EP4022408A1 - Procédé et dispositif pour analyser un processus de déroulement - Google Patents

Procédé et dispositif pour analyser un processus de déroulement

Info

Publication number
EP4022408A1
EP4022408A1 EP20764974.0A EP20764974A EP4022408A1 EP 4022408 A1 EP4022408 A1 EP 4022408A1 EP 20764974 A EP20764974 A EP 20764974A EP 4022408 A1 EP4022408 A1 EP 4022408A1
Authority
EP
European Patent Office
Prior art keywords
sub
sequence
data
period
variable
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP20764974.0A
Other languages
German (de)
English (en)
Inventor
Nikolai FALKE
Jan JENKE
Thomas Holm
Calvin Darian WOLTING
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wago Verwaltungs GmbH
Original Assignee
Wago Verwaltungs GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wago Verwaltungs GmbH filed Critical Wago Verwaltungs GmbH
Publication of EP4022408A1 publication Critical patent/EP4022408A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/4184—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by fault tolerance, reliability of production system
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32201—Build statistical model of past normal proces, compare with actual process
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present invention relates to a method and a device for analyzing a sequence process, in particular for analyzing a cyclical or non-cyclical sequence process, which typically has several sub-processes.
  • the first hurdle is access to the control device that controls or regulates the process of the process.
  • the focus is on already existing machines and / or systems in which access to the control device, in particular to the program logic, is reserved to the manufacturer or supplier of the machine / system. Gaining independent access to the control device is usually associated with additional costs or considerable effort for the user and user - if at all possible.
  • the present invention sets itself the task of determining and analyzing process data of a sequence process, in particular its chronological sequences and states, in order to obtain process data relating to the target and actual sequence process without having access to the actual control device, which controls / regulates the process.
  • the present invention sets itself the task of dividing a cyclical or non-cyclical sequence process into repetitive sub-processes based on ascertained process data in order to be able to evaluate the process stability with regard to the individual sub-processes of the sequence process.
  • Previously known methods such as the method known from EP 2946568 Ai for monitoring electronic and / or electrical devices, use power parameters measured / monitored via a main power cable in order to determine and reduce the energy requirement of a device.
  • a so-called NILM (Non-Intrusive Load Monitoring) method is also known.
  • the NILM method is based on the assumption that every technical device in a system generates an individual signal. These signals are recorded as the aggregated total power consumption of the system.
  • NILM algorithms pattern recognition algorithms
  • machine learning processes individual device signals are broken down, i.e. disaggregated, in the total power consumption.
  • the disaggregation enables the energy consumption of individual devices to be determined and used to optimize the system's energy efficiency.
  • the object is achieved by a method for analyzing a sequence process, the sequence process having at least one repetitive sub-process.
  • the method comprises steps a to f: a. Recording of process data of the sequence process over a reference period, b. Automatic determination of phase boundaries based on the recorded process data, c. Identifying at least one repetitive sub-process whose duration is limited in time by two adjacent phase boundaries, d. Determining at least one reference variable for each identified, repetitive sub-process from the process data recorded in the period, e. Recording of process data of the sequence process over a period of time following the reference period and repeating steps b. and c. to recognize the recurrence of an identified sub-process, f. comparing the recorded process data of the recognized sub-process with the at least one reference variable of the corresponding identified sub-process in order to determine deviations from a standard operation.
  • a sequence process to be analyzed can be a cyclical or non-cyclical sequence process.
  • the process to be analyzed has at least one repetitive sub-process.
  • the method enables the automated division of this process into sub-processes, with a sub-process being time-limited by two adjacent phase boundaries.
  • the automated division of the operational process into sub-processes comprises the process steps b. (automatic determination of phase boundaries) and c. (Identifying the repetitive sub-process).
  • the sequence process to be analyzed can have repetitive sub-processes as well as non-repetitive sub-processes, with a repetitive sub-process during the course of the
  • Sequence process can occur repeatedly. Likewise, a repetitive sub-process can only occur once during the duration of the sequence process.
  • a process to be analyzed can be, for example, a process in production or logistics.
  • the sub-processes reflect different process steps.
  • An example of a workflow in production is a repetitive task performed by a robot.
  • the sequence process can include the following three sub-processes, for example: gripping the component, changing position, releasing the component.
  • Another example of a sequence process is an injection molding process with the sub-processes: closing mold, injection, holding pressure, plasticizing, opening mold.
