Method for monitoring multiple electric drives of an electromechanical system

Abstract

A method for monitoring multiple electric drives (M1, M2, M3, M4) of an electromechanical system, wherein the drives (M1, M2, M3, M4) operate jointly on a movable machine element of the system, characterized in that during operation of the drives, one or more motor currents of multiple drives (M1, M2, M3, M4) are measured at a predetermined sampling rate and stored as a series of measured values ​​assigned to the respective drive, each containing a number (m) of measured values, in that statistical parameters are calculated from multiple series of measured values, wherein statistical parameters are calculated from the series of measured values ​​of multiple drives that characterize a correlation between multiple drives, and in that one or more state information and / or state predictions for one or more drives are generated by analyzing the parameters that characterize a correlation.

Description

[0001] The invention relates to a method and a system for monitoring or analyzing one or more electrical drives of an electromechanical system, e.g. a wind direction tracking system of a wind turbine, where the drive(s) act, for example, as actuators on a movable machine element of the system, e.g., on a bearing ring of an azimuth bearing.

[0002] The electromechanical system is, for example, a wind direction tracking system for a wind turbine, which keeps or adjusts the nacelle of the wind turbine in the direction of the prevailing wind. For this purpose, the nacelle of the wind turbine is rotatably mounted on a tower or tower head. In large turbines, the wind direction tracking is actively achieved using one or more drives, preferably electric drives. These drives, also known as azimuth drives, operate as actuators, for example, on the bearing ring of the azimuth bearing or tower head bearing, which can be, for example, a large roller bearing with external or internal teeth. The drives can be designed, for example, as geared motors with an output pinion, several coaxial planetary gear stages, and a flanged AC motor. A standstill brake may be integrated into the motor.Additionally, one or more azimuth brakes are implemented. Various configurations are available, such as internal toothing of the azimuth bearing with an internal azimuth brake, internal toothing of the azimuth bearing with an external azimuth brake, external toothing of the azimuth bearing with an internal azimuth brake, or external toothing of the azimuth bearing with an external azimuth brake. Multiple electric motor drives are always provided, all operating together on a single bearing ring of the azimuth bearing.

[0003] The electric drives are designed, for example, as single-phase or multi-phase AC drives or three-phase drives, meaning they are operated with single-phase or multi-phase alternating current (three-phase current). Alternatively, the invention also includes systems with one or more DC drives.

[0004] Wind turbines, and especially their tracking systems, are subject to high stresses in practice, making them prone to wear and tear and potentially leading to damage. Reliable detection of damage to the tracking system, and particularly to its drives, is crucial, as failures result in downtime and thus high costs and losses for the operator. With existing systems, damage to the tracking drives can currently only be detected unreliably. This means that if damage to one drive goes undetected, other components, such as additional gearboxes and electric motors, or even gear rings or bearings, can be damaged or destroyed. Known passive protective measures are insufficient to prevent such fault propagation.This is because the currents of the electric motors, which can generally be used to monitor the drive, are highly dynamic and constantly fluctuate, sometimes even permanently. Such fluctuations in current draw arise, for example, from the wind load on the nacelle, imbalance of the rotor blades, rotor speed, imbalance of the generator, temperature of the motor windings and supply lines, motor slippage, measurement inaccuracies, motor tolerances (current draw), tolerances of the gear ring, tolerances of the motor gearbox, the condition of the motor brake, and / or the condition of the nacelle bearing. It is also due to the high dynamics of the electric motors' current draw that the gradual onset of drive defects cannot always be reliably detected with conventional (manual) current measurement during maintenance or service calls.

[0005] Furthermore, defective drives predominantly deliver normal current draw values, so standard protective devices (motor protection switches, circuit breakers) do not trip. Moreover, even with one or more defective drives, the nacelle is usually still aligned with the wind by the remaining, functional drives. This places an excessive load on these drives. However, even such an excessive load on the remaining drives does not trigger the protective device, as the wind tracking system does not move continuously, but on average only for a few seconds. This, with an average increased current flow, is insufficient to cause the thermal protection device to trip. The resulting increased load on the remaining drives leads to further drive damage. Often, the conventional protective device only trips when, for example...three out of four drives are defective and the last remaining drive is subjected to such a high load that thermal protection devices trigger or the system control stops due to the lack of wind tracking.

