Abstract
A method for determining and/or verifying a status of a door system includes the following steps: determining at least one item of measurement information relating to the door system in a measuring step, and determining and/or verifying the status of the door system using an artificial intelligence system and using the at least one determined item of measurement information in a status determination step.
Claims
1. A method for determining and/or verifying a status of a door system, wherein the method includes the following steps: at least one item of measurement information relating to the door system is determined in a measuring step, and the status of the door system is determined and/or verified by an artificial intelligence system and by the at least one determined item of measurement information in a status determination step.
2. The method according to claim 1, wherein the at least one item of measurement information is determined by measuring a measurement variable of the door system with a measuring device and/or wherein the at least one item of measurement information is determined by setting and/or determining an actuation variable of the door system with a control device.
3. The method according to claim 1, wherein the artificial intelligence system is trained by training data, at least partially in a training phase before the status determination step.
4. The method according to claim 3, wherein the training data comprises at least one item of model information, determined and/or output by a model.
5. The method according to claim 4, wherein the at least one item of model information relates to at least one measurement variable and/or actuation variable of the door system.
6. The method according to claim 3, wherein the training data comprises training measurement information relating to the door system and/or relating to a door device comprising the door system and/or wherein the training data comprises training measurement information relating to a further door system and/or a further door device comprising the further door system.
7. The method according to claim 3, wherein the training data includes normal operating data, wherein the normal operating data relates to a wear-free state of the door system and wherein the training data includes wear operating data, wherein the wear operating data relates to a wear state of the door system and/or a wear state of a subsystem of the door system.
8. The method according to claim 1, wherein a partial region of an operating phase of the door system is determined by the artificial intelligence system, in which a sign of wear of a subsystem of the door system and/or a sign of wear of the door system is determinable by determining the at least one item of measurement information, by determining at least one measurement variable and/or actuation variable; and/or wherein a partial region of an operating phase of the door system is determined by a model, in which a sign of wear of a subsystem of the door system and/or a sign of wear of the door system is determinable by determining the at least one item of measurement information, by determining at least one measurement variable and/or actuation variable.
9. The method according to claim 8, wherein the operating phase of the door system comprises an opening process and/or a closing process of the door system and/or of a door device including the door system, wherein the partial region of the operating phase is only one partial region of the opening process and/or of the closing process.
10. The method according to claim 1, wherein the determined and/or verified status of the door system relates to or comprises a wear status of a subsystem of the door system, and/or a wear status of a plurality of subsystems of the door system, and/or a wear status of the entire door system.
11. The method according to claim 1, wherein the door system is a partial system of a door device, and wherein the door system is a drive unit of the door device or comprises a drive unit of the door device.
12. A status determination device for determining and/or verifying a status of a door system, wherein the status determination device is configured for obtaining at least one determined item of measurement information relating to the door system, and wherein the status determination device is configured for determining and/or verifying the status of the door system by an artificial intelligence system and by the at least one determined item of measurement information.
13. A system for determining and/or verifying a status of a door system, wherein the system comprises a status determination device and the door system, wherein the status determination device is configured for obtaining at least one determined item of measurement information relating to the door system, and wherein the status determination device is configured for determining and/or verifying the status of the door system by an artificial intelligence system and by the at least one determined item of measurement information.
14. The system according to claim 13, wherein the system comprises a measuring device and/or a control device, wherein the measuring device is configured in such manner that the measuring device determines the at least one item of measurement information by measuring a measurement variable of the door system, wherein the measuring device is configured such that the measuring device provides the at least one item of measurement information to the status determination device and/or wherein the control device is configured in such manner that the control device determines the at least one item of measurement information by setting and/or determining an actuation variable of the door system, wherein the control device is configured such that the control device provides the at least one item of measurement information to the status determination device.
