How to monitor the condition of your machine tool

Abstract

The invention relates to a method for monitoring the state of a machine tool 1 having several machine axes, in which test cycles are carried out, in which at least some of the machine axes are actuated and associated state data are determined, based on which a state diagnosis is performed, in which the state data are compared with reference values, which are determined from reference state data acquired in several reference test cycles on several reference machines 2, 3, ..., n.

Description

[Technical field]

[0001] The invention relates to a method for monitoring the state of a machine tool having several machine axes.The machine tool can be a gear cutting machine, in particular a gear grinding machine, for machining toothed workpieces. [Background technology]

[0002] When machining a workpiece with a machine tool, manufacturing deviations naturally occur, which manifest themselves as tolerances between the actual shape of the actually manufactured workpiece and the specified nominal shape. Manufacturing deviations can be caused, among other things, by failures or wear of various components of the machine tool or by improper assembly of the components. For example, manufacturing deviations can be caused by drives moving the slide of the machine tool to a position other than the nominal position specified by the machine controller, by worn bearings in the spindle, or by machine parts not being properly connected to each other so that vibrations are sufficiently damped, etc.

[0003] It is therefore desirable to detect as early as possible failures or wear of machine components, mounting errors or other errors on the machine tool that may result in production deviations so that maintenance measures can be taken in time. For this purpose, it is known to carry out test cycles on the machine tool before machining of the workpiece or while machining is interrupted, in which some or all of the machine axes are systematically moved and the relevant measurements are carried out. In this process, for example, position deviations of the respective machine axes from a specified nominal position or vibration data can be recorded. On the basis of the measurement results, the state of the machine or of the individual machine axes is then evaluated. The measurement results can be compared, for example, with specified tolerance limits. If the tolerances limited by the tolerance limits are exceeded, this indicates a failure of the corresponding machine axis and maintenance measures can be initiated.

[0004] Setting tolerance limits is a very difficult task that requires a lot of expertise. Setting tolerance limits is an iterative process that is prone to errors. Moreover, since signals are usually acquired from tens to hundreds of sensors, this task can be very time consuming.

[0005] US 6,399,433 discloses a method for monitoring the machine geometry of a gear cutting machine, in which workpieces are measured with a measuring device to determine actual data. The actual data is correlated with specification data to determine deviations in the geometric settings of the machine axes. Deviations from the geometric settings for a number of workpieces are stored and a statistical evaluation of the stored deviations is performed to determine the geometric changes in the machine axes. The statistical evaluation includes a short-term evaluation and a long-term evaluation. These evaluations are correlated with each other so that process deviations are automatically detected. The method is based on measurements taken on workpieces machined with the monitored machine.

[0006] US Pat. No. 5,399,433 discloses a method for monitoring a micromachining process, in which measured values ​​are recorded during the machining of a workpiece. The measured values ​​are normalized and from these normalized values ​​parameters of the machining process are calculated which are correlated in a known manner with the machining errors of the workpiece. In this way process deviations can be detected. This document is silent about monitoring the state of machine components. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] European Patent Application Publication No. 3229088 [Patent Document 2] International Publication No. 2021048027 Summary of the Invention

[0008] In a first aspect, it is an object of the present invention to provide a method for monitoring the condition of a machine tool, which allows for an objective assessment of the condition of the machine tool (machining tool) without requiring special expertise.

[0009] This object is achieved by a method according to claim 1. Further embodiments are defined in the dependent claims.

[0010] Thus, there is provided a method for monitoring the condition of a machine tool having multiple machine axes, comprising the following steps: performing a test cycle in which at least some of the machine axes are systematically actuated and associated condition data are determined by measurements; performing a condition diagnosis comparing the condition data to at least one reference quantity; Includes.

[0011] The method is characterized in that at least one reference quantity is determined from reference condition data, the reference condition data being obtained during a plurality of reference test cycles on a plurality of reference machines.

[0012] Thus, in the proposed method, condition data can be obtained as measurement data or quantities derived from said measurement data indicative of a number of conditions of a number of machines. These machines are referred to herein as "reference machines" and the corresponding condition data as "reference condition data". The reference condition data can be stored in a database. The reference condition data has been obtained in particular by performing a number of test cycles on the reference machine while processing of the reference machine has ceased. These test cycles are referred to as "reference test cycles". The terms "reference machine", "reference test cycle" and "reference condition data" are not intended to imply that the reference machine is a particularly reliable machine, that the reference test cycle is a particularly carefully performed test cycle, or that the reference condition data is particularly reliable data. Rather, these terms are only used to logically distinguish the machine to be evaluated from the machine whose condition is utilized as a basis for comparison. The reference condition data can also include condition data obtained for the machine to be evaluated in a previous test cycle. In this respect, the machine to be evaluated can also serve itself as one of the reference machines. It is very important to note, however, that the reference condition data is not limited to condition data acquired solely using the machine being evaluated itself - rather, it is an essential aspect of the present invention to enable condition data from multiple machines to be used in the evaluation of another machine.

[0013] This is based on the premise that in practice, most of the reference test cycles are performed while the processing of the reference machine is stopped, while the corresponding reference machine is in a "good" state, i.e. in a state where it is able to produce defect-free workpieces. In practice, only a small number of test cycles will involve "bad" states, since such "bad" states are usually quickly detected and eliminated based on manufacturing deviations. Thus, in a statistical average over many reference machines and many reference test cycles, the reference state data essentially represents the "good" state of the reference machine. This knowledge is used to perform an automatic state diagnosis of the machine to be evaluated. No prior knowledge of the machine to be evaluated itself is required for this.