[0006] A condition monitoring system for a motor is known, for example, from WO 2011 / 069545, where the condition monitoring function of the motor is achieved by the motor end shield. For this purpose, the end shield comprises a sensor unit for acquiring a measured parameter of the motor, a communication means, and a power supply unit for providing the sensor unit with energy. The sensor unit can, for example, include means for measuring bearing current, the temperature of the motor, the imbalance of the motor, or even acoustic vibrations. Such a condition monitoring system cannot reliably detect the typical defects occurring in a wind tracking system, since the measured parameters of the electric motors remain within the normal range until, as experience has shown, only a functioning drive takes over the wind tracking. This is where the invention comes in.

[0007] The invention is based on the objective of creating a method with which one or more electric drives of an electromechanical system can be analyzed and / or monitored in a simple and reliable manner, in particular to detect damage and / or wear on individual drives early and reliably or to avoid them in advance.

[0008] To solve this problem, the invention teaches, in a generic method for monitoring one or more electrical drives of an electromechanical system, that during the operation of the drive(s), one or more motor currents of one or more drives are measured at a predetermined sampling rate and stored as series of measured values ​​assigned to the respective current (or phase) of the respective drive, each with a predetermined number of measured values. that statistical parameters are calculated (and, if necessary, temporarily stored) from one or more series of measurements and that by analyzing the temporal development of the characteristic values ​​and / or by analyzing a relationship between the characteristic values ​​of different motor currents, one or more state information and / or state predictions for one or more drives are generated.

[0009] According to the invention, the current(s), e.g., the individual phase currents of one or more phases and preferably of one or more electric drives, are recorded, stored, and statistically evaluated at a high sampling rate. This allows statistical parameters to be determined from a large number of measured values, from which conclusions and, if necessary, predictions about the system condition or the condition of the drives can be derived. Within the scope of the invention, a high sampling rate means a sampling rate of more than 0.2 Hz, e.g., at least 0.5 Hz, preferably at least 1 Hz. A sampling rate of less than 10 Hz, e.g., less than 5 Hz, is generally sufficient to avoid excessively large data volumes. Consequently, at least one measurement per second is particularly preferred. A system according to the invention is therefore equipped with suitable measuring devices for measuring the currents of the individual drives at such a sampling rate.The measured values ​​are stored as measurement series in a database and statistically or stochastically evaluated using an anomaly detection algorithm.

[0010] According to the invention, the current or currents of the drive(s) are measured. These can be, for example, the respective instantaneous values, the rectified average value, or the RMS value.

[0011] The method is preferably used in a system with multiple drives, each designed as a multiphase drive with several phases. This can preferably involve a wind direction tracking system where several (e.g., four) electric multiphase drives operate on a common machine element, namely a bearing ring of an azimuth bearing. However, the method can also be used in other types of systems with one or more drives, where the drive(s) can be single-phase or multiphase drives and / or DC drives. For example, DC drives are also used in wind turbines for emergency operation, e.g., in connection with blade pitch control.

[0012] Preferably, for each motor current or each individual phase of each individual drive, measurement series with a multitude of measured values ​​are continuously recorded, with each measurement series potentially containing, for example, 100 to 2000 measured values, preferably 400 to 1000 measured values. For instance, measurement series with 600 measured values ​​each can be recorded. Consequently, individual "clusters" of 600 measurement points each are preferably formed. The measurement series, which, for example, summarize ten minutes of motor operation, do not need to be recorded continuously but can also consist of several operating phases, e.g., in wind direction tracking where the drives are operated for only a few seconds at a time, so that a measurement series comprises a multitude of tracking phases.

[0013] The evaluation and analysis are based on the measurement series. In the first step of the evaluation, statistical parameters are calculated from the measurement series.

[0014] These can be statistical parameters that characterize only a motor current of a drive or a single phase of a single drive, without considering any correlation between individual phases or drives. Such statistical parameters, each assigned to a single phase of a single drive, can include, for example, the mean value of each individual phase, the density of measurements, the RMS (root mean square) of the measurement series, the value of the highest density of measurements, the minimum, the maximum, the standard deviation, the variance, the first sigma, the second sigma, the third sigma, or the median. Statistical parameters can also be calculated from the values ​​of several measurement series, such as the mean of all phases of a drive.