  • An example of a non-cyclical sequence process includes the sub-processes machine on, machine off, standby.
  • Another example of a non-cyclical flow process includes the Sub-processes room occupied, room not occupied, room occupied by several visitors. The individual sub-processes are separated from one another by phase boundaries.
  • At least one reference variable can be determined.
  • the process data and the reference variable (s) can each be variables that change over time or a set of variables that change over time.
  • the process data and the reference variable (s) can be recorded and displayed, for example, by means of curves over time.
  • the reference variable can include a lower and / or upper threshold value which stretch the limits of the normal operation of the respective sub-process.
  • the reference period can be freely selected. In the case of non-cyclical processes, the reference period can be selected, for example, until at least one repetitive sub-process has been recognized. In the case of cyclical sequence processes, the reference period can be selected to be at least equal to the period duration of the sequence process, for example.
  • the recurrence of an identified sub-process can be recognized.
  • the period following the reference period does not have to immediately follow the reference period, but can begin at any later point in time.
  • the corresponding recorded process data can be compared with the at least one reference variable of the corresponding identified sub-process. This makes possible
  • the method enables a change and / or a type of change in a repetitive sub-process to be recognized. Since the
  • phase boundaries have to be determined separately for each process process, this step is carried out automatically.
  • the discovery can enable phase boundaries to be determined as strongly as possible Phase boundary determination based on visual perception with graphically displayed process data (curve progressions) corresponds.
  • the user of the operational process does not have to manually determine the phase boundaries or the identification and recognition of repetitive sub-processes.
  • the phase boundary can be determined within a duration T.
  • individual features or their combination of the recorded process data can be analyzed. This first requires the determination of phase boundaries.
  • Phase boundaries divide phases with similar characteristics (ie sub-processes) from one another.
  • the aim of determining the phase boundaries is to achieve a classification of the sequence process that is as similar as possible, which would also be the result of a manual classification based on an optical analysis of the graphically displayed process data.
  • Y can be a one-dimensional or a multi-dimensional signal (process data), e.g. B. the power consumption or the vibration of a machine.
  • T is the period of the recurring cycle or the duration of the non-cyclical process.
  • the process Y can first be divided into K phases (partial processes), which are separated by phase boundaries: t k e [0, T], ke (0, ..., K ⁇ .
  • the individual phases can then be defined by y tk .. tk + 1.
  • the change point detection method is suitable for determining the phase boundaries the costs c m (y tk .tk + 1 ) are calculated and at total cost be summed up.
  • the cost functions c m (y tt ⁇ ) record a measured variable such as the deviation from the mean value, changes in the variance or deviations from a linear behavior within a phase Y tk..tk + 1 -
  • Different cost function models m record different measured variables.
  • Different phase boundaries are “suggested” through the use of search methods.
  • the phase boundaries t k are determined by minimizing the total costs: minV K Tn (t k , Y). tk
  • the actual analysis of the process can be started over a longer period of time t >> T.
  • it can be displayed which sub-process is currently being carried out and sub-processes can be highlighted that deviate from normal operation. This provides the user of the operational process with a starting point for a deeper analysis of the operational process.
  • the sub-process with the highest deviation from normal operation can, for example, be a focus of a subsequent analysis in order to optimize the process stability and / or process quality.
  • the determination of the phase boundaries by means of the change point detection method typically requires knowledge of the number of phases / sub-processes of the operational process, as well as knowledge of which combination of search method and cost function is suitable to describe the operational process. If, for example, different constant values are assumed in the sequence process, then a cost function is suitable for describing the degradation process, which measures the deviation from the mean value. If the number of phases / sub-processes of the sequence process is unknown, the number of phases / sub-processes of the sequence process can be determined automatically.
  • the sequence process can be a cyclical sequence process
  • the reference time period can comprise at least one, preferably at least two, period durations T of the cyclical sequence process.
  • the method can include the automated determination of the period as an additional step. As the length of the reference period increases, the reference variable can be determined more precisely, so that more reliable statements can be made about the stability and quality of a sub-process and / or at least a part of the sequence process. The automated determination of the period allows the analysis of cyclical processes with initially unknown period. Cyclical