[0014] It is not enough to only consider the historical condition data from previous test cycles of the machine under evaluation itself. For example, the machine under evaluation may have been fitted with defective bearings from the beginning, so that the condition data obtained from this machine over its entire service life will be significantly worse than if it had been fitted with perfect bearings. Nevertheless, it may be possible in some cases to produce acceptable workpieces despite the defective bearings. Only by comparing the condition data of the machine under evaluation with reference condition data obtained by measurements on other machines, or with reference quantities derived from these reference condition data, is it possible to recognize and identify the problem with the machine under evaluation and to detect the defective bearings.

[0015] The reference machine is preferably similar to the machine tool to be evaluated. The reference machine does not have to be identical to the machine to be evaluated. In the present context, a machine is considered to be "similar" to the machine to be evaluated if the size, design and axis arrangement are generally identical. In practice, for example, machines of the same type from the same manufacturer are considered to be similar. However, machines may differ, for example, in their additional equipment.

[0016] The reference state data can in particular be obtained by carrying out the same type of test cycle with a reference machine as for the machine to be evaluated, i.e. by carrying out a test cycle in which the machine axes of the reference machine are systematically moved and reference measurements are performed, and the state data determined in the test cycle for the machine to be evaluated can then be stored again in a database itself to serve as reference state data for future test cycles of the same or another machine.

[0017] The measurement data determined in the test cycle may include position deviation data characterizing position deviations of at least some of the moving components from a nominal position specified by the machine controller and / or vibration data characterizing a vibration state of at least some of the moving components. The position deviation data may be acquired using position sensors as are well known from the prior art. The vibration data may be determined using motion sensors such as acceleration sensors as are also well known in the prior art. The measurement data may also include power data characterizing a current consumption in a drive motor of at least one moving component. Various other types of data are also conceivable. Such data may be acquired by separate sensors or may be read out directly from the machine controller.

[0018] The condition data derived from the measured quantities can include various types of data. For example, the condition data can include direct measurement data such as individual position deviations or instantaneous vibration amplitudes. However, the condition data can also include quantities generated from the measurement data by mathematical or algorithmic processing. Such condition data can for example be average values ​​of the measurement data, other statistical quantities derived from the measurement data, or quantities derived from such statistical quantities. The calculation of the condition data from the measurement data can include a spectral analysis (particularly an order analysis) of the measurement data, in particular the position deviation data, the vibration data and / or the power data. The spectral analysis is used to determine spectral intensity values ​​of the measurement data over a specified frequency range or order range. The condition data can then include spectral intensity values ​​at selected discrete frequency values ​​or orders, or quantities derived from these values, for example the sum of such intensity values ​​over a specified frequency range or order range, or the results of a peak fitting routine applied to the spectrum. The condition data can also include a complete time series and / or a complete spectrum of the measured quantity.

[0019] The status data may include specific indicators (specific status indicators) derived from measurement data from two or more sources (in particular two or more sensors) and / or from measurement data relating to the operation of two or more machine axes. Such specific indicators may make it possible to reach conclusions regarding very specific causes of errors.

[0020] In case the machine tool is a gear cutting machine, in particular a generating gear cutting machine, the condition data may also include predicted EOL data indicating at which orders excitations are expected in the EOL spectrum on the EOL test stand (EOL: end-of-line) when a toothed workpiece machined by the gear cutting machine is placed in a gear assembly and undergoes rolling motion with a mating gear in the gear assembly. The proposed method then allows automatic prediction of the orders at which noise problems are expected to occur for a workpiece manufactured by the machine being evaluated. For the discussions underlying this procedure and further embodiments, reference is made to the patent application of the same applicant entitled "Method for monitoring the condition of a gear cutting machine", filed on the same date as the present application, the contents of which are incorporated by reference in their entirety into the present disclosure.

[0021] The reference quantity can be various kinds of quantities: generally speaking, it can be the reference condition data itself, which is determined in the same way as the condition data described above, or it can be a quantity generated from the reference condition data by mathematical or algorithmic processing, in particular by statistical analysis of the reference condition data.

[0022] The reference amount may in particular include at least one tolerance limit for at least one type of condition data. In this case, the tolerance limit is automatically set by the computer based on at least one statistical reference value determined by statistical analysis of the reference condition data of the corresponding type. In this way, the tolerance limit no longer needs to be set manually, which requires a lot of time and effort, and no specialized knowledge is required for setting the tolerance limit.

[0023] The tolerance limits of the machine being evaluated are now determined by statistical analysis of the reference condition data, as described above. Knowledge of the statistical distribution of the reference condition data during a large number of previous test cycles on a large number of similar machines is used to automatically define the tolerance limits of the machine being evaluated. This is based on the assumption that the reference condition data do not only characterize a "good" condition on average, but also vary statistically in a way that is specific to the type of component or machine being considered, and therefore variations with similar statistical properties can be expected in the machine being evaluated.

[0024] In particular, an expectation value of the reference condition data and an indicator of the variance (or equivalently, the standard deviation) of said reference condition data can be calculated as statistical criteria. Acceptable limits for the corresponding condition data of the monitored machine can then be set symmetrically around the expectation value, for example at a distance corresponding to a predetermined multiple of the standard deviation.

[0025] The test cycle can be repeated several times at different times, during which the workpiece is machined with the machine tool and during which the test cycle is performed during a break in the machining process when the machine tool is not in machining engagement with the workpiece. For example, wear or failure of machine tool components may occur during machining. To better detect this, the condition diagnosis may include a comparative evaluation of condition data from several test cycles with at least one reference quantity.

[0026] In particular, the comparative evaluation can include a comparative statistical evaluation. determining at least one statistical value of condition data obtained from a plurality of test cycles; comparing the statistical value to at least one reference quantity; Includes.

[0027] In this way, the statistical variation of the condition data from one test cycle to the next can be made available for analysis in particular. For example, a strong variation in the value of the condition data may indicate a component failure even if the average value of this condition data over several test cycles does not indicate an anomaly. In this respect, a measure of the distribution of the values ​​of at least one type of condition data from several test cycles can serve as a statistical value.