[0015] Alternatively or additionally, statistical parameters are calculated from the measurement series of several phases and / or several drives, indicating a correlation or relationship between several phases or drives. These parameters can include, for example, the covariance of the measurement series or trend lines, such as the covariances of the trend lines M (1, 2, 3, 4) L1 to M (1, 2, 3, 4) L2 or M (1, 2, 3, 4) L2 to M (1, 2, 3, 4) L3 or M (1, 2, 3, 4) L3 to M (1, 2, 3, 4) L1. Similarly, the correlation coefficients for these respective pairs of measurement series can be calculated.

[0016] In a second step of the evaluation, the calculated statistical parameters are stored in an evaluation unit, preferably each with a timestamp. The evaluation unit can, in particular, comprise a database server in which the calculated statistical parameters are stored.

[0017] In a third step, the previously calculated statistical parameters are classified or analyzed, whereby - as described above - these can be statistical parameters that are assigned to individual streams or individual phases of individual drives (e.g. means, variance) or statistical parameters that already represent a relationship between individual phases of individual drives or the phases of different drives (e.g. covariance, correlation coefficient).

[0018] In a possible first embodiment, the evaluation process involves classifying the measured values ​​that represent a correlation between multiple phases or drives, e.g., by evaluating the covariances or correlation coefficients, in order to generate status information for one or more drives. For example, functional, new drives exhibit low covariances between the phases / drives. Conversely, a higher covariance between individual phases or drives suggests a fault in the respective drive.It is more practical not to consider the absolute values ​​of the covariance or to compare the covariance in a current situation with specific limits or thresholds for the covariance, but rather to analyze the temporal evolution of the covariance in order to identify unusual developments that indicate damage or anomalies that could lead to future damage. A prerequisite for evaluating or analyzing the temporal evolution of the covariance is the measurement of several motor currents or phase currents, so that anomalies concerning the relationship between these phase currents can be detected via the covariance.

[0019] Similarly, information or status information can be obtained via the correlation coefficient between individual phases or drives. A correlation coefficient of 1 represents a perfect, linear relationship, while a value of 0 indicates a complete lack of linear relationship. It is then possible, for example, to define a threshold for a correlation coefficient that triggers a warning and / or a fault message. For instance, a correlation coefficient between 0.7 and 0.6 could trigger a warning message, and a correlation coefficient below 0.6 could trigger a fault message. When analyzing the correlation coefficient, it can also be useful to monitor its progression over a specific period to detect and predict malfunctions, damage, or anomalies.

[0020] Of particular importance is the fact that in certain systems, such as wind tracking systems, the drives transfer their mechanical energy to the same machine element, e.g., the same gear ring, so that the resulting loads are also distributed across all drives. It follows that the individual phase currents of the individual drives are correlated. For this reason, the correlation coefficient (or covariance) is an important and meaningful indicator of the condition of each individual drive.

[0021] While the first embodiment described always considers measured values ​​or series of measured values ​​that represent a correlation between several motor currents or several phases of one or more drives, a possible second embodiment provides for the analysis of individual or multiple characteristic values ​​that each relate to only one drive, one phase, or one current, without relying on a relationship between several phases or drives or requiring data from an (additional) reference system. For this purpose, the temporal development of the characteristic values ​​of such a current or a single phase of a drive is analyzed, for example, by classifying them based on previously determined and stored characteristic values, or by first determining and storing characteristic values ​​as reference values ​​in a learning phase.To this end, it is useful to determine measurement distribution functions or probability density functions from the continuously recorded measurement series. These functions are then stored as characteristic values ​​or key figures, from which further characteristic values ​​or key figures are derived. These can include, for example, the aforementioned characteristic values / key figures, each relating to the distribution function or probability density distribution, such as the spread, the first, second, or third sigma, the mode, the variance, the minimum, the maximum, the standard deviation, and / or the median. This allows for the monitoring and analysis of individual drives via time series analysis without requiring data from a reference system. The trend analysis can be used to detect wear, identify damage, or predict damage.