15. A computer program product, for determining and/or verifying a status of a door system, wherein the computer program product comprises commands, which, when the computer program product is executed by a computer, by a system according to claim 13 and/or by a status determination device for determining and/or verifying a status of a door system configured for obtaining at least one determined item of measurement information relating to the door system, wherein the status determination device is configured for determining and/or verifying the status of the door system by an artificial intelligence system and by the at least one determined item of measurement information, cause the computer to carry out a method for determining and/or verifying a status of a door system, wherein the method includes the following steps: at least one item of measurement information relating to the door system is determined in a measuring step, and the status of the door system is determined and/or verified by an artificial intelligence system and by the at least one determined item of measurement information in a status determination step.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0228] Further advantages and details of the disclosure will be explained below on the basis of the exemplary embodiments represented in the drawings. They show:
[0229] FIG. 1 a schematic representation of a system according to one exemplary embodiment of the present disclosure;
[0230] FIG. 2 a schematic representation of a system according to one exemplary embodiment of the present disclosure;
[0231] FIG. 3 a schematic representation of a training process of an artificial intelligence system according to one exemplary embodiment of the present disclosure;
[0232] FIG. 4 a schematic representation of a method for determining and/or verifying a status of a door system according to one exemplary embodiment of the present disclosure;
[0233] FIG. 5 a schematic representation of a generation of a model according to one exemplary embodiment of the present disclosure;
[0234] FIG. 6 a schematic representation of a model according to one exemplary embodiment of the present disclosure;
[0235] FIG. 7 a schematic representation of a nominal model curve according to one embodiment of the present disclosure obtained by means of a model according to the exemplary embodiment of FIG. 6; and
[0236] FIG. 8 a schematic representation of the nominal model curve of FIG. 7 in comparison with a wear-afflicted model curve according to one embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE DRAWINGS
[0237] According to one exemplary embodiment of the present disclosure, a system is schematically represented in FIG. 1. The system comprises a door device 1 having at least one door system 10. It is conceivable that the door device 1 has further door systems. The door system 10 is preferably a drive unit of the door device 1. Alternatively, it is for example possible that the door system 10 is an electric lock of the door device 1. The door system 10 typically comprises a plurality of subsystems 11, 12, 13, which can each also be understood as parts or groups of parts, by means of which the door system 10 is formed. Examples of such subsystems 11, 12, 13 of a door system 10 formed as a drive unit are a power supply, an electronic controller, a motor, a gear unit, a tab carriage, an energy accumulator (in particular a spring), etc. The door system 10 has a control device 50 or is connected to a control device 50. The control device 50 can also be understood as a subsystem of the door system 10 according to one embodiment of the present disclosure. The control device 50 is in particular configured for controlling the door system 10 or a function of the door system 10. For example, the control device 50 controls a door system 10 formed as a drive unit by means of a pulse width modulation. The control device 50 outputs an actuation variable for this purpose. It is conceivable that the actuation variable is predefined and set and/or measured or determined by the control device 50. The set and/or determined actuation variables can thereby be understood as determined measurement information 102 of the control device 50. Furthermore, the door system 10 comprises one or a plurality of measuring devices 40, 41, in particular sensors. Alternatively, it is conceivable that the measuring devices 40, 41 are formed partially or completely separately from the door system 10. One or a plurality of measurement variables of the door system 10 can be measured by means of the measuring devices 40, 41. Examples of such measuring devices 40, 41 are structure-borne sound sensors, acoustic sensors, voltage sensors, current sensors, temperature sensors, optical sensors, etc. The measuring devices 40, 41 determine measurement information 100, 101 relating to the door system 10 by measuring measurement variables. The measurement information 100, 101, 102 relates in particular to an operating phase or a partial region of an operating phase of the door system 10 or of the door device 1. The partial region 90 is in particular one or a plurality of connected or separate regions 91, 92, 93, 94 of an operating phase of the door device. The control device 50 and/or the measuring devices 40, 41 are in communicative connection with an edge device 60. It is conceivable that the edge device 60 is connected as a separate device to the door system 10 and/or to the door device 1. Alternatively, it is conceivable that the edge device 60 is arranged only in the environment of the door device 1 and/or of the door system 10. It is alternatively conceivable that the edge device 60 is built in together with the control device 50. The edge device 60 preferably comprises communication means, in particular wireless communication means, for communicating with a local network and/or a telecommunications network. In the represented exemplary embodiment, the edge device 60 comprises a status determination device 30, which has an artificial intelligence system 31. The status determination device 30 can also be formed separately from the edge device 60. For example, it is possible that the status determination device 30 is formed by means of the control device 50.