[0028] In an advantageous embodiment, in order to detect the imminent failure of a machine component in time, the temporal evolution of the machine's condition as a function of time or as a function of the number of processed workpieces is analyzed as part of the condition diagnosis. For this purpose, the evolution of the condition data obtained from a number of test cycles can be analyzed as a function of time or as a function of the number of processed workpieces, and the result of this analysis can be compared to at least one reference quantity. In particular, the analysis of this evolution can include an extrapolation of future values ​​of the condition data. For the extrapolation, a regression analysis of the condition data can be performed, for example, with a polynomial function, in particular a quadratic function, and the result of this regression analysis can be compared to at least one reference quantity in order to predict, for example, the expected time of failure of the component. This approach is particularly beneficial when the extrapolated condition data are condition data that are directly correlated with the quality of a particular component. In this way, the imminent failure of a component can be predicted at an early stage and appropriate measures can be taken before a failure occurs ("predictive maintenance").

[0029] In some embodiments, for condition diagnosis, the reference condition data stored in the database can be classified into at least two condition classes (e.g. "good" and "bad", or in more sophisticated variants, "new condition", "medium condition", "unsafe condition" and "defective condition"). Then, for each of the condition classes, at least one statistical reference value is calculated from the reference condition data, and for condition diagnosis, the condition data is compared with the statistical reference values ​​of the at least two condition classes. In this way, evaluation parameters can be determined that allow a differentiated evaluation of the condition of the machine or its components.

[0030] Claim 8 Depending on the result of the condition diagnosis, an action can be triggered. For example, a diagnostic message can be issued to a user (e.g., a maintenance person). This diagnostic message can be sent via the network to a terminal device that is spatially separated from the machine tool and can be output at this terminal. This can be done, for example, by messaging services such as SMS or WhatsApp, push messages, or by email. For example, a diagnostic message about a selected component and / or the overall condition of the monitored machine can include evaluation parameters that can indicate two, three, four or more discrete values, for example "good" and "bad", or in more differentiated embodiments, "good", "medium", "dangerous" and "faulty". The results of the condition diagnosis can be visualized accordingly using the terminal device. The terminal device can be, for example, a desktop computer, a notebook computer, a tablet computer, or a smartphone. This makes it possible to monitor the condition of one or more machines from anywhere.

[0031] Additionally or alternatively, depending on the result of the condition diagnosis, at least one process parameter, e.g., the rotational speed of the spindle, may be automatically modified during machining of the workpiece on the machine tool, process recommendations may be automatically issued to the user of the machine tool, or in extreme cases, further machining may be automatically stopped.

[0032] The condition diagnosis may include a comparative statistical analysis of the condition data with reference condition data for at least two different types of condition data to identify the condition of different components. Some types of condition data, such as the spectral intensity of vibration signals at different frequencies, may be affected by wear of two components, but in different ways. By performing a comparative statistical analysis of the condition data with the reference condition data for these two types of condition data, a conclusion can be made regarding the component in a wear state that is responsible for the determined condition indicator.

[0033] As already mentioned, the reference state data is preferably stored in a database. This database can be located remotely from the machine being monitored. This database can also be implemented in the cloud, for example in the form of a computing resource shared by several users as a service. An evaluation computer can access the database to perform the analysis of the state. This evaluation computer is preferably located spatially away from the machine tool. This evaluation computer is connected to the machine tool by a network connection. This evaluation computer also does not have to be a single physical device, but can be implemented in the cloud. The terminal communicates with the evaluation computer via a network, in particular the Internet.

[0034] The invention also provides an apparatus for monitoring the state of a machine tool having multiple machine axes, the apparatus comprising a processor and a storage medium having stored thereon a computer program which, when executed on the processor, performs the following steps: - actuating at least some of the machine axes during a test cycle of the machine tool, performing associated measurements, and receiving state data determined by the measurements; performing a condition diagnosis in which the condition data is compared to at least one reference quantity; Run The at least one reference quantity is determined from reference condition data, the reference condition data being obtained during a plurality of reference test cycles on a plurality of reference machines.

[0035] The above statements relating to the method according to the invention also apply mutatis mutandis to the device according to the invention.

[0036] The present invention further provides a corresponding computer program, which can be stored on a non-volatile storage medium.

[0037] Preferred embodiments of the present invention will now be described with reference to the drawings, which are intended to illustrate, but not to limit, these preferred embodiments of the present invention. [Brief description of the drawings]

[0038] [Figure 1] FIG. 1 is a schematic diagram of a generating grinding machine. [Diagram 2] FIG. 2 shows the analysis of the measurement data. [Diagram 3] FIG. 3 is a schematic diagram of a network having several similar generating grinding machines communicating with a database via a service server. [Figure 4]FIG. 4 shows a statistical distribution of values ​​of a reference condition indicator. [Diagram 5] FIG. 5 is a diagram showing a state diagnosis according to the first example. [Figure 6] FIG. 6 is a diagram showing a condition diagnosis according to the second example. [Figure 7] FIG. 7 is a flow chart of a method for monitoring a generating grinder. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] [Example structure of a generating grinding machine] FIG. 1 shows an example of a machine tool in the form of a gear generating grinding machine 1, which will hereinafter also be referred to as the "machine" for short. The machine 1 has a machine bed 11, on which a tool carrier 12 is guided displaceably along a radial feed direction X. The tool carrier 12 holds an axial slide 13, which is guided displaceably along a feed direction Z relative to the tool carrier 12. A grinding head 14 is attached to the axial slide 13 and can be pivoted about a pivot axis extending parallel to the X direction (so-called A-axis) to match the helix angle of the gear to be machined. The grinding head 14 also holds a shift slide, which allows the tool spindle 15 to be shifted along a shift direction Y relative to the grinding head 14. A worm-shaped toothed grinding wheel (grinding worm) 16 is clamped to the tool spindle 15. The grinding worm 16 is driven by the tool spindle 15 to rotate about a tool axis B.