[0022] This means that even when considering a single drive or phase, the statistical parameters, which are determined from the continuously recorded series of measurements, are correlated, and correlation coefficients can be calculated from this correlation. The aforementioned condition information and / or condition forecasts can be derived from these correlation coefficients or their development over time.

[0023] It is therefore possible to correlate the calculated statistical parameters in a trend analysis with statistical parameters measured and calculated in the past (e.g., during a learning phase). This correlation allows for the determination of trend values ​​and the generation of condition information for one or more motors / drives. To detect anomalies, the calculated statistical parameters are thus transferred into a trend analysis. By considering the correlation with historical values ​​(learning phase), the determined trend values ​​provide condition information or a key figure from which a defect can be identified.

[0024] Optionally, the determined trend values ​​are correlated not only with historical data but also with data from other drives and / or phases in the system. This correlation integrates the results into the determination of the condition information or key figure. Overall, a defect can be identified from the condition information or key figure, for example, in a drive or motor, its associated gearbox, the gearing (of the bearing ring), or a braking system. Slow-developing faults are also reliably detected using time series analysis or trend analysis of the derived values. If all drives are fully functional, the trend values ​​of the calculated key figures remain static. However, if significant deviations in the trend values, acute deviations from other drives in the system, or abstract values ​​are detected, a fault or characteristic can be identified.Defect detection. Trend value analysis is implemented using an evaluation algorithm in the evaluation unit.

[0025] The measurement densities can always be used for evaluation. These densities, or density functions, are recorded and stored as measurement series (e.g., clusters with, for example, 600 measurements each). From this, a trend value can be calculated over the entire operating time. For instance, an increasing or decreasing trend value can indicate a malfunction of the drive. The trend values ​​are then compared and correlated. However, individual statistical parameters of specific phases can also provide important additional information, independent of any correlation, as described above. For example, acute malfunctions can be identified by evaluating the first sigma, the second sigma, and / or the third sigma.

[0026] Of particular importance is the fact that, according to the invention, no comparison of measured values ​​or even a simple comparison of statistical parameters (e.g., averages) takes place, but rather that series of measured values ​​or the statistical parameters determined from these series are always correlated. Consequently, correlation coefficients are preferably determined and their development is analyzed. Preferably, the entirety of all determined measured values ​​is included in the coefficient determination.

[0027] Overall, by measuring the currents and, for example, the phase currents of all phases of multiple drives at a high sampling rate and by statistically evaluating them with an algorithm, reliable monitoring / analysis of the drives is achieved for reliable fault detection and damage prevention. The method according to the invention does not work with statistical reference values ​​or with predefined setpoint values. Characteristic parameters of the drives are correlated, for example, to detect a defective drive or drives, including the corresponding torque converters. This allows conclusions to be drawn not only about the condition of individual motors but also about the entire wind tracking system. The system does not need to be parameterized for the respective installation. Only communication settings need to be configured, so the system is easy to install.This can be achieved without placing particularly high demands on the qualifications of the installers.

[0028] Since, for example, data from multiple systems, such as wind turbines, are preferably analyzed on a central server, the centralized processing of the data makes it possible to utilize correlations of the calculated motor parameters of an entire wind farm and obtain even more meaningful results, e.g., for forecasts. Optionally, the evaluation algorithm can also be designed so that, by combining different analyses, it is possible to precisely locate damage and / or wear. For example, suitable analyses based on the parameters allow for the targeted differentiation of damage or impending damage to brakes, shafts, gearboxes, or gear rings.

[0029] Of particular importance is the fact that the system makes it possible not only to detect current damage or wear, but above all to provide indications of future damage occurring long before damage occurs or a drive fails and thus overloads the other drives.

[0030] The algorithm can also use methods of artificial intelligence or neural networks, or be implemented with such methods, so that the system is self-learning, since all data from each individual drive is permanently stored in the database over long periods of time and is available for analysis.