[0238] According to one exemplary embodiment of the present disclosure, a system is schematically represented in FIG. 2. Unlike the system represented in FIG. 1, a cloud 61 is shown. The cloud 61 can for example be in communicative connection with the edge device 60 via a telecommunications network and corresponding communication means. In the exemplary embodiment represented in FIG. 2, the status determination device 30 and the artificial intelligence system 31 are configured by means of the cloud 61. The measurement information 100, 101, 102 determined by the measuring devices 40, 41 and/or the control device 50 is transmitted to the cloud 61 via suitable communication means, for example by means of the edge device 60, and provided in such manner to the status determination device 30. The cloud 61 comprises a data carrier 62 and/or is in communicative connection with a data carrier 62. For example, measurement information 100, 101, 102 of the door system 10 and/or
training data 200, 201, 202 can be stored in the data carrier 62. The training data 200, 201, 202 is in particular provided for training the artificial intelligence system 31. The training data 200, 201, 202 preferably comprises model information 200 and/or training measurement information 202 determined and/or output by means of a model 20, which is measured and/or determined at one or a plurality of further door systems 10′, 10″ and/or training measurement information 201, which has been determined at the door system 10. The further door systems 10′, 10″ and/or the assigned further door devices 1′ preferably also have a communication connection with the cloud 61 and/or the data carrier 62 for transmitting such training measurement information 202. It is preferably possible that the further door systems 10′, 10″ are structurally-identical or similar systems to the door system 10.
[0239] A schematic representation of a training phase of an artificial intelligence system 31 according to one exemplary embodiment of the present disclosure is shown in FIG. 3. In the training phase, the artificial intelligence system 31 is preferably trained by means of training data 200, 201, 202 in such manner that it can detect signs of wear, in particular anomalies and/or defects of the door system 10 and/or of a subsystem 11, 12, 13 in the measurement information 100, 101, 102. Different types of training data 200, 201, 202 can thereby be used to train the artificial intelligence system 31. It is particularly preferably possible that model information 200 generated by means of a model 20 is used to train the artificial intelligence system 31. The model information 200 comprises regular and/or correct data which illustrates a wear-free good state of the door system 10 and/or anomaly data which illustrates a wear state and/or a defect of the door system 10 and/or of a subsystem 11, 12, 13, of the door system 10. The regular and/or correct data can accordingly also be understood as normal operating data which relates to a wear-free state of the door system 10. The anomaly data can accordingly also be understood as wear operating data which relates to a wear state of the door system 10 and/or a wear state of a subsystem 11, 12, 13 of the door system 10. By means of such model information 200, a broad database can be particularly efficiently achieved which enables advantageous training of the artificial intelligence system 31. By means of the model 20, wear operating data or anomaly data can thereby be targetedly generated for determined effects of wear of the door system 10 or of a determined subsystem 11, 12, 13. Therefore, the artificial intelligence system 31 can be targetedly trained for detecting determined effects of wear and for assigning detected patterns to determined subsystems 11, 12, 13. Alternatively or additionally to using model information 200 for training the artificial intelligence system 31, it is possible to use training measurement information 201 which is determined at the door system 10, in particular by means of the control device 50 and/or one or a plurality of measuring devices 40, 41. For example, as training measurement information 201, measurement information 100, 101, 102 can be used, which has been recorded in a normal state of the door system 10 and/or measurement information 100, 101, 102 can be used, which has been recorded in a wear state of the door system, and for which a determined effect of wear has been detected and has been assigned to this measurement information 100, 101, 102. Alternatively or additionally to using model information 200 and/or training measurement information 201 for training the artificial intelligence system 31, it is possible to use training measurement information 202 which has been recorded at one or a plurality of further door systems 10′, 10″, for example by means of measuring devices and/or control devices of the further door systems 10′, 10″. For example, as training measurement information 202, such information can be used, which has been recorded in a normal state of the further door systems 10′, 10″, and/or such information can be used, which has been recorded in a wear state of the further door systems 10′, 10″, and for which a determined effect of wear has been detected and assigned to this training measurement information 202.