[0040] The machine bed 11 is also fitted with a swiveling workpiece carrier 20 in the form of a turret which can be swiveled between at least three positions about the swiveling axis C3. Two identical workpiece spindles are mounted diametrically opposite each other on the workpiece carrier 20. Of these workpiece spindles, only one workpiece spindle 21 with an associated tailstock 22 is visible in FIG. 1. A workpiece is clamped on each of the workpiece spindles and can be driven to rotate about the workpiece axis C1 or C2. The workpiece spindle 21 visible in FIG. 1 is located in a machining position in which a workpiece 23 clamped on it can be machined with a grinding worm 16. The other workpiece spindle, offset by 180° and not visible in FIG. 1, is located in a workpiece exchange position in which a finished workpiece can be removed from it and a new raw part can be clamped on it. A dressing device 30 is mounted offset by 90° to the workpiece spindles.

[0041] The machine 1 thus has a large number of movable components, such as slides or spindles, which can be moved under the control of corresponding drives, which are often referred to in the technical world as "NC axes" or "machine axes", or simply as "axes". In some cases, this designation also includes the components that are driven by drives, such as slides or spindles.

[0042] The machine 1 also has a number of sensors. By way of example, only two sensors 18, 19 are shown diagrammatically in FIG. 1. The sensor 18 is a vibration sensor which detects vibrations of the housing of the grinding spindle 15. The sensor 19 is a position sensor which detects the position of the axial slide 13 along the Z direction relative to the tool carrier 12. However, in addition to these, the machine 1 also comprises a number of further sensors. These sensors include, in particular, a further position sensor which detects in each case the actual position of one linear axis, a rotation angle sensor which detects in each case the rotational position of one rotary axis, a current sensor which detects in each case the drive current of one axis and a further vibration sensor which detects in each case the vibrations of one driven component.

[0043] All driven axes of the machine 1 are digitally controlled by a machine controller 40. The machine controller 40 comprises several axis modules 41, a control computer 42 and a control panel 43. The control computer 42 receives operator commands from the control panel 43 and sensor signals from various sensors of the machine 1 and calculates therefrom control commands for the axis modules 41. The control computer 42 also outputs operating parameters for display on the control panel 43. The axis modules 41 provide at their outputs the control signals for one machine axis each.

[0044] A monitoring device 44 is connected to the control computer 42 .

[0045] The monitoring device 44 can be a separate hardware unit associated with the machine 1. The monitoring device 44 can be connected to the control computer 42 via an interface known per se, for example the known Profinet standard, or it can be connected to the control computer 42 via a network, for example the Internet. The monitoring device 44 can be spatially part of the machine 1 or it can be located spatially remote from the machine 1.

[0046] The monitoring device 44 receives various different measurement data from the control computer 42 during operation of the machine. Among the measurement data received from the control computer are sensor data obtained directly by the control computer 42 and data read out by the control computer 42 from the axis modules 41, including for example data indicating the target positions of the various machine axes and the target current consumption in the axis modules.

[0047] The monitoring device 44 may optionally have its own analog and / or digital sensor inputs for directly receiving sensor data as measurement data from further sensors, which are typically sensors not directly required for the control of the actual machining process, for example acceleration sensors for detecting vibrations or temperature sensors.

[0048] The monitoring device 44 can alternatively be implemented as a software component of the machine controller 40, for example running in a processor of the control computer 42, or can be designed as a software component of a service server 45, which will be described in more detail below. In Fig. 1 a processor 451 and a memory device 452 of the service server 45 are shown accordingly.

[0049] The monitoring device 44 communicates with a service server 45 either directly or via the internet and a web server 47. The service server 45 in turn communicates with a database server 46 having a database DB. These servers may be located remotely from the machine 1. The server does not have to be a single physical device. In particular, the server may be implemented as a virtual unit in the so-called "cloud".

[0050] The service server 45 communicates with a terminal 48 via a web server 47. The terminal 48 is capable of running, among other things, a web browser for visualizing the received data and their evaluation. The terminal does not require any particular computing power. For example, the end device can be a desktop computer, a notebook computer, a tablet computer, a mobile phone, etc.

[0051] [Workpiece lot processing] For completeness, the following describes how a workpiece is machined using machine 1.

[0052] To process a workpiece (unmachined part) that has not yet been machined, the workpiece is clamped by an automatic workpiece changer to a workpiece spindle in a workpiece exchange position. The workpiece exchange takes place in parallel with the processing of another workpiece in another workpiece spindle in a processing position. Once the new workpiece to be processed has been clamped and the processing of the other workpiece has been completed, the workpiece carrier 20 is swiveled 180° about the C3 axis so that the spindle with the new workpiece to be processed moves to the processing position. Before and / or during the swiveling process, a meshing operation is carried out with the associated meshing probe. For this purpose, the workpiece spindle 21 is rotated and the position of the tooth space of the workpiece 23 is measured with the meshing probe 24. The rolling angle is determined on this basis.

[0053] When the workpiece spindle holding the workpiece 23 to be machined reaches the machining position, the workpiece 23 is brought into collision-free meshing with the grinding worm 16 by moving the tool carrier 12 along the X-axis. The workpiece 23 is then machined by the rollingly meshed grinding worm 16. During machining, the workpiece is continuously advanced along the Z-axis with a constant feed in the radial direction X. In addition, the tool spindle 15 is slowly and continuously moved along the shift axis Y (so-called shift movement) in order to continuously use unused areas of the grinding worm 16 for machining.