  • Sequence processes enable particularly precise monitoring of the process stability, since the periodic process data (such as the power consumption) provide a clear reference variable, for example in the form of a reference period. Deviations This reference variable can be measured and provide information about changes in the operational process or changes in corresponding sub-processes.
  • the method can furthermore comprise the automated determination of the number of repetitive sub-processes during a period duration or an expiration time of the sequence process. In this way, it is also possible to analyze process processes whose number of sub-processes is unknown before the analysis begins.
  • the automated determination of the number of repetitive sub-processes can include at least the calculation of a difference between a reference distribution and a normalized gain value and / or the evaluation of at least one cost function.
  • a normalized gain gain ⁇ orm can first be calculated. This normalized gain describes the amount by which the total costs are reduced by adding another phase:
  • the maximum gap indicates the optimal number of phases, ie the sub-processes of the operational process. In other words, the maximum gap is at the point where inserting an additional phase is no longer worthwhile for the first time:
  • a key figure for the quality q cr (“er” for cost reduction) of the cost reduction can be determined for the automated determination of the number of repetitive sub-processes (phase division).
  • the normalized costs can be used for a phase division be calculated. In order to create comparability, this value can be averaged over all the cost functions considered.
  • M be the set of all considered cost function models and #M their power:
  • This value can be interpreted as the proportion to which the total costs of a process Y are reduced on average by a phase division T, ie based on a set of cost functions.
  • the phasing T with the lowest value for q cr can be seen as the best possible phasing.
  • z. B. Compare the phasing of different combinations of cost functions and search methods. This allows the automated selection of cost functions and search methods or the automated combination of cost functions and search methods in order to analyze process processes.
  • the key figure for the quality q cr can also be used as a cost function averaged over all models interpreted, which is summed up over all K phases.
  • This alternative does not include an automatic selection of a search method, but can be combined with the variant described above, the calculation of the quality q cr .
  • the selection of a cost function and a search method or their combination can thus be automated.
  • Different cost function models can also be used to determine the phase boundaries or to divide up phases within a process.
  • Process phases of the operational process at the beginning of the analysis of the operational process for a device which is set up to analyze the operational process may be unknown. This enables the automated analysis of operational processes.
  • the process data can be sensor data, in particular sum signals of sensor signals, particularly preferably exclusively,
  • Total power consumption data of the operational process and / or vibration data of an industrial plant The use of process data such as total signals, total power consumption data, vibration data and / or the like enables the analysis of operational processes without explicitly having access to the actual control device which controls / regulates the operational process.
  • the process data can, for example, describe the energetic balance of a machine / system whose process is to be analyzed.
  • the electrical energy fed in is converted into other forms of energy. If an actuator moves or a sensor is used, electrical energy is used for this. Therefore, for example, the operational process, including its sub-processes, can be described by means of the overall power consumption data and analyzed using this. It is not necessary to record and evaluate individual sensor signals for analysis. Instead, it is sufficient to record / evaluate a sum signal. Process processes for which only sum signals are available can thus be analyzed. The inclusion of this
  • Sum signals such as the total power consumption data, vibration data and / or the like, can be achieved easily and can be carried out inexpensively.
  • different search methods and cost functions can be used to automatically determine phase boundaries of a sequence process and to identify at least one repetitive sub-process of the sequence process. This enables a precise description of the process and the most exact possible determination of the phase boundaries.
  • the step of automatically determining phase boundaries can be carried out with the aid of change point detection methods.
  • the determination of phase boundaries with the help of change point detection methods enables an exact automatic determination of phase boundaries between sub-processes.
  • the reference variable of a sub-process can have at least one of the following variables: mean value, standard deviation, variance.
  • the reference variable can have an upper and / or lower threshold value, the reference variable and the threshold values being able to characterize normal operation.
  • the mean, standard deviation and variance can easily be determined.
  • the recorded process data of a recognized sub-process can easily be compared with these reference values of the corresponding identified sub-process in order to determine deviations from normal operation.
  • the determination of a reference variable enables the elimination and / or reduction of disturbance variables. This can be done, for example, by averaging. The so determined