[0240] A schematic representation of a method for determining and/or verifying a status of a door system 10 according to one exemplary embodiment of the present disclosure is shown in FIG. 4. Measurement information 100, 101 relating to one or a plurality of measurement variables of the door system 10 is recorded by means of one or a plurality of measuring devices 40, 41. Alternatively or additionally, measurement information 102 relating to one or a plurality of actuation variables of the door system 10 is determined by means of a control device. The measurement information 100, 101, 102 is thereby determined in particular during the operation of the door device 1, which includes the door system 10. The determined measurement information 100, 101, 102 is provided to a status determination device 30. By means of an artificial intelligence system (or an AI functionality) 31, the status determination device 30 analyses the measurement information 100, 101, 102 and thus determines a status of the door system 10. If signs of wear are not determined in the measurement information 100, 101, 102, a wear-free status of the door system 10 is for example determined. If, by means of the artificial intelligence system 31, wear of the door system 10 or of a subsystem 11, 12, 13 of the door system 10 is determined in the measurement information 100, 101, 102, it is conceivable that wear information 400 is output by the status determination device 30. Such wear information 400 can for example indicate an advantageous time frame and/or time for maintenance of the door system 10 determined by means of the artificial intelligence system 31 and/or include information for the subsystem 11, 12, 13 in which wear has been detected (for example also a defect). Such wear information 400 can for example be provided to a service technician and/or be used for planning future maintenance of the door system 10 and/or of the door device 1. In this way, particularly advantageous predictive maintenance is made possible.
[0241] A schematic representation of a method for generating a model 20 according to one exemplary embodiment of the present disclosure is shown in FIG. 5. A real door system 10 is analyzed for this purpose. The properties and parameters of the subsystems 11, 12, 13, 14, 15, 16, 17, 18, 19 are determined in a system influence determination step 600. The relevant system influences are preferably filtered out in a system influence filtering step 601. The door system 10 is segmented or divided into individual subsystems 11, 12, 13, 14, 15, 16, 17, 18, 19 in a system component step 602. The interactions between the individual subsystems 11, 12, 13, 14, 15, 16, 17, 18, 19 (or components) are determined and/or described in an interaction step 603. A verification is carried out in a testing step 604 of the model as to whether the model meets an accuracy criterion, i.e. in particular has a desired accuracy. If the desired accuracy is not reached, all or some of the steps 600, 601, 602, 603, 604 are carried out again, in particular until the desired accuracy is reached. If, in the testing step 604, the desired accuracy is reached, the sequence of steps 600, 601, 602, 603, 604 is terminated. Decomposition and word model formation is carried out. The model 20 preferably has individual model blocks 71, 72, 73, 74, 75, 76, 77, 78, 79 for all relevant subsystems 11, 12, 13, 14, 15, 16, 17, 18, 19. The model blocks 71, 72, 73, 74, 75, 76, 77, 78, 79 can also be understood as simulation partial models.
[0242] The following model blocks are conceivable as examples:
[0243] The first model block 71 relates to a gear unit.
[0244] The second model block 72 relates to a tab carriage.
[0245] The third model block 73 relates to a door.
[0246] The fourth model block 74 relates to a motor.
[0247] The fifth model block 75 relates to an electronic controller.
[0248] The sixth model block 76 relates to a deflection unit, in particular a deflection roller and/or a toothed belt.
[0249] The seventh model block 77 relates to an energy accumulator, in particular a spring.
[0250] The eighth model block 78 relates to a power supply.
[0251] The ninth model block 79 relates to a force transmission element, for example a toothed belt.
[0252] A number of other possibilities for model blocks are conceivable for different door systems.