[0054] In parallel with the workpiece machining, the finished workpiece is removed from the other workpiece spindle and another blank is clamped onto this spindle.

[0055] If, after machining a certain number of workpieces, the grinding worm 16 has become too dull and / or the tooth flank shape too inaccurate due to continued use, the grinding worm is dressed. For this purpose, the workpiece carrier 20 is swiveled ±90° so that the dressing device 30 reaches a position opposite the grinding worm 16. The grinding worm 16 is then dressed using the dressing tool 33.

[0056] [Test Cycle] While machining is halted, the monitoring system 44, in conjunction with the machine controller 42, executes test cycles in which the condition of individual or all components of the machine 1 is checked. During such test cycles, selected some or all of the machine axes are systematically actuated and measurements are made on the machine.

[0057] For example, each linearly movable component moves with an associated machine axis, and the current instantaneous position of the component is determined continuously or for selected positions using the position sensors mentioned above. From this process, the position deviation between the specification (nominal position) and the measurement (actual position) is determined and transmitted to the monitoring device 44. The same can be done for a rotationally driven spindle, in which case a rotation angle sensor is used to determine the position deviation.

[0058] The vibration behavior of selected components (especially slides and spindles) is also determined while the components in question are driven by the assigned machine axes. Vibration sensors connected to these components are used for this purpose. The results of the vibration measurements are also transmitted to the monitoring device 44.

[0059] Furthermore, the power consumption of the drive motors of the machine axes is determined: for example, current sensors integrated in the axis modules 41 can be used for this purpose. In addition, the temperature of the drive motors and other measured quantities can also be determined.

[0060] All this can be done while one machine axis is operated alone. However, it is also possible to operate two or more machine axes in combination so that the behavior of the machine when they are operated simultaneously is recorded. In this case, for example, amplified vibrations may occur that are greater than would be expected based only on the vibration behavior when a single machine axis is operated, or controller errors may be detected that can only be determined when two machine axes are operated synchronously.

[0061] In addition, it is conceivable to purposefully generate vibrations and record the response of the various machine components in order to investigate the damping behavior of the machine. From such investigations, conclusions can be drawn about the quality of the joints between the machine components. In particular, automatic frequency response measurements can be performed.

[0062] The monitoring device 44 determines various status data from the received measurement data, which make it possible to draw direct or indirect conclusions about the state of the machine or its individual components.

[0063] The condition data is obtained by selecting from the measurement data and / or by mathematically processing and analyzing the measurement data. Some examples of condition data are given below.

[0064] a) Basic indicators A particular type of condition data, obtained by selecting or mathematically analyzing signals from a single sensor, that allows a conclusion to be drawn regarding the condition of a single component, is referred to below as a basic indicator.

[0065] An example of a basic indicator is a position deviation indicator, which can be, for example, a single measured position deviation or an average of several measured position deviations for the same component at different nominal positions. The position deviation indicator directly indicates the positioning accuracy of the associated component.

[0066] Another example is the maximum current consumption of a drive motor during a movement process, which makes it possible to draw conclusions, for example, regarding excessive friction or jamming of the associated machine shaft.

[0067] A third example is the average amplitude (e.g. RMS value) of the signal from a vibration sensor during the movement process. The average amplitude makes it possible to draw direct conclusions about the vibration tendency of a component.

[0068] Certain vibration indicators obtained from the spectral analysis of the vibration signal of a single moving process can also be called basic indicators. In particular, the spectral intensities at selected discrete excitation frequencies or excitation orders can be obtained. These intensities can directly serve as basic indicators, or the basic indicators can be calculated from these intensities by simple mathematical operations, such as addition or averaging.

[0069] This is shown in FIG. 2 by way of example with a time signal from a vibration sensor connected to the tool spindle and a spectrum that can be obtained from the time signal by filtering and FFT operations. The monitoring device can, for example, calculate the RMS amplitude from the time signal. The monitoring device can also evaluate the spectrum near a number of discrete frequency values ​​to determine the strength of the spectrum at those frequency values. These discrete frequency values ​​can, for example, be certain multiples ("orders") of the workpiece rotation speed. The spectrum in FIG. 2 includes several clearly visible peaks at such frequency values.

[0070] For example, strong peaks at the tool rotation speed and its integer multiples (i.e. integer orders) may indicate eccentricity in the tool spindle. Peaks at certain integer or non-integer multiples (integer or non-integer orders) of the tool rotation speed may indicate bearing damage in the tool spindle. If the bearing orders are known, it may be possible to identify the affected bearings from the orders of the peaks. In some cases, assignment to individual defect patterns can only be made by differential diagnosis. For example, it would be possible to draw a conclusion as to which component of the machine is responsible for the peaks simply by analyzing the relative intensity ratios of the peaks to one another.

[0071] In the simplest case, a comprehensive basic indicator of the entire component can be obtained by simply adding up the intensities of the peaks in a certain frequency range or order range. This does not allow drawing conclusions about the individual causes of the component's poor condition (such as eccentricity or bearing damage), but it may be sufficient to detect failures of this component in the first place and to initiate appropriate maintenance measures.

[0072] Instead of determining the intensities of the individual peaks and using these as basic indicators, it is also conceivable to use all the values ​​of the complete spectrum as state quantities.

[0073] b) Specific indicators A specific indicator may be condition data obtained from a mathematical or algorithmic combination of measurements from various sources (particularly various sensors) or from a single sensor when two or more machine axes are actuated, e.g., when the machine axes move in tandem. Such condition indicators may enable very specific conclusions to be drawn regarding the cause of the condition in question, but require specific knowledge of the interaction of the individual components of the machine.

[0074] An example of such a specific indicator is a state quantity resulting from a calculation including, on the one hand, the average current consumption of the drive motor of the linear axis and, on the other hand, the spectral intensity of the acceleration sensor over a wide frequency range. Such an indicator can, for example, make it possible to narrow down the cause of increased friction of the linear axis in question (for example a worn ball screw drive).