  • Reference variable can for example be saved as an ideal reference period for later comparison with other process data / comparison variables.
  • the identification of at least one repetitive sub-process can include the identification of similar curve courses of the process data, with similar curve courses preferably having a specific sequence of positive and / or negative increases within predetermined tolerance ranges. Corresponding sub-processes can thus be identified quickly and reliably.
  • the method can further include determining at least one comparison variable for the identified sub-process, the comparison comprising comparing the at least one comparison variable with the at least one reference variable.
  • the determination of a comparison variable enables a simplified assessment of the process stability and / or process quality, since the comparison variable of the identified sub-process can be compared directly with the reference variable of the corresponding sub-process. The deviation between the comparison variable and the reference variable can then be used as a measure of the stability and / or quality of the partial or sequence process.
  • the comparison can include comparing the value of the at least one comparison variable at the current point in time with a value of the corresponding reference variable include earlier date.
  • the comparison variable of a sub-process can have at least one of the following variables: mean value, standard deviation, variance. Additionally or alternatively, the comparison can include comparing the value of the at least one comparison variable of the identified sub-process with the value of this comparison variable of a further corresponding sub-process during the same period of the sequence process.
  • the comparison of the comparison variable with the value of the corresponding reference variable enables an assessment of the stability and / or quality of the partial or sequence process in comparison to a reference sequence process. This corresponds to a target and actual process process comparison.
  • Sub-process with the value of this comparison variable of a further corresponding sub-process during the same period of the sequence process enables the assessment of the stability and quality of the sequence process during the execution of the sequence process. In this way, deviations from normal operation can be recognized quickly.
  • the normal operation can be determined by the reference variable and optionally by a predetermined tolerance range of the reference variable for each identified sub-process.
  • the tolerance range of the reference variable can in particular be determined by an upper and lower threshold value.
  • the results of the comparison can be displayed to the user of the operational process on a user interface, such as a graphical user interface.
  • the results of the comparison or a signal which indicates the discrepancy can be forwarded to a further control, such as the control device of the sequence process, in order, for example, to stop the sequence process or to switch to an error mode.
  • the method can furthermore include the evaluation of the process stability of the sequence process and / or at least one sub-process, based on an ascertained deviation from normal operation.
  • the process stability can be rated on a scale from 0 to 1, for example. In this case, the value 1 corresponds to a target process stability that is initially set, for example when the process data is recorded in the Reference period T ref , was determined.
  • the process stability for the corresponding sub-process to be assessed can be assessed with a value less than 1. If the process stability of the sequence process and / or sub-process falls below a predefined lower threshold value, the sequence process and / or sub-process can, for example, be stopped, maintenance can be initiated and / or a maintenance interval can be adjusted.
  • the method can further include identifying the type of deviation from normal operation.
  • the type of deviation can take place in an automated manner.
  • the type of deviation from normal operation can be identified by evaluating the course over time of the process stability of the sequence process and / or at least one sub-process. For this purpose, after each detection of a sub-process, the process stability is saved and evaluated for the run. The course of the process stability over time can then be displayed graphically in order to be able to identify the type of deviation from normal operation quickly and easily.
  • the following types of deviations can be identified: shift, drift, noise, and / or other anomalies.
  • the type of deviation can be identified by evaluating the process data, the comparison variable and / or the process stability. Depending on the sub-process, error cases in the sequence process and / or the machine can be assigned to these types of deviation, such as the failure of a part of the machine, the wear and tear of a part of the machine or a collision of a part of the machine. In particular, the evaluation of the course of the process stability over time to identify the type of deviation from normal operation enables the type of deviation to be identified quickly and easily, even over a longer observation period.
  • the object is also achieved by a device for analyzing a sequence process, the device comprising at least one sensor arrangement for recording process data of the sequence process.
  • the device is set up to carry out the method described above.
  • the device can in particular be different from the machine / system that executes the sequence process to be analyzed. This enables the analysis of processes in existing systems, such as machines or plants, by retrofitting the device.
