incorporates a belt conveyor or where a belt conveyor is used. Thus, an analysis, such as a comparison, is possible without influencing the operation of the belt conveyors through special measuring devices or test runs.

[0024] As explained above, belt conveyors can vary considerably, making comparison, especially using only the initial functional value described above, impossible or only possible in isolated cases. To nevertheless enable a benchmarking process, certain conditions must be met, including at least the following: 1. A digital model is required for the belt conveyor(s) to be analyzed or compared. This model uses mathematical algorithms to simulate the electrical and mechanical behavior of the belt conveyor. The mathematical algorithms used are based on standards such as DIN and / or CEMA. 2. The digital model is used to normalize the belt conveyor(s). This eliminates inconsistencies and provides comparable results.For this purpose, the function value described above is used, and a normalized function value is determined, in particular by eliminating the lifting power, considering only the mechanically used energy, and / or excluding dynamic operating processes. Eliminating the lifting power eliminates differences arising from the topology of the belt conveyors. Considering only the mechanical energy eliminates the influence of different drive configurations and efficiency variations. Excluding dynamic operating processes (e.g., starting and stopping) eliminates influences from different operating behaviors of the belt conveyors. Dynamic operating processes are, in particular, processes in which acceleration causes a change in the velocity of the material being conveyed. Normalization can therefore be performed in one, two, or three ways.Standardization allows for the comparison and evaluation of identical or similar states of different belt conveyors. The analysis, such as a benchmarking process, does not need to be a snapshot but can also be conducted over a longer period (a day, a week, etc.). This ensures that sufficient measurement data is collected to determine the first and second standardized performance values. Load classes and / or loading classes can also be taken into account, as already described in the final version. 3. The belt conveyors to be compared are advantageously of the same or similar design, with a particularly similar proportion of the sum of special and secondary resistances relative to the total resistance. A similar design means, in particular, that the belt conveyors were manufactured according to the established DIN or CEMA standards.The classification of similarly sized resistance components for special and secondary resistances compared to the total resistance could be done in groups as follows: Group 1 → Special and secondary resistances < 5% of the total resistance; Group 2 → Special and secondary resistances < 10% of the total resistance; Group 3 → Special and secondary resistances < 15% of the total resistance. This classification is exemplary. It can be adapted to the belt conveyors used for the analysis. The determination of special and secondary resistances is carried out in the digital models of the belt conveyors according to the rules known from standards (e.g., DIN or CEMA).For example, DIN standards include a diagram illustrating the importance of indentation rolling resistance for safe system dimensioning while minimizing investment and operating costs. This diagram shows the magnitudes of the resistance components for longer belt conveyors, differentiating between a horizontal conveyor and one with an approximately 5% incline. It also points out that with the increasing use of energy-optimized conveyor belts, the proportion of indentation rolling resistance to the total motion resistance will decrease accordingly. When comparing the resistance components of two longer belt conveyors of the same design but with different inclines, the following resistances are distinguished: incline resistance, special resistances, secondary resistances, oscillation bending resistance, conveyor belt flexing resistance, idler running resistance, and indentation rolling resistance.

[0025] In one embodiment of the procedure, two and / or all three of the conditions described above are fully or partially fulfilled.

[0026] Belt conveyors that meet these conditions, i.e., are in the same group as described in section 3, can be compared with each other.

[0027] Determining the two described function values ​​allows us to: To use an initial, even non-standardized, functional value to evaluate the operational behavior of a single belt conveyor, whereby operational behavior can encompass both electrical and mechanical properties as well as operational quality; operational quality can refer to: o How continuously does the belt conveyor operate? o Are there many operating phases without material being transported? and / or o Are start and stop cycles frequent? To use an initial, even non-standardized, functional value to identify medium- or long-term changes in one and the same belt conveyor, in order to potentially...to separately identify causes and initiate countermeasures; to use the second functional value to compare different belt conveyors based on the described functionalities of the first functional value, whereby the first and second functional values ​​are normalized; for example, this makes it possible to compare the performance of belt conveyors in different plants, e.g., cement plants, ore mines, etc., and to carry out a corresponding benchmarking process; in evaluating the benchmarking process, positive experiences in plant A can be transferred to the operating conditions in plant B; the benchmarking process can provide the basis for optimizing the operating behavior of belt conveyors at different locations.