[0075] Another example of such a specific indicator is a state quantity determined for the coordinated motion of the tool spindle and shift slide by performing the following calculation:

number

[0076] An overall status indicator for an overall assessment of a component can also be formed from all status data characterizing the component. In this way, the status of each component is represented by only one indicator. If one overall status indicator indicates a problem, troubleshooting can be performed using the individual status quantities.

[0077] Correlations that allow for the calculation of such specific indicators are often only revealed through data analysis of very large data sets across many machines (e.g., correlation analysis of known damage patterns with assigned basic indicators). Specific indicators are often specific to a particular type of machine and cannot be easily transferred to other types of machines.

[0078] [Database] The function of the database DB will now be described with reference to Fig. 3. A machine 1 to be monitored and a number of similar machines 2, 3, ..., n are connected via a web server 47 to a service server 45 and to a database server 46 having the database DB.

[0079] Each of these machines is equipped with a monitoring device which continuously transmits certain data to a database DB during operation of the respective machine. This data includes, in particular, a unique identifier of the machine, a time stamp and a number of status data as described above. This data can optionally also include further data, for example data relating to the workpieces processed following a test cycle, for example an indicator of the workpiece quality achieved.

[0080] These data are stored in a database DB. As a result, over time, the database contains a very large amount of condition data obtained for several similar machines in many different test cycles. These condition indicators are referred to below as reference condition data.

[0081] [Evaluation of Reference Condition Indicators] The reference state variables can be statistically evaluated. Such a statistical evaluation can be carried out in particular in order to ascertain a typical change behavior of the reference state variables and, on the basis of this, to define tolerance limits for the state variables of the monitored machine. The changes in the state variables over the life cycle of the machine can also be statistically evaluated, and the current state variables of a particular machine can be compared with the reference state variables stored in a database to automatically obtain, for example, indications of component wear.

[0082] This will be explained in more detail below using some examples.

[0083] a) Automated setting of tolerance limits 4, the following is an example of how the data in the database can be used to set tolerance limits for the condition data of the monitored machine 1. The corresponding calculations can be performed by the service server 45.

[0084] The database contains reference condition data values ​​for many test cycles on many similar machines. It can be assumed that these values ​​were mostly obtained on machines that were operating without defects, since defects will usually be detected and removed sooner or later. In this respect, it can be assumed that the reference condition data values ​​have a statistical distribution essentially as would be expected for defect-free machines, with only a few statistical outliers due to machines with worn components.

[0085] FIG. 4 shows an example of a distribution of values ​​of any type of reference condition data. On the horizontal axis, the values ​​of the reference condition data are plotted, and on the vertical axis, as a bar graph, the relative frequency of equal value intervals ("bins") is plotted. It can be seen that the distribution of the values ​​of the reference condition data in this example essentially corresponds to a normal distribution, the density function of which is also plotted in FIG. 4 as a dotted line. The distribution in FIG. 4 is characterized by an expectation value μ R and the standard deviation σ R or variance σ R 2 and

[0086] The term "expected value" is used herein synonymously with the term "sample mean." The term "variance" is used herein to indicate the mean squared deviation of a sample's values ​​from the sample mean. The "standard deviation" is the square root of the variance.

[0087] Based on this statistical distribution, the lower tolerance limit LL and the upper tolerance limit UL of the corresponding condition data of the monitored machine can be automatically determined. For this purpose, an appropriate density function (here, the density function of the normal distribution) is fitted to the distribution of the values ​​of the reference condition data to obtain the expected value μ R and standard deviation σ R In practice, the more reference state data there are in the database, the more accurate the fitting will be. R The range [μ R -p σ R ,μ R +p σR ], where the coefficient p is a positive real number that indicates how many standard deviations the tolerance limit is from the expected value. According to the well-known Six Sigma 6σ (which is however usually used for different purposes), we can choose, for example, p=6. If the customer requires less strict tolerances, we can choose a larger coefficient p.

[0088] At each future test cycle, the service server 45 then compares the relevant status data with the tolerance limits LL, UL. In Fig. 2 such tolerance limits are depicted diagrammatically for several types of status data. When the value of the status data falls outside the tolerance range, the service server 45 triggers appropriate action. For example, the service server 45 can send an SMS, a push message or an email to maintenance personnel. Optionally, the service server can also affect future machining operations or temporarily stop machining on the machine 1.

[0089] b) Defining state classes It is conceivable to classify the values ​​of the reference condition data into two, three, four or more condition classes so that a more differentiated assessment of the condition of the component can be made. This can be done simply on the basis of the values ​​themselves or on the basis of further information. For example, an analysis of the reference condition data can show that there is always a point in time when the reference condition quantity suddenly shows a "better" value. It can then be concluded that this sudden improvement is the result of maintenance or replacement of the component.

[0090] Such an event can be easily identified in the totality of the reference condition data, and the reference condition data values ​​for a certain number of test cycles immediately following such an event can be classified as class A, indicating a new condition, while the reference condition data values ​​for a certain number of test cycles immediately preceding such an event can be classified as class C, indicating a dangerous condition. Reference condition data values ​​between class A and class C can be sorted into class B, indicating an average use condition, and outliers in the condition data that are "worse" than the class C values ​​can be classified as class D, indicating a defective condition.

[0091] The classification into different condition classes can also be based on criteria other than a sudden change in the value of the reference condition data. For example, it is conceivable to store information directly in the database about the number of machining operations already performed with the component, about the operating time of the component in question, or about the quality of the workpieces produced with the machine after an inspection cycle. This information can then be taken into account to perform the classification into condition classes. A corresponding classification can be performed, for example, using machine learning algorithms (ML algorithms).