  • the sensor arrangement can comprise a current sensor, a power consumption sensor and / or a vibration sensor. Other sensors are also possible.
  • the sensor arrangement can be set up to record at least one sum signal of the sequence process to be analyzed.
  • the device can comprise a graphical user interface which is set up to display process data, a reference variable and / or a comparison variable, wherein the graphical user interface can in particular be set up so that a user of the sequence process can display phase boundaries and / or display process data of a sub-process ( manually).
  • the recorded process data of a sub-process can be assigned a specific sequence of the process, such as gripping a component, changing position, releasing the component, and thus simplifying the assessment of the process stability and / or process quality.
  • the object is also achieved by a computer program, comprising program instructions, which can be executed by at least one processor and which cause the processor to control a device according to a method described above.
  • FIG. 1 shows a schematic representation of a device for analyzing a sequence process
  • FIG. 2 shows a schematic sequence of a method for analyzing a
  • FIG. 4 shows an exemplary representation of process data from a further sequence process
  • FIGS. 5A to 5C show an exemplary representation of process data of a further sequence process
  • 6 shows an exemplary example of a normalized gain function
  • 7A to 7D show an exemplary representation of deviations from normal operation.
  • FIG. 1 shows a schematic representation of a device 50 for analyzing a cyclical or non-cyclical sequence process Y.
  • An example of a cyclical sequence process Y is a repetitive task which is carried out by a robot.
  • the sequence process Y can include, for example, the following three sub-processes y t , k..t, k + i: Grip component y t , o..t, i, change position y t , i..t, 2 , release component y t , 2 ..t, o + T.
  • sequence process Y is an injection molding process with the following five sub-processes y t , k..t, k + i: Close mold y t, 0..t, i , injection y t , i..t, 2 , Reprint yt, 2 ..t, 3 , plasticizing y t , 3 ..t, 4, open tool y t , 4 ..t, o + T.
  • the individual sub-processes y t, k..t, k + i are each separated from one another by phase boundaries t 0 ... tk.
  • An example of a non-cyclical sequence process Y includes the sub-processes y t, k..t, k + i machine on, machine off, standby.
  • Another example of a non-cyclical sequence process Y includes the sub-processes y t, k..t, k + i room occupied, room not occupied, room occupied by several visitors.
  • the device 50 can record process data 20, 20, 20 ′′.
  • the device 50 can comprise a sensor arrangement 52 for recording process data 20, 2o, 20 ′′ of the sequence process.
  • the process data 20, 20 ‘, 20 ′′ can be a total input variable (sum signal), such as the total power consumption.
  • the process data 20, 20, 20 ′′ can also be some other sum signal, such as vibration data of an industrial plant, temperature data, noise emission data, or the like.
  • the sensor arrangement 52 can comprise at least one current sensor, a power consumption sensor, a vibration sensor, a temperature sensor, a noise emission sensor and / or other process data sensors.
  • the individual output variables 22, 24, 26, 28 of the sequence process Y (e.g. component-specific power consumption, component-specific vibration data, component-specific temperature data, component-specific noise emission data, position data of individual components, or the like) cannot be accessed by the user of the sequence process Y and / or for the device 50 and are therefore not available for the analysis of process Y.
  • the Process data 20, 20 ', 20 ′′ are recorded and analyzed according to method 100 for analyzing a sequence process.
  • FIG. 2 shows a schematic sequence of a method 100 for analyzing a sequence process.
  • the method comprises the steps (a.) Of recording 110 process data, optionally of automated determination 115 of the period duration of the sequence process, (b.) Of determination 120 of phase boundaries, optionally of automated determination 125 of the number of repetitive sub-processes, (c. ) the identification 130 of a repetitive sub-process, (d.) the determination 140 of a reference variable, (e.) the recording 150 of process data and (f.) the comparison 160 of the recorded process data in order to determine deviations from normal operation.
  • the process data 20, 20 ‘ by considering the process data 20, 20 ‘,
  • FIG. 3 shows an exemplary illustration of process data 20 of a sequence process Y which were recorded during a period T ref (reference period) and / or a period T mes (measurement period).
  • FIG. 3 shows the output variables 22 and 24, which represent, for example, the power consumption of individual components - such as individual actuators - of an industrial plant over time.
  • the process data 20 which represent, for example, the time profile of the total power consumption of the industrial plant, are recorded.
  • the output variables 22 and 24, which, for example, each represent the time profile of a component-specific power consumption of a component of the industrial plant, are not recorded and are therefore not available for the analysis of the sequence process Y.
  • phase boundaries are automatically determined and repetitive sub-processes are identified.
  • at least one reference variable is determined for each identified, repetitive sub-process.
  • further process data can be recorded during a measurement period T mes that follows the reference period T ref. In the example above, these process data would also correspond to the time sequence of the
  • the recurrence of an identified sub-process is recognized.