[0028] In one implementation of the procedure, standardization is performed for analysis. This standardization specifically concerns the functional value or a multitude of functional values. Standardization enables the comparability of belt conveyors. Thus, a first belt conveyor can be compared to a second belt conveyor through standardization, even if the first and second belt conveyors differ. Such differences arise, for example, from a different lifting height and / or a different belt length, and / or a different conveyed material, and / or repairs, etc. The described standardization allows for the creation of a benchmark for evaluating the performance of a belt conveyor, as well as for evaluating the performance of belt conveyors with similar transport tasks.

[0029] Systems in the mining industry are dimensioned according to technical, physical, and mathematical rules, which are defined for belt conveyors, for example, in DIN or CEMA standards. This enables standardized design of belt conveyors. A digital twin can be developed for various belt conveyors, based on the aforementioned rules and validated with measurement data to ensure it behaves like its physical counterpart. This validation can be based on the dimensioning data or on initial measurements taken during or shortly after commissioning. In this way, for example, the initial state of the belt conveyor can be captured in the digital twin.

[0030] In one embodiment of the method, a standard is applied to an operating state of the belt conveyor. An example of an operating state is the speed at which the belt conveyor is operated.

[0031] In one embodiment of the process, normalization is performed to a characteristic value of a material, where the characteristic value specifically relates to or represents the weight and / or quantity of the material. The material is the material conveyed, i.e., transported, by the belt conveyor. Normalization to the material, or to the material's characteristic value, relates, for example, to the material's load and / or its specific density. Different material loads on the belt conveyor result in load classes. Load classes can also be referred to as load classes. Load classes can differ from one another, for example, as follows, or include the following parameters: Quantity differences at the same speed: Mass in meters = Conveyor capacity (e.g., in tonnes / hour) / Speed ​​(e.g., in meters / second)

[0032] Standardization into load classes allows the same belt conveyor to be standardized in such a way that changes in its functional value can be detected. The functional value can be determined for each load class, and changes in the functional value over time then allow conclusions to be drawn about the performance and / or condition of the belt conveyor.

[0033] In the method according to the invention, normalization to a lifting height is used. The lifting height is the height that the belt conveyor must overcome or actually overcomes. The lifting height can be positive, negative, or zero. Different terrain profiles can be traversed using the belt conveyor, resulting in different individual lifting heights, particularly due to the terrain's ups and downs, which ultimately combine into a single lifting height. Normalization to the lifting height makes it possible to compare different belt conveyors. The operating values ​​are determined, especially for the respective speed and load conditions, and then normalized to the lifting height. This allows for the comparison of different belt conveyors. It is particularly possible to also consider the conveying capacity, as this is indirectly included in the load classes.

[0034] In one embodiment of the method, the standardization concerns the first functional value of the belt conveyor (i.e., specifically the first belt conveyor) and the second functional value of the subsequent belt conveyor. The method is therefore applicable to multiple belt conveyors, and the standardization allows for the comparison of different belt conveyors.

[0035] In one embodiment of the procedure, the belt conveyors do not differ in their length and / or conveying capacity by more than 50%, particularly 25%, and the belt conveyors are designed to perform similar transport tasks. A similar transport task exists, for example, when goods of the same type are transported, such as iron ore, coal, sand, etc. Specifying a maximum difference facilitates comparability and / or reduces the standardization effort. Belt conveyors to be compared are therefore used specifically for similar transport tasks. Similar transport tasks arise for the following belt conveyors, which differ in the length of the path they travel: Long-distance conveyors in kilometers (transport function from A to B over one or more kilometers); storage yard conveyors in a storage yard, especially in hundreds of meters (transport function from A to B); bunker discharge conveyors (material discharge function from bunkers or silos).

[0036] In one implementation of the method, the functional value is an energy performance indicator. The energy performance indicator can also be referred to as an Energy Performance Indicator (EnPI). The energy performance indicator is a key performance indicator (KPI) used to describe the performance of a belt conveyor. However, since belt conveyors vary considerably and there are numerous influencing factors and complex relationships that affect this indicator, i.e., the EnPI, comparing different belt conveyors is hardly possible without standardization. Standardization enables the comparability of belt conveyors. This means, for example, that a belt conveyor operator can determine an EnPI and then assess whether the calculated value is good or bad, as a benchmark is available.It is therefore possible to determine an energy performance indicator of a belt conveyor, in particular by using a digital twin in conjunction with real measurement data of the belt conveyor, a benchmark is determined which in particular allows the operator to immediately evaluate the performance of his belt conveyor.