[0031] It is important that, according to the invention, a direct comparison of the measured motor currents is avoided, but rather statistical parameters are considered and interpreted, for example, in the sense of a time series analysis. The invention recognizes that a simple comparison of individual motor currents is unsuitable for damage assessment, since, for example, in a free-running motor, even a shaft breakage would not result in any irregularity due to the rated current draw. Due to the aforementioned disturbances and environmental influences, extreme hysteresis in the current draw occurs, making a simple comparison of individual motor currents unsuitable. Furthermore, every electric motor exhibits manufacturing tolerances that cannot be determined due to the aforementioned external influencing factors, and this also prevents a static limit value for the current draw used for fault detection. Therefore, according to the invention, a mathematical or...Stochastic / statistical evaluation of calculated key figures.

[0032] The invention relates not only to the described method, but also to the electromechanical system itself, that is, an electromechanical system which, for example, can be designed as a wind direction tracking system for a wind turbine. An electromechanical system comprises at least one movable machine element on which one or more drives act. The system is configured to carry out the described method; that is, it is equipped with or connected to a control and evaluation unit configured for carrying out the described method.

[0033] The electromechanical system is particularly preferably a wind direction tracking system for a wind turbine, in which several AC motors operate on a common bearing ring or gear ring of an azimuth bearing. However, other electromechanical systems are also included, in which several drives preferably operate on a common machine element. Thus, the invention can also be used, for example, in crane systems, such as portal slewing cranes. Early fault detection is always the primary focus. Furthermore, the invention also allows for the monitoring of systems with individual drives. Monitoring of systems with DC drives is also possible according to the invention.

[0034] Preferably, the data is stored and evaluated externally, i.e., outside the actual electromechanical system, e.g., outside the wind turbine. In a preferred embodiment, the invention proposes that the system be equipped with a local control unit for acquiring the measured values. Optionally, the measured values ​​can be temporarily stored. However, permanent storage and evaluation of the measured values ​​or series of measured values ​​is preferably not provided for in the local control unit. Instead, the measured values ​​are preferably transmitted (e.g., via cable / fiber optic cable or mobile network) to an externally located evaluation unit, which can be implemented independently of the wind turbine and at a considerable distance. Such an evaluation unit comprises, for example,A database server with evaluation software is set up so that the measurement series are stored on the database server and analyzed using an evaluation algorithm. The external evaluation unit can, for example, serve as a central control room for several wind turbines. The evaluation unit preferably has an interface through which the status information can be queried by or transmitted to external devices, such as PCs, tablets, or smartphones. This makes it possible to transmit warning messages from the database server to various devices and to query status information from the database server using these devices.

[0035] Furthermore, it is possible to automatically influence the electromechanical system by, for example, stopping the wind tracking when a specific fault event is registered. The system according to the invention can therefore optionally be configured so that a system, e.g., a wind turbine, is automatically stopped and consequently shut down in response to certain status information. In this way, serious damage to the system or consequential damage can be avoided.

[0036] The invention will now be explained with reference to drawings which merely illustrate one embodiment. The drawings show: Fig. 1 Schematically simplified, an electromechanical system in the embodiment as a wind direction tracking system with a condition monitoring system according to the invention, Fig. 2 an enlarged section from the plant to Fig. 1, Fig. 3a, b statistical parameters (covariances) for properly functioning (new) drives on the one hand and defective drives with increased slippage on the other hand, Fig. 4a, b statistical parameters (correlation coefficients) for the drives according to Fig. 3a, b, Fig. 5a, Fig. 5b simplifies histograms or distribution density functions.

[0037] In the Fig. 1 and Fig. Figure 2 is a schematically simplified representation of an electromechanical system in the embodiment of a wind direction tracking system, with a condition monitoring system according to the invention for the electric drives of the wind direction tracking system.