  • the process data recorded during the measurement period T mes can then be compared with a previously determined reference variable of the corresponding identified sub-process in order to determine deviations from normal operation.
  • the recorded process data can be sum signals, such as, for example, total power consumption data of the sequence process. The use of sum signals enables the analysis of operational processes without explicitly having access to output variables 22, 24 which, for example, represent the time profile of a component-specific power consumption of a component of the industrial plant.
  • FIG. 4 shows an exemplary representation of process data 20, 2o ‘of a further sequence process Y.
  • This exemplary representation is intended to exemplify the automatic determination of the phase boundaries based on the change point detection method.
  • Phases (ie sub-processes y tfc..tfc + 1 ) divided.
  • the phase boundaries are described by t 0 ... t 3 .
  • the cost functions measure, for example, the deviation of the signal from its mean value (here y 0 re f, yi, ref, y 2, ref) between two adjacent phase boundaries. Cost functions for further characteristics or their combination can also be used.
  • the phase boundaries result from the minimization of the function V (t; y). 4 shows a signal which represents the process data 20 which were recorded during a reference period T re f.
  • the signal shown can represent process data 20 'that were recorded during a measurement period T me s.
  • the signal 20, 20 ′ shown assumes three values in the example shown in FIG. If the cost functions measure the deviations from the mean value between two adjacent phase boundaries, a minimum of the function V (t; y) is assumed if the boundaries t k are chosen so that they are exactly at the
  • Fig. 5B shows a spike-like signal
  • Fig. 5C shows a mixed signal. These signals are suitable as input data (process data) for analyzing the sequence process.
  • Fig. 6 shows an exemplary example of the automated determination of the number of repeating sub-processes underlying calculating a difference between a reference distribution and a normalized gain value gain ⁇ orm, as has already been described above.
  • Abis 7D each show an exemplary representation of a time course of the process stability S for the partial processes y t, o..t, i , y ..t, 2 , yt, 2 ..t, o + T.
  • the process stability can be rated on a scale from 0 to 1, for example.
  • the value 1 corresponds to a target process stability that was initially determined, for example when the process data were recorded in the reference period T ref . If a deviation from normal operation is determined, for example by comparing the comparison variable with the corresponding reference variable, the process stability for the corresponding sub-process to be assessed can be assessed with a value less than 1.
  • the time curves of the process stability S for the partial processes yt, o..t, i, yt, i..t, 2 , yt, 2 ..t, o + T shown in FIGS. 7A to 7D are t over a long period of time >> T added.
  • Each point of a time curve represents the process stability of a corresponding sub-process y t , 0 ..t, i, yt, i..t, 2 , yt, 2 ..t, o + T, as it is after going through (and recognizing) the respective sub-process was evaluated.
  • a lower threshold value S min of the process stability S is shown in FIGS. 7A to 7D. If the process stability S is rated greater than Smin after running through (and recognizing) the respective sub-process, then there is no deviation or a tolerable deviation from normal operation. If the process stability S of the sequence process and / or sub-process falls below this predefined lower threshold value Smin, the sequence process and / or sub-process can, for example, be stopped, maintenance can be initiated and / or a maintenance interval can be adjusted.
  • the process stability S for the partial process y t, i..t, 2 deviates from normal operation, that is to say the process stability S is at least partially below the threshold value Smin.
  • the process stability S for the partial process y t, i..t, 2 decreases as the observation period t continues.
  • the type of deviation (here: drift) can be classified and output to the user.
  • the occurrence of a deviation of the type “drift” can, for example, indicate the wear and tear of a component which is in operation during the partial process y t, i..t, 2.
  • FIG. 7D shows an exemplary fourth case.
  • an “anomaly” occurs in the evaluation of the process stability S for the sub-process y t, 2..t, o + T.
  • the type of deviation (here: anomaly) can be classified and output to the user.
  • the occurrence of a deviation of the type "anomaly” can, for example, indicate a process or partial process that has not been optimally set. For example, there is a collision or the components involved are “caught”.
  • a deviation of the type "anomaly” can indicate an imminent failure of a component.
  • a recognized deviation and / or the type of recognized deviation is output to the user of the operational process.
  • the latter can then interpret the process data, the comparison variable and / or the process stability, in particular the course of the process stability over time, in order to draw conclusions about the deviation from normal operation, the type of deviation from Standard operation and / or the cause of the deviation from standard operation for the entire process and / or individual sub-processes.
  • the assessment of the (partial) process quality and stability can be simplified by the present invention. This can be done separately for each sub-process and / or for the entire process flow. In particular, no raw sensor data need to be interpreted to assess the (partial) process quality.