[0037] For example, if a digital twin is operated using measurement data from the actual operation of the belt conveyor (e.g., throughput, speed, operating time), KPIs such as the Energy Performance Indicator (EnPI) can be determined. These performance indicators then describe the theoretical performance of the belt conveyor without considering changes from its initial state (i.e., the state shortly after initial commissioning). The effects of wear, temperature, summer or winter operation, the alignment of the belt conveyor (in the case of retractable belt conveyors), or other changes are disregarded. The actual EnPI can be determined, particularly based on measured values, and compared with the theoretical EnPI. This comparison then allows for an immediate assessment of the performance of the system under consideration, i.e., the belt conveyor.

[0038] In one version of the procedure, a technical deviation and / or a technical malfunction is detected. Examples of technical deviations are: a changed power requirement of the drives, an increased running resistance of the belt, etc.

[0039] Examples of technical malfunctions include: an unplanned shutdown and / or interruption due to damage to components, an unplanned shutdown and / or interruption due to material spills, belt misalignment, etc. etc.

[0040] One aspect of the process is the detection of procedural deviations and / or disruptions. If such deviations are detected, countermeasures can be suggested or automatically implemented.

[0041] Examples of procedural deviations include: The material supply fluctuates; the material supply is frequently interrupted; the belt only runs with a partial load; poor energy performance, as high drive power is consumed for less material; a change in operating behavior due to temperature changes (e.g., due to a low temperature; this leads to more viscous lubricant and thus to an increase in bearing running resistance).

[0042] Examples of procedural disruptions include: Frequent interruptions, which worsen the performance of the belt conveyor; frequent slowdowns of transport, which also worsen the performance of the belt conveyor.

[0043] The causes of variations and / or deviations can also lie in the system control. For example, incorrectly set limit values ​​lead to frequent malfunctions and thus to interruptions.

[0044] The determined functional values ​​and the resulting trend provide an indication of the performance and thus the condition of the belt conveyor. Information for an individual belt conveyor can be obtained by determining the functional values ​​through normalization of speed and assignment to the described load classes. This normalization allows for a comparison of the functional values ​​under identical operating conditions. Further normalization based on the lifting height also allows for a comparison of "similar" belt conveyors (similar belt conveyors are, for example, those designed according to the same standard, transporting the same goods, etc.). This enables a performance comparison (benchmarking) between belt conveyors. As described above, the functional value provides an indication of the belt conveyor's condition; however, further investigations are necessary to determine the specific cause (technical or process-related).

[0045] In one embodiment of the method, an intervention is made in the operation of the belt conveyor. This refers specifically to a real, i.e., physically existing, belt conveyor. The intervention can positively influence the performance, i.e., the efficiency, of the belt conveyor. The intervention involves, for example, a change to the mechanics and / or electrical components of the belt conveyor and / or the loading of the belt conveyor, etc.

[0046] In one iteration of the procedure, the effectiveness of the intervention is analyzed. This analysis makes it possible to gradually improve the performance of the belt conveyor. For example, the second functional value and / or another functional value are used to analyze the effectiveness of the intervention. These functional values ​​are determined or normalized according to the described procedure.

[0047] In one implementation of this method, an operator of equipment in the mining industry, particularly the operator of a belt conveyor, can evaluate the performance of the equipment under consideration and its development compared to its initial state. Changes to the equipment and their effects (e.g., new conveyor belt, replacement of idler rollers, changes to start and stop times, gearbox replacement) can be quickly assessed. The effects of implemented measures (e.g., new components in the equipment, new operating mode) become readily apparent. This method can support operators in improving the performance of their equipment and / or consciously monitoring its development. For example, the belt conveyor is dimensioned considering continuous operation at nominal load and, if necessary, taking extreme cases into account. The use of the digital twin allows for the current operating situation to be analyzed, e.g.,The loading situation and belt speed should be factored into the evaluation. This makes the results of a "what-if" analysis more realistic. In other words, how would the originally dimensioned belt conveyor behave under the current loading and speed conditions? With a sufficiently high-quality digital twin, influences from wear and tear or temperature effects could potentially be taken into account. Providing technical data, for example, for a plant maintenance technician, is another aspect of using the digital representation, and in particular a digital twin. This support service can be further enhanced by combining technical and economic data. In conjunction with the automation of bulk material handling systems, such digital solutions represent an improvement in plant operation.