[0253] A schematic representation of a model 20 according to one exemplary embodiment of the present disclosure is shown in FIG. 6. The example shows a model 20 for a door system 10 formed as a drive unit. The model 20 comprises a first model block 71 for a power supply, a second model block 72 for an electronic controller, a third model block 73 for a motor, a fourth model block 74 for a gear unit, a fifth model block 75 for a tab carriage and a sixth model block 76 for an energy accumulator (in particular a spring). Model information 200, in particular a model curve 300, is generated for a measurement variable and/or actuation variable measurable at the door system by means of the model 20. In this characteristic model curve 300, individual effects, in particular effects of wear, of the subsystems of the door system 10 observed in the model blocks are identified and assigned to the subsystems. The connections can be identified by means of the model 20 through variations in the properties and parameters of the model blocks. In this case, in particular also a partial region 90 of an operating phase of the door system 10 can be identified, in which determined signs of wear of the door system 10 or of subsystems 11, 12, 13 of the door system 10 are identifiable in measurement information. The partial region 90 can thereby comprise a connected region or two or more separate and spaced apart regions of the operating phase. The model curve 300 is for example an angular speed w of the drive shaft of the gear unit over the time t during an opening travel of the door device 1. In this exemplary embodiment, wear of the gear unit is shown in the front region of the opening travel. Increased wear of the gear unit leads to a decrease in the curve following the acceleration travel. The wear of the gear unit is thereby for example definable as follows: Wear of the gear unit=actual gear unit play−initial gear unit play. Wear of the bearing of the tab carriage for example can be noticeable by a bend of the curve in the constant travel, in particular by a decline in the angular speed with loss of the acceleration components.
[0254] A schematic representation of a nominal model curve 301 obtained by means of a model 20 is shown in FIG. 7. The nominal model curve 301 can thereby also be understood as model information 200. The nominal model curve 301 represents the angular speed w of the drive shaft of a gear unit of a door system 10 as a function of the time t for an opening process of the door device 1, which comprises the door system 10. The gear unit is a subsystem 11, 12, 13 of the door system 10. The nominal model curve 301 is in this case the curve which is obtained without signs of wear of the door system 10, i.e. in particular in a normal state or initial good state of the door system 10. Furthermore, manufacturing and/or tolerance-related deviations 302 of the door system 10 without wear are represented. Such manufacturing- and/or tolerance-related deviations can be taken into account in the model 20 for the different subsystems of the door system 10 and the entire door system 10 by means of model blocks of the model and their properties.
[0255] A schematic representation of the nominal model curve 301 of FIG. 7 (without wear) in comparison with a wear-afflicted model curve 303 is shown in FIG. 8. The partial region 90 (or the regions 91, 92, 93, 94 of the partial region 90) of the opening process of the door device 10 is identifiable by means of the model 20 or by means of the model curves 301, 303 generated by the model, in which effects of wear of individual subsystems are shown for a measurement of the angular speed w of the drive shaft of the gear unit. A first, second, third and fourth region 91, 92, 93, 94 of the partial region 90 of the operating phase are in particular represented. The wear of the gear unit can for example be determined and/or quantified via a difference D between a current value and a nominal or initial value. Oscillations are discernible in the first region 91, in particular at the beginning of the opening travel, with increasing gear unit play. The increasing gear unit play is an effect of wear of the gear unit, which is therefore detectable in the first region 91. An increased friction and increased wear of the gear unit leads, in the second region 92, to a decline in the angular speed following the end of the acceleration travel. A decline in the angular speed with increasing friction and increasing wear of the gear unit is also discernible in the third region 93 during a constant travel. Oscillations around the nominal model curve 301 are created in the fourth region 94 with increasing friction. Therefore, a partial region 90, including one or a plurality of separate regions 91, 92, 93, 94, can be determined by means of the model 20, in which signs of wear are determinable by determining measurement information 100, 101, 102 of a door system 20 and preferably being assignable to individual subsystems of the door system 10. FIG. 8 shows an exemplary embodiment of the present disclosure. A number of further exemplary embodiments are conceivable, in which for example measurement variables different to the angular speed w can be used for determining signs of wear and/or determining the partial region 90.