Landscapes

  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

La présente invention concerne un dispositif et un procédé permettant d'analyser un processus de déroulement, le processus de déroulement comportant au moins un processus partiel répétitif et le procédé comprenant les étapes suivantes : a. enregistrer des données de processus du processus de déroulement, pendant un intervalle de référence, b. déterminer de manière automatique des limites de phase au moyen des données de processus enregistrées, c. identifier au moins un processus partiel répétitif qui est limité temporellement dans sa durée par deux limites de phases voisines, d. déterminer au moins une grandeur de référence pour chaque processus partiel répétitif identifié, à partir des données de processus enregistrées, e. enregistrer des données de processus du processus de déroulement, sur un intervalle suivant l'intervalle de référence et répéter les étapes b. et c., de manière à identifier la récurrence d'un processus partiel identifié, f. comparer les données de processus enregistrées du processus partiel identifié avec ladite au moins une grandeur de référence du processus partiel identifié correspondant, de manière à déterminer des écarts par rapport au mode de fonctionnement normal.
EP20764974.0A 2019-08-29 2020-08-28 Procédé et dispositif pour analyser un processus de déroulement Pending EP4022408A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019213019.4A DE102019213019A1 (de) 2019-08-29 2019-08-29 Verfahren und vorrichtung zum analysieren eines ablaufprozesses
PCT/EP2020/074138 WO2021038079A1 (fr) 2019-08-29 2020-08-28 Procédé et dispositif pour analyser un processus de déroulement

Publications (1)

Publication Number Publication Date
EP4022408A1 true EP4022408A1 (fr) 2022-07-06

Family

ID=72322437

Family Applications (1)

Application Number Title Priority Date Filing Date
EP20764974.0A Pending EP4022408A1 (fr) 2019-08-29 2020-08-28 Procédé et dispositif pour analyser un processus de déroulement

Country Status (5)

Country Link
US (1) US20220179402A1 (fr)
EP (1) EP4022408A1 (fr)
CN (1) CN114341755A (fr)
DE (1) DE102019213019A1 (fr)
WO (1) WO2021038079A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102021116520A1 (de) 2021-06-25 2022-12-29 Lenze Se Verfahren zur Qualitätsüberwachung von elektrischen Antrieben
DE102022120182A1 (de) 2022-08-10 2024-02-15 Liebherr-Werk Nenzing Gmbh System, Verfahren und Computerprogrammprodukt zur automatischen Erkennung von Arbeitszyklen einer Tiefbaumaschine

Family Cites Families (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2006114011A2 (fr) * 2005-04-28 2006-11-02 Netstal-Maschinen Ag Procede et dispositif pour la surveillance automatique d'operations repetitives d'une presse d'injection
US7917362B2 (en) * 2006-04-19 2011-03-29 Mediatek Inc. Method and apparatus for determining a bit boundary of a repetition-coded signal
EP2172820A1 (fr) * 2008-10-06 2010-04-07 Basf Se Procédé et système destinés à l'analyse automatique de données de processus
WO2011116186A1 (fr) * 2010-03-17 2011-09-22 The Trustees Of Columbia University In The City Of New York Procédés et systèmes d'analyse aveugle de la consommation de ressources
US8880203B2 (en) * 2010-05-21 2014-11-04 Fisher-Rosemount Systems, Inc. On-line alignment of a process analytical model with actual process operation
CN101885230B (zh) * 2010-07-22 2014-03-12 华中科技大学 注射成形工序周期自动识别方法
US20120283885A1 (en) * 2011-05-04 2012-11-08 General Electric Company Automated system and method for implementing statistical comparison of power plant operations
ES2541277T3 (es) * 2012-12-24 2015-07-17 Air Products And Chemicals, Inc. Aparato y métodos para monitorizar y controlar unidades de proceso cíclico en un entorno estable de planta
US10552511B2 (en) * 2013-06-24 2020-02-04 Infosys Limited Systems and methods for data-driven anomaly detection
DK2946568T3 (en) * 2014-04-09 2017-02-20 Smappee Nv ENERGY MANAGEMENT SYSTEM
JP6540532B2 (ja) * 2016-02-09 2019-07-10 オムロン株式会社 監視装置及び監視装置の制御方法
US10248097B2 (en) * 2016-05-09 2019-04-02 Yokogawa Electric Corporation Method and apparatus for optimizing process control systems
CN106840686B (zh) * 2017-01-20 2018-10-23 湖南大学 一种发动机变工况下控制参数的在线检测装置和检测方法
CN108931967A (zh) * 2017-05-22 2018-12-04 大隈株式会社 运行监控装置及其控制程序
DE102017219549A1 (de) * 2017-11-03 2019-05-09 Volkswagen Aktiengesellschaft Verfahren zur Zustandsüberwachung einer Fertigungsanlage
JP6703017B2 (ja) * 2018-01-22 2020-06-03 ファナック株式会社 計測結果分析装置、計測結果分析方法及びプログラム