[0048] A belt conveyor has, in particular, sensors for acquiring sensor data, whereby at least one sensor data point can be used to determine a functional value, and where one of the described methods can be carried out for analyzing the functional value. For example, a programmable logic controller (PLC), a control system, or a monitoring system can be used for the analysis.

[0049] In one configuration of the belt conveyor, it features a monitoring system. This monitoring system also offers the possibility of providing trend analyses. Determining and / or analyzing the operating values ​​requires data that can be provided by a monitoring system or corresponding sensors. The method for determining and / or analyzing the operating values ​​can be part of the monitoring system or installed separately.

[0050] A computer program product is intended for installation on a computing unit, wherein the computer program product is configured to perform one of the described procedures.

[0051] The invention is described and explained in more detail below with reference to the figures. The individual features of the embodiments shown in the figures can be combined by a skilled person to form new embodiments without departing from the scope of the invention. Identical reference numerals denote similar elements. The figures show: FIG 1 a method for analyzing a belt conveyor, FIG 2 another method for analyzing a belt conveyor, FIG 3 another method for analyzing a belt conveyor taking into account a monitoring system, FIG 4 a method for comparing two belt conveyors using a database, FIG 5 a method for comparing two belt conveyors using two databases, FIG 6 a first representation of a trend of energy performance indicators, FIG 7 another representation of a trend of energy performance indicators, FIG 8 a first representation for an intervention in the operation of the belt conveyor, FIG 9 another representation for an intervention in the operation of the belt conveyor, FIG 10 a sequence of processing steps under given conditions, FIG 11 function blocks in a simulation software, FIG 12 drive power and load on the belt over time, FIG 13 assignments to load classes, FIG 14 energy performance indicators over time,FIG 15 Detailed view of an example function block, FIG 16 Schematic for determining function values ​​and FIG 17 Normalized and unnormalized first and second function values ​​,

[0052] The representation according Figure 1Figure 1 shows a method for analyzing a belt conveyor 1. The belt conveyor 1 is a real belt conveyor. A normalized digital representation of the real belt conveyor is generated in a data model 3. This digital representation can also be referred to as a digital twin. In data model 3, measurement data, i.e., real values ​​13 of the real belt conveyor 1, are used, for example, for the speed of the belt conveyor and / or the load, i.e., the load on the belt conveyor. In a normalization module 5, real values ​​14 are normalized according to a speed of the belt conveyor 1 and a load class of the belt conveyor 1. Real values ​​or real measured values ​​13 are therefore, in particular, the speed and the load for data model 3. Real values ​​or real measured values ​​14 for normalization module 5 are, in particular, a measured power and the load. A result value 43 is determined using data model 3.The normalization module 5 determines a result value 44. The result values ​​43 and 44 can already be function values. The result values ​​43 and 44 are stored in a database 6. A function value, or even a further function value, can be determined using the result values ​​43 and 44. This determination takes place, for example, in database 6 or in another determination unit not shown. The function values ​​stored in the database can be visualized and / or compared in a visual representation 10. Database 6 provides visualization data 47. The representation thus offers the possibility of visualization, analysis, and / or comparison, especially of real values ​​with simulated values. Function values ​​are, for example, energy performance indicators.This results in a comparison, in particular, of the development of real-world measured energy performance indicators with energy performance indicators determined by simulation using data model 3 and real-world measured values ​​13, 14. The real-world values ​​of speed and load are the input variables for the digital representation, i.e., data model 3. The required power is calculated and normalized. By using the load, a distinction can be made between load classes. Based on this normalization according to load class and speed, the theoretical energy performance indicators for the respective load class are determined. The determined function values ​​can be plotted over time to depict a development or trend. The digital representation, i.e., the data model, describes, for example, an ideal state or initial state of the belt conveyor.Standardization is also necessary, especially for comparison with the actual belt conveyor.

[0053] The energy consumed (in particular a measured value, or calculated from measured values) is normalized according to speed and load classes, and the actual energy performance indicators for the respective load classes are determined. The difference between the results of theoretical and actual energy performance indicators describes the system's condition. A visualization of the analysis or comparison of at least one actual function value with a simulated function value is achieved, in particular, through a visual representation 10 using a visualization device, such as a screen.