[0038] Wind tracking is used to align the nacelle of a wind turbine with the prevailing wind direction. The nacelle is mounted on a rotating bearing at the top of the tower and can be adjusted using several electric drives. Fig. Figure 1 shows a simplified section of a tower head bearing 1 with a bearing ring 2, on which several electric drives M1, M2, M3, M4 operate. An exemplary arrangement with internal gearing of the azimuth bearing and internal drives, as well as an external azimuth brake 3, is shown. The electric drives are connected to a control unit 4, which is located in the wind turbine, e.g., in the tower head or in the nacelle. The drives are designed as three-phase AC motors or three-phase motors. In the exemplary embodiment, four drives M1, M2, M3, M4 are provided, which act together on the same machine element, namely the same bearing ring 2. The control unit 4 activates the drives as needed to adjust the nacelle to a change in wind direction. According to the invention, the drives are equipped with measuring devices 5.connected to which the phase currents of all three phases L1, L2, L3 of each individual drive M1, M2, M3, M4 are measured. Furthermore, in . Fig. Two common protective devices, e.g., a motor protection switch 11 and a contactor 12, are indicated and can be provided in the conventional manner. The measuring devices 5 record all phase currents of the drives at a high sampling rate of, for example, 0.5 Hz to 5 Hz, e.g., approximately 1 Hz, i.e., every second. Starting and stopping peaks are already subtracted from the system by the measuring devices 5 or by the control unit 4. During motor operation, which typically lasts only a few seconds as part of wind direction tracking, the measured values ​​are (temporarily) stored in the control unit 4. The measured values ​​are transmitted (e.g., after the measurement is complete or after the wind direction tracking has stopped) via an A / D converter 13 and a microcontroller MC to an evaluation unit or storage and evaluation unit 7 (e.g., via TCP / IP) using an interface or communication device 6.The storage and evaluation unit 7, for example, is designed as a database server for storing large amounts of data, or is equipped with a database server, and evaluation software is also stored in the evaluation unit 7. The measured values ​​are evaluated in the storage and evaluation unit, and status information for the drives M1, M2, M3, and M4 is generated from this evaluation. This status information can be accessed and visualized via various end devices, such as a PC 8, a tablet 9, or a smartphone 10, whereby the end devices can communicate with the evaluation unit 7 either wired or wirelessly.

[0039] In the illustrated embodiment, all three phase currents L1, L2, L3 can therefore be measured for each of the motors M1, M2, M3, and M4 at the described high sampling rate of, for example, 1 Hz. Measurement series with a predetermined number m of measurements are stored; in this embodiment, 600 measurements per series. This means the measurements are stored in clusters of 600 measurement points, for each individual phase of each motor, resulting in twelve measurement series being generated and stored. At a sampling rate of 1 Hz and 600 measurements per series, each measurement series represents a period of 10 minutes. This period does not result from a continuous measurement but rather encompasses the entire operation of the respective drive as a result of several successive wind tracking operations.In the control unit 6, which is located locally in the area of ​​the drives, it is therefore not necessary to store complete series of measured values; instead, only the individual measurements taken during the operation of the drives are temporarily stored and transferred to the storage and evaluation unit 7. There, the measured values ​​are collected as series of measured values ​​in order to subsequently perform an evaluation.

[0040] Of particular importance is that, according to the invention, a statistical / stochastic evaluation and analysis of the measurement series is carried out; that is, statistical parameters are generated from the measurement series, for example, on the one hand, parameters for a correlation between several phases or several drives, and on the other hand, statistical parameters for individual phases of the respective drive. From these statistical parameters, state information for a drive or for all drives can be generated individually or through suitable combinations, using the evaluation software or an evaluation algorithm stored in the storage and evaluation unit 7, which generates the state information representing the respective state of the individual drives.

[0041] From the most recent m measurements, e.g. 600 measurements, and consequently from each individual series of measurements (via the measurement distribution densities) for each drive and each phase, one or more of the following statistical parameters can be derived: Mean of each individual phase, mean of overall L1 / L2 / L3, that is (sum L1 / 600 + sum L2 / 600 + sum L3 / 600) / 3, mean of the individual phase measurements (L1 + L2 + L3) / 3, RMS, density of measurements, value of the highest density of measurements or maximum of the measurement distribution, minimum of the measurement series, maximum of the measurement series, standard deviation, variance, first sigma, second sigma, third sigma, median.

[0042] These statistical parameters contain statistical information about the measurement series, without considering relationships between individual measurement series.