Also Published As

Publication number Publication date
US20220179402A1 (en) 2022-06-09
WO2021038079A1 (fr) 2021-03-04
DE102019213019A1 (de) 2021-03-04
CN114341755A (zh) 2022-04-12

Similar Documents

Publication Publication Date Title
DE102017011290B4 (de) Vorrichtung für maschinelles Lernen, CNC-Vorrichtung und maschinelles Lernverfahren zum Erkennen eines Anzeichens eines Auftretens von Rattern im Werkzeug einer Werkzeugmaschine
DE60211002T2 (de) Verfahren und system zum analysieren der steuerschaltungsleistungsfähigkeit in einem industriellen prozess
EP1543394B1 (fr) Procede et dispositif pour surveiller une installation technique comprenant plusieurs systemes, notamment une centrale electrique
DE102017211737B4 (de) Überwachungsvorrichtung und Verfahren zur Überwachung eines Systems
EP3210088B1 (fr) Procédé et système d'assistance pour la détection d'une défaillance dans une installation
WO2019137760A1 (fr) Procédé et dispositif pour la commande et/ou la surveillance d'un processus répétitif
EP4022408A1 (fr) Procédé et dispositif pour analyser un processus de déroulement
DE10241746B4 (de) Verfahren zur zyklischen Qualitätsbewertung und Prozessüberwachung bei periodischen Produktionsprozessen
DE102021124253A1 (de) Maschinelles Lernverfahren zur Anomaliedetektion in einem elektrischen System
EP3796117B1 (fr) Procédé de diagnostic et système de diagnostic pour une installation technique industrielle
DE4124501C2 (de) Neuronales Netz mit automatischer Installation von Zwischenneuronen
EP3942376B1 (fr) Procédé d'établissement d'une propriété d'une machine, en particulier d'une machine-outil, sans détection de la propriété à l'aide d'une technique de mesure
EP3151072B1 (fr) Procede et systeme de reconnaissance de defaut et de surveillance pour un composant de machine commande ou regle electroniquement
EP2928157A1 (fr) Procédé d'analyse et/ou d'évaluation d'au moins un événement au sein d'une installation technique
EP2052451A1 (fr) Procede a base de modele pour surveiller un reseau d'alimentation en energie et systeme pour mettre le procede en oeuvre
EP1431927A1 (fr) Méthode pour estimer la durée de vie résiduelle d'un appareil
DE102016215871A1 (de) Verfahren und Vorrichtung zur Bewertung eines Diagnoseergebnisses
DE102019131456A1 (de) Verfahren zum Bestimmen einer Eigenschaft einer Maschine, insbesondere einer Werkzeugmaschine, ohne messtechnisches Erfassen der Eigenschaft
DE102019125873B4 (de) Verfahren sowie sensor- und auswerteinrichtung zur dezentralen ermittlung von diagnoseinformationen über elektrische verbraucher einer maschinenanlage
DE102014009305B4 (de) Verfahren zur Diagnose eines Katalysators
DE102017216749A1 (de) Verfahren zur Bereitstellung eines Steuersignals
DE112021008086T5 (de) Zustandserfassungssystem, zustandserfassungsverfahren und zustandserfassungsprogramm
DE102021130482A1 (de) Verfahren und Vorrichtung zur Überwachung eines zyklischen Arbeitsprozesses
EP4300219A1 (fr) Procédé de détermination d'une configuration de régulateur pour un système d'entraînement, programme informatique, support lisible par ordinateur, dispositif et système d'entraînement
EP4254083A1 (fr) Procédé de paramétrage d'un système de surveillance, dispositif de paramétrage et système de surveillance

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: UNKNOWN

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20220214

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
P01 Opt-out of the competence of the unified patent court (upc) registered

Effective date: 20230522