[0054] The representation according Figure 2 Figure 1 shows another method for analyzing a belt conveyor 1, in which a first function value 45 is calculated in a processing module 9 from the result values ​​43 and 44. After Figure 2The data model 3, the normalization module 5, and the database 6 are implemented in a cloud 8. The cloud 8 is an internet-based system. Calculations in the cloud, as well as data storage in the cloud, can be performed centrally or decentrally. The visualization 10 of the analysis can, for example, take place on a plant, specifically where the belt conveyor 1 is located.

[0055] The representation according Figure 3Figure 1 shows another method for analyzing a real belt conveyor 1, where the real values ​​(measured values) 13 and 14 are taken from a monitoring system 15. The monitoring system 15 monitors the belt conveyor 1. In the normalization module 5, the real values ​​are normalized according to speed and load class. In the data model 3, a digital representation of the real belt conveyor is implemented, which is normalized. Normalized here means normalized according to speed and load class. The database 6 is implemented in the cloud 8. A comparison can be made using the visual representation 10. For example, the development of the energy performance indicator can be measured in reality and compared by simulation, using real values ​​as input for the simulation.

[0056] The representation according Figure 4shows a method for comparing two belt conveyors, a first belt conveyor 1 and a second belt conveyor 2, using a common database 6. According to Figure 4 compared to, for example, after Figure 1 Additionally, the data is normalized by stroke to allow for comparison between the different belt conveyors. Belt conveyor 1 is assigned data model 3', which is also normalized by stroke, and normalization module 5. Belt conveyor 2 is assigned data model 4', which is also normalized by stroke, and another normalization module 5. The actual measured values ​​13' and 14' are derived from belt conveyor 2. Data models 3' and 4' differ from the data models according to... Figures 1 to 3through additional normalization according to the stroke of the respective belt conveyor 1 or 2. In the symbol for data model 3, 3', the belt conveyor is therefore also drawn straight, due to normalization. The stroke is a measure of the gradients (positive as well as negative) that the belt conveyor has to overcome. After Figure 4 Normalization is also performed according to the stroke, in order to compare different belt conveyors, especially those with varying elevation profiles. The normalization is therefore based on speed, load class, and stroke. The use of a monitoring system is possible here as well, but is not shown. Data models 3' and 4' generate result values ​​43 and 43'. The respective normalization modules 5 generate result values ​​44 and 44'. Using the data in database 6, different belt conveyors in a factory, open-pit mine, or mine can be compared.

[0057] The representation according Figure 5 shows in contrast to Figure 4 A method for comparing two belt conveyors, 1 and 2, using two databases, 6 and 7. Belt conveyor 1 is assigned database 6, and belt conveyor 2 is assigned database 7. A first function value 45 from database 6 can be compared with a second function value 46 from database 7. Normalization is performed according to speed, load (load class), and stroke. The representation shown in Figure 10 could also be achieved using a monitoring system, which is not shown.

[0058] After the Figures 4 Thus, a comparison of different belt conveyors in an industrial plant, an open-pit mine, or a mine can be carried out, or even in different plants, different open-pit mines, or different mines. The number of databases (6, 7) can be adjusted accordingly.

[0059] The representation according Figure 6Figure 12 shows a comparison of energy performance indicators (EnPIs) as an example of functional values. The following energy performance indicators are shown at a constant speed (e.g., 50% or 80% of the rated speed) and three different load classes: I, II, and III. The values ​​(data) of the following energy performance indicators are plotted over time (date), where the values ​​have properties such as: actual, theoretical (predicted), and dimensioned (dim, which stands for dimensioning, i.e., an original design of the belt conveyor under consideration). 22 EnPi dim (load class I) kWh / (t*km) 23 EnPi dim (load class III) kWh / (t*km) 24 EnPi theo (load class II) kWh / (t*km) 25 EnPi real (loading class I) kWh / (t*km) 26 EnPi real (load class III) kWh / (t*km) 27 EnPi dim (load class II) kWh / (t*km) 28 EnPi theo (Loading class I) kWh / (t*km) 29 EnPi theo (load class III) kWh / (t*km) 30 EnPi real (load class II) kWh / (t*km)

[0060] This is an example of how to present the results of the theoretical and actual energy performance indicators over time for three load classes. According to the normalization process, these energy performance indicators are normalized according to speed (many belt conveyors operate at a constant speed, fewer at variable speed) and the load classes. The EnPi dim can also be calculated for different load classes. This value is a theoretical value based on dimensioning calculations. If the belt conveyor operates at variable speed, the normalization can be performed according to the different speeds, load classes, and, if applicable, also the stroke. Such a result presentation can be, for example, three-dimensional or using a matrix, as shown in Figure 6 not shown.