[0092] The values ​​of the reference condition data can be statistically analyzed for each of the condition classes separately, for example, expectation and variance can be determined separately for each condition class.

[0093] To draw a conclusion regarding the wear state of the component, the current values ​​of the state quantities can be compared, for example, with expected values ​​of corresponding reference state quantities of various state classes.

[0094] c) Consideration of state data from several test cycles: extrapolation and statistical analysis By considering values ​​of condition data from different test cycles, it is possible to characterize the condition of a component more adequately than by considering a single value.

[0095] FIG. 5 shows an example of the value of a state quantity Z as a function of time or as a function of successive test cycles. The value of Z changes with each test cycle. Initially, the value of Z is the value μ A This value is the expected value of the corresponding reference state quantity of state class A. Therefore, we can conclude that the component whose state is characterized by state quantity Z is initially in a nearly brand new condition. However, over time, the instantaneous value of Z increases and initially approaches μ, which is the expected value of the corresponding reference state quantity of state class B. B Then, the expected value of the corresponding reference state quantity of state class C, μ C It can therefore be concluded that the component in question has changed from an average operating condition to a dangerous condition. In order to predict the time of failure of the component, an extrapolation of the value of Z to the future can be performed. This can be done, for example, by means of a regression analysis. In Fig. 5, as an example, a quadratic regression curve is plotted as a dashed curve. The predicted time of failure t 0 can in this case be estimated, for example, as the point in time at which this curve reaches the range of normal values ​​for condition class D. This type of analysis allows predictive maintenance of components before they fail ("predictive maintenance").

[0096] It is also conceivable to determine the values ​​of the state quantity over several test cycles, perform a statistical analysis of the totality of the values ​​thus collected and compare the distribution of these values ​​with the distribution of values ​​of a reference state quantity.

[0097] In the simplest case, the instantaneous expectation of the state variable can simply be determined from the collected values ​​and compared to the expectation of a reference state variable. The "instantaneous expectation" is the expectation over a certain number of test cycles.

[0098] Instead of comparing expected values, other statistical parameters can be compared. For example, for each condition class, the corresponding variance or standard deviation of the values ​​of the reference condition quantities can be determined. In many cases, as a component wears, not only does the expected value of the corresponding condition quantity change, but its variance also increases. Thus, by monitoring the variance or standard deviation, it is possible to draw conclusions regarding the wear state of the component.

[0099] This is shown exemplarily in Fig. 6, where the time course of the instantaneous standard deviation σ of the state quantity is plotted. 0 It can be seen that there is a sudden strong increase in the standard deviation around , which approximately corresponds to the standard deviation of the reference state quantity in state class D. This indicates a sudden failure of the corresponding component.

[0100] In this case, monitoring the "standard deviation" or "variance" of the statistical parameters can give indications of a component failure even if the expected value of the corresponding state quantity has not changed at all. In this respect, statistical analysis makes it possible to detect imminent or actual failure of a component much more reliably than if only individual values ​​were monitored.

[0101] Instead of a simple statistical analysis of the kind described above, a classification algorithm can also be used, for example correlating a particular set of state variables with reference state variables, in order to draw a conclusion regarding the state of the component. Again, ML algorithms can be used for this purpose.

[0102] d) Output and visualization of results The results of the automatic component diagnostics can be easily visualized, for example by means of a traffic light system, where the status of each component is evaluated individually as green (good), yellow (needs attention) or red (bad). Depending on the component status an evaluation of the status of the whole machine can likewise be made. This allows a very easy overview of the status of the machine and its components. Indications of impending failures can also be output in the sense of "predictive maintenance".

[0103] By clicking on one of the components, one can easily visualize the relevant data that led to the corresponding rating.

[0104] The visualization can be done platform-independently on any end device via a web browser. Other evaluation measures can be provided correspondingly platform-independently. This facilitates analysis even remotely. In particular, the status of any machine can be viewed in detail from any mobile device via the cloud.

[0105] In addition, as already explained above, when a situation exists that requires intervention, it is conceivable to automatically send a corresponding message via SMS, push message or email.

[0106] [flowchart] FIG. 7 shows an exemplary flow chart summarizing a method for monitoring the condition of a generating grinder.

[0107] Tolerance limits for the state variables are first defined in block 110. For this purpose, reference state variables of comparable processing situations are retrieved from a database in step 111 and statistically analyzed in step 112. On the basis of this statistical analysis, tolerance limits are set in step 113.

[0108] A test cycle is then performed in block 120 using these tolerance limits with subsequent condition diagnostics. The machine components are moved (step 121) and during this process measurement data is continuously acquired (step 122). State quantities are generated from the measurement data (step 123) and sent to a database for storage (step 124). In step 125, the state quantities are compared to the tolerance limits and based on this comparison an action is triggered, such as, for example, a graphical output of a condition assessment of the component.

[0109] In block 130, future failures of machine components are predicted. For this purpose, current state quantities are extrapolated to obtain future state quantities (step 131). In step 132, the extrapolation results are compared with statistical values ​​or tolerance limits of reference state quantities, and based on this comparison, actions are triggered, such as, for example, outputting a predicted time of failure.

Claims

1. A method for monitoring the state of a machine tool (1) having multiple machine axes, comprising: performing a test cycle in which at least some of the machine axes are actuated and associated status data (Z) are determined by measurement; The condition data (Z) is compared with at least one reference quantity (LL, UL; μ Ｒ , σ Ｒ ; μ Ａ -μ Ｄ ;σ Ａ -σ Ｄ ) performing a condition diagnosis comparing the Including, At least one of the reference quantities (LL, UL; μ Ｒ , σ Ｒ ; μ Ａ -μ Ｄ ;σ Ａ -σ Ｄ ) is the reference state data (Z Ｒ ) and the reference state data (Z Ｒ ) are obtained in a plurality of reference test cycles on a plurality of reference machines (2, 3, . . . , n).