[0043] Additionally, it is particularly advantageous to calculate statistical parameters for the correlation between multiple measurement series, i.e., measurement series from several phases and multiple drives. These parameters primarily include the covariances of the measurement trends and / or the correlation coefficients. For example, all covariances and correlation coefficients for all possible combinations of measurement series (M (1 - N) Li to M (1 - n) Lj) can be evaluated. All statistical parameters are time-stamped and stored in the evaluation and storage unit. The integrated algorithm then analyzes the stored statistical parameters by classifying and analyzing them, thereby drawing conclusions about the condition of the system or the drives.

[0044] For example, the following will be mentioned: Fig. 3a and Fig. Reference is made to 3b on the one hand and 4a and 4b on the other: In Fig. Figure 3a shows the covariances for a large number of measurement series for a new or properly functioning drive or multiple drives. Four consecutive measurement phases, each with 600 data points and thus covering a period of 10 minutes, are shown, with the individual evaluations labeled A1, A2, A3, and A4. The covariances for the various combinations M are shown. n Li to M n L j The graph is plotted. The labeling on the X-axis is only an example of some combinations. It is evident that all covariances are relatively low, suggesting a properly functioning drive or drives.

[0045] In contrast, the Fig. 3b A situation in which the covariances increase slightly whenever drive M4 is involved, suggesting a fault in drive M4, e.g., increased slip. This is particularly noticeable in the covariance M4L1 to M4L3, i.e., in a covariance where the different phases of the same drive M4 are involved.

[0046] Similar information can be obtained by evaluating the correlation coefficients according to Fig. 4a and Fig. 4b. It is again evident that the correlation coefficient is in a high range for all measurement phases (cf. Fig. 3a), while it drops to low values ​​in the case of a defective drive (compare Fig. 3b). It is evident that these dips in the correlation coefficient always occur when drive M4 is involved, meaning that a fault in a drive can be detected particularly reliably via the correlation coefficient. Consequently, the software can verify which motor is malfunctioning by comparing or analyzing the relationships between the individual correlation coefficients, thus preventing damage. Of particular importance is the ability to analyze the temporal development of these covariances or correlation coefficients using time series analysis or trend analysis.

[0047] The following show Fig. 3a, Fig. 3b and Fig. 4a, Fig. 4b merely illustrates the evaluation and analysis of certain statistical parameters. In particular, further statistical parameters are evaluated and monitored to detect various types of defects or wear.

[0048] The Fig. 5a and Fig. 5b shows exemplary histograms or distribution density functions of measurement series for a new drive ( Fig. 5a) on the one hand and a defective drive ( Fig. 5b) on the other hand. It is evident that the measurement distribution for a new drive is approximately normally distributed, with the frequency of the phase currents indicated on the x-axis being plotted. In contrast, it shows Fig.5b such a distribution for an existing system with a defect. These measurement distribution densities can be stored in statistical parameters, or statistical parameters can be derived from the distribution densities, e.g., the spread, the first, second, and third sigma, the mode, the variance, the minimum, the maximum, the standard deviation, and / or the median. By considering the correlation with stored reference values, or especially by analyzing the temporal development of these statistical parameters, an individual drive or a single motor current can be analyzed or monitored, and wear can be reliably detected or predicted without having to monitor a reference system. Only the drive's historically stored data is required as a reference, by analyzing the temporal development of the distribution density function or its parameter. For example,The three sigma values ​​are used to verify short-term measurements in order to reliably detect acute damage. For example, if measured values ​​frequently fall outside the sigma range, this indicates a fault, and the further the measured value deviates from the mode, the clearer and more acute the fault. Even when monitoring or analyzing a single drive or phase (without considering a reference system), correlations between the measured value series recorded over time and the resulting characteristic values ​​can be analyzed, and correlation coefficients can be determined and evaluated.

[0049] Furthermore, it is advantageous that the storage and evaluation of the very extensive data is not carried out in the local control unit 4 in the wind turbine; instead, only the measurement and transmission of the measured values ​​take place there (possibly after temporary intermediate storage). The storage and evaluation unit 7 is usually located far away from the wind turbine, so that, for example, a large number of wind turbines can be monitored via a central monitoring system.