[0061] The representation according Figure 7This shows a further comparison of energy performance indicators depending on the load classes. The energy performance indicator (e.g., in kWh / t*km) is displayed over time (e.g., in minutes (min), hours (h), days (d), or months). The display distinguishes between load classes I (31), II (32), and III (33). Energy performance indicators 34, 37, and 40 are based on the original design, i.e., the dimensioning calculation of the belt conveyor. Energy performance indicators 35, 38, and 41 are theoretical, and energy performance indicators 36, 39, and 42 are real. Theoretical means they were determined in the data model, i.e., in the digital twin.

[0062] The representation according Figure 8Figure 1 shows the evaluation of the effects of an intervention 19 on a real system, namely the belt conveyor 1. The intervention 19 is based on an analysis in an analysis unit 49. In the analysis unit 49, functional values ​​are analyzed using a processing module 9 and a database 6, and an intervention 19 is generated in the belt conveyor. System data 16 is also processed in the analysis unit 49. Sensor data 11, for example, yields the measured values ​​13 and 14.

[0063] The representation according Figure 9 This shows how an intervention 19 can be analyzed. The described procedure can be used to evaluate interventions that have been carried out. Interventions are changes to the actual belt conveyor 1. By determining functional values, the effectiveness of an intervention can be evaluated. An intervention could be, for example, a lower or higher load on the belt conveyor, or a belt change.

[0064] The representation according Figure 10 This shows a sequence of processing steps under given conditions. The following elements are shown: 50: Condition A with constant speed (constant velocity) 51: Condition BI with 2000t > Load on the belt 1800t 52: Condition B II with 2000t > Load on the belt 1800t 53: Condition B III with 2000t > Load on the belt 1800t 54: Condition B IV with 2000t > Load on the belt 1800t 56: If A and BI were true then 57 : If A and B II were true then 58: If A and B III were true then 59: If A and B IV were true then 60, 61, 62,63: Integrate current drive power and conveying element 64: Calculation of the energy performance indicator (function value) for load class I 65: Calculation of the energy performance indicator (function value) for load class II 66: Calculation of the energy performance indicator (function value) for load class III 67: Calculation of the energy performance indicator (function value) for load class IV

[0065] Integrating the data yields energy [kWh] from drive power [kW] and conveying capacity [t / h] as conveying volume [t]. The energy efficiency rating is calculated by dividing the energy by (conveying volume * transport distance) [kWh / (t*km)].

[0066] The representation according Figure 11 shows function blocks 70, 71, 72 and 73 in a simulation software for determining energy performance indicators for four load classes: • I 31, • II 32 • III 33 • IV 74.

[0067] The representation according Figure 12The graph shows the values ​​77 for the drive power 75 and the load 76 on the belt over time 78, divided into load classes I, II, III and IV, for a belt conveyor. One boundary condition is a constant speed, such as 40%, 100% or 110% of the rated speed.

[0068] The representation according Figure 13 This shows the assignments to load classes. For example, load class III is considered, i.e., the time during which load 76 is in this class. From this, we can determine the drive power 75 consumed during the same time. The corresponding delivery rate is assigned to class III in order to then determine the energy performance indicator. The area under consideration in class III is highlighted in gray.

[0069] The representation according Figure 14 shows the approximately parallel course of the values ​​79 of energy performance indicators of different classes 1 to IV: 81 EnPI_I 82 EnPI_II 83 EnPI_III 84 EnPI_IV

[0070] Over time, 78. This shows a user that nothing significant has changed on the belt conveyor during this period.

[0071] The representation according Figure 15 This shows a detailed view of an example function block 80. A function block is provided for each load class. Normalization based on the stroke is taken into account, specifically a reduction by the work required for lifting. In addition to the display of the function blocks in Figure 11 The further normalization after the stroke is also shown here.