2. At least one of the reference quantities (LL, UL; μ Ｒ , σ Ｒ ; μ Ａ -μ Ｄ ;σ Ａ -σ Ｄ ) includes tolerance limits (LL, UL) for at least one type of status data (Z), The tolerance limits (LL, UL) are determined based on at least one statistical reference value (μ Ｒ , σ Ｒ ) and is automatically set based on the The statistical reference value (μ Ｒ , σ Ｒ ) is the reference state data (Z Ｒ 2. The method of claim 1, wherein the statistical analysis of

3. At least one of the statistical reference values ​​(μ Ｒ , σ Ｒ ) includes at least one type of reference state data (Z Ｒ ) expectation value (μ Ｒ ) and the reference state data (Z Ｒ ) variance (σ Ｒ ２ and an indicator of

4. the test cycle is repeated several times at different times and a workpiece (23) is machined using the machine tool (1) between the test cycles; The condition diagnosis is carried out by comparing the condition data (Z) acquired in several test cycles with at least one of the reference quantities (LL, UL; μ Ｒ , σ Ｒ ; μ Ａ , . . . , μ Ｄ , σ Ａ ,... ,σ Ｄ 4. The method of claim 1, further comprising a comparative evaluation in which the measured value is compared with the measured value.

5. The comparative evaluation is determining at least one statistical value (μ, σ) of the state data (Z) obtained from a plurality of the test cycles; The statistical value (μ, σ) and at least one of the reference quantities (LL, UL; μ Ｒ , σ Ｒ ; μ Ａ , . . . , μ Ｄ , σ Ａ ,... ,σ Ｄ ) and The method of claim 4, comprising:

6. The comparative evaluation is analyzing the progression of the condition data (Z) obtained from the plurality of test cycles as a function of time or as a function of the number of workpieces machined using the machine tool; The results of this analysis are compared with at least one of the reference quantities (LL, UL; μ Ａ -μ Ｄ ;σ Ａ -σ Ｄ ) and Including, 5. The method of claim 4, wherein analyzing the progression preferably comprises extrapolating future values ​​of the condition indicator (Z).

7. At least two state classes (A to D) are based on the reference state data (Z Ｒ ) is formed from For each condition class (A to D), at least one statistical criterion (μ Ａ , . . . , μ Ｄ , σ Ａ ,... ,σ Ｄ ) is calculated, In the condition diagnosis, the condition data (Z) is the statistical reference value (μ Ａ , . . . , μ Ｄ , σ Ａ ,... ,σ Ｄ 4. The method according to claim 1, wherein the β-glucan is compared with the β-glucan.

8. The method of any one of claims 1 to 3, comprising triggering an action depending on the result of the condition diagnosis.

9. the action includes issuing a diagnostic message to a user; the diagnostic message is preferably transmitted via a network to a terminal device (48) spatially separated from the machine tool (1) and output on the terminal device; The method of claim 8 , wherein the sending is optionally done via a messaging service, by push message, or by email.

10. automatically modifying at least one process parameter for machining a workpiece (23) on said machine tool (1) as a function of the result of said condition diagnosis; The method of claim 8, comprising:

11. The status data (Z) may be of the following types: position deviation data determined using at least one position sensor (19) and indicative of a position deviation of at least one component from a nominal position; vibration data determined using at least one motion sensor (18) and indicative of a vibration state of at least one of said components; and / or power data indicative of current consumption in a drive motor of at least one of said components; and / or A method according to any one of claims 1 to 3, including data derived from said types of data.

12. said determining said state data (Z) includes spectral analysis of measurement data; The spectral analysis preferably includes determining spectral intensity values ​​of discrete excitation frequencies or excitation orders; The method according to any one of claims 1 to 3, wherein the state data (Z) comprises the spectral intensity values ​​or quantities derived from the spectral intensity values.

13. 4. The method according to claim 1, wherein the status data (Z) comprises at least one specific status indicator derived from measurement data from two or more sources or from measurement data relating to the operation of two or more of the machine axes.

14. 4. The method according to claim 1, wherein the state data (Z) includes predicted EOL data indicating at what order excitation is expected in an EOL spectrum on an EOL test stand when a toothed workpiece machined using a gear cutting machine is placed in a gear assembly and performs rolling motion together with a mating gear in the gear assembly.

15. The reference state data (Z Ｒ 4. The method according to claim 1, wherein the information is stored in a database (DB).

16. An evaluation computer (45) accesses the database (DB) to perform an analysis of the condition; 16. The method according to claim 15, wherein the evaluation computer (45) is preferably arranged spatially separate from the machine tool and connected to the machine tool (1) by a network connection.

17. The state data (Z) is the reference state data (Z Ｒ 16. The method of claim 15, further comprising storing said status data (Z) in said database (DB) so that said status data (Z) is available for future test cycles as a test status record (STC).

18. A device for monitoring the status of a machine tool (1) having multiple machine axes, A processor (451) and a storage medium (452) storing a computer program, The computer program, when executed on the processor, performs the following steps: - in a test cycle of the machine tool (1), at least some of the machine axes are actuated, associated measurements are taken, and status data (Z) determined by the measurements are received; The condition data (Z) includes at least one reference quantity (LL, UL; μ Ｒ , σ Ｒ ; μ Ａ -μ Ｄ ;σ Ａ -σ Ｄ ) performing a condition diagnosis; Run At least one of the reference quantities (LL, UL; μ Ｒ , σ Ｒ ; μ Ａ -μ Ｄ ;σ Ａ -σ Ｄ ) is the reference state data (Z Ｒ ) and the reference state data (Z Ｒ ) obtained in a plurality of reference test cycles on a plurality of reference machines (2, 3, . . . , n).

Images