[0072] The representation according Figure 16 shows a scheme for determining function values ​​for a load class, once as a total value and once reduced by the lifting capacity, dg normalized to the lifting height (lift = 0m).

[0073] The following elements are shown: 50: Condition A with constant speed (constant velocity) 51: Condition BI with 2000t > Load on the belt 1800t 56: If A and BI were true then 85: Determination of the transported quantity 86: Calculation of the required total power 87: Calculation of the required lifting capacity 88: Integrated current delivery rate 89: Integrated current total output 90: Integrated current total power minus lifting power 91: Calculation of the energy performance indicator (function value) for load class I 92: Calculation of the reduced energy performance indicator (function value) for load class I.

[0074] Integrating the data yields energy [kWh] from drive power [kW] and conveying capacity [t / h] as conveying volume [t]. The energy efficiency rating is calculated by dividing the energy by (conveying volume * transport distance) [kWh / (t*km)].

[0075] The representation according Figure 17The diagram shows normalized and unnormalized first and second function values. A first belt conveyor 1 and a second belt conveyor 1' are represented symbolically. Real first measured values ​​14 of the first belt conveyor 1 are fed into the first normalization module 5, which has a first data model 95 for simulating the first belt conveyor 1. The first normalization module 5 determines a first result value 43. This first result value 43 can simultaneously be the first function value, which is normalized by the first normalization module 5. Real second measured values ​​14' of the second belt conveyor 1' are fed into the second normalization module 5', which has a second data model 95' for simulating the second belt conveyor 1'. The second normalization module 5' determines a second result value 43'.The second result value, 43', can simultaneously be the second function value, which is normalized by the second normalization module, 5'. The normalized function values ​​43 and 43' can be compared. This comparison through normalization is possible because the belt conveyors 1 and 1' are comparable, as symbolized by ≈ 96.

Claims

1. A method of analyzing a belt conveyor (1), wherein a data model (3) is used, wherein the data model (3) relates to a digital representation of the belt conveyor (1), wherein a first function value (45) of the belt conveyor (1) is determined, wherein the first function value (45) and a second function value (46) are used to analyze the belt conveyor, wherein the second function value (46) relates to the belt conveyor (1) or a further belt conveyor (2), wherein, for analysis, at least the first function value (45, 46) is normalized using the data model (3), wherein normalization is to a lifting height, wherein, by means of the normalization, a topology dependence of the function values is at least reduced.

2. The method according to claim 1, wherein the function values (45, 46) relate to one or a plurality of belt conveyors (2) of the same or similar design.

3. The method according to claim 1 or 2, wherein the function values (45, 46) are determined at a time interval or wherein the function values (45, 46) relate to different belt conveyors (1, 2).

4. The method according to any one of claims 1-3, wherein normalization is to an operating state.

5. The method according to any one of claims 1 to 4, wherein normalization is to a characteristic value of a material, wherein the characteristic value relates in particular to the weight and / or the amount of the material.

6. The method according to any one of claims 1 to 5, wherein the normalization relates to the first function value (45) of the belt conveyor (1) and the second function value (46) of the further belt conveyor (2).

7. The method according to any one of claims 1 to 6, wherein the belt conveyors (1, 2) differ in their length and / or conveying capacity by not more than 50%, wherein the belt conveyors (1, 2) have in particular similar transport tasks.

8. The method according to any of claims 1-7, wherein the function value is an energy performance metric.

9. The method according to any of claims 1-8, wherein a technical deviation and / or a technical fault is detected.

10. The method according to any of claims 1-9, wherein a procedural deviation and / or a fault is detected.

11. The method according to any of claims 1 to 10, wherein an intervention in the operation of the belt conveyor (1, 2) is performed.

12. The method according to any of claims 1 to 11, wherein the effectiveness of the intervention is analyzed.

13. A belt conveyor (1) having sensors for determining sensor data (9), wherein at least one sensor datum can be used to determine a function value (45), wherein a method according to any one of claims 1 to 12 can be carried out to analyze the function value (45).

14. The belt conveyor (1) according to claim 13, wherein said belt conveyor comprises a monitoring system (15).

15. A computer program product for installation on a computing unit, wherein the computer program product is configured to perform a method according to any one of claims 1 to 12.