[0050] Furthermore, the storage and evaluation unit 7 can also contain visualization software that visually displays the collected and derived values ​​from the database for the user and serves as a communication interface between the evaluation algorithm and the user. Appropriate responses to the error event, such as an alarm email or, if desired, a stop of the wind tracking, can also be executed.

[0051] Furthermore, the storage and evaluation unit 7 is shown in a highly simplified form in the drawings. It may, in particular, include a database server. The software for data evaluation, anomaly detection, and error reporting can be located on the same server. Alternatively, however, an additional computer / server can be provided to perform the evaluation, anomaly detection, and error reporting. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] WO 2011 / 069545

[0006]

Claims

[1] Method for monitoring one or more electric drives (M1, M2, M3, M4) of an electromechanical system, wherein the drive(s) (M1, M2, M3, M4) act on a movable machine element of the system, characterized by , that During operation of the drives, one or more motor currents of one or more drives (M1, M2, M3, M4) are measured at a predetermined sampling rate and stored as a series of measured values ​​assigned to the respective drive, each with a number (m) of measured values. that statistical characteristic values ​​of the respective drive (M1, M2, M3, M4) are calculated from one or more series of measured values, that by analyzing the temporal development of the characteristic values ​​and / or by analyzing a relationship between the characteristic values ​​of different motor currents, one or more state information and / or state predictions for one or more drives are generated. [2] Method according to claim 1, characterized by that the sampling rate is more than 0.2 Hz, at least 0.5 Hz, preferably at least 1 Hz. [3] Method according to claim 1 or 2, characterized by , that the measurement series each contain 100 to 1000 measurements, e.g. 400 to 1000 measurements. [4] Method according to any one of claims 1 to 3, characterized by that the system has one or more single-phase or multi-phase AC drives as drives (M1, M2, M3, M4). [5] Method according to any one of claims 1 to 3, characterized by that the system has one or more DC drives as drives. [6] Method according to any one of claims 1 to 5, characterized by that several drives (M1, M2, M3, M4) are provided which work together on the same movable machine element and that these drives are each designed as, for example, multiphase drives with several phases (L1, L2, L3). [7] Method according to any one of claims 1 to 6, characterized by that the electromechanical system is designed as a wind direction tracking system for a wind turbine and that the movable machine element is formed by a bearing ring of an azimuth bearing. [8] Method according to any one of claims 1 to 7, characterized by , that measurement distribution functions or distribution density functions are determined from the measurement series and, if necessary, stored, and / or that the characteristic values ​​are determined from the measurement distribution functions or distribution density functions, or that the distribution functions or density functions are stored in characteristic values. [9] Method according to any one of claims 1 to 8, characterized by , that one or more characteristic values ​​from the following group are calculated and, if necessary, temporarily stored as statistical characteristic values ​​from individual measurement series for the respective motor current or the respective phase of the respective drive: Mean values ​​of individual or all currents / phases, mean value of all currents / phases of a drive, minimum, maximum, standard deviation, variance, first sigma, second sigma, third signal, median, RMS (effective value), sum of squares, measurement density, value of the highest measurement density. [10] Method according to any one of claims 1 to 9, characterized by , that as statistical parameters from several measurement series of different phases and / or different drives, one or more parameters from the following group are calculated and, if necessary, temporarily stored: Covariance, correlation coefficient. [11] Method according to any one of claims 1 to 10, characterized by, that the temporal development of the characteristic values ​​of a current or a phase or a drive is analyzed by, for example, classifying them based on previously determined and stored characteristic values, whereby, for example, characteristic values ​​are first determined and stored as reference values ​​in a learning phase and / or whereby characteristic values ​​determined successively over time are correlated. [12] Method according to any one of claims 1 to 10, characterized by , that the relationship or the temporal development of the relationship of characteristic values ​​of different streams or phases or drives is analyzed by, for example, correlating characteristic values ​​or the temporal course of characteristic values. [13] Method according to any one of claims 1 to 10, characterized by, that statistical parameters representing a relationship between measurement series of different currents or phases or drives, e.g. covariances and / or correlation coefficients, are analyzed by, for example, correlating them with stored reference or limit values ​​or analyzing their temporal progression. [14] Method according to any one of claims 1 to 13, characterized by that the measured values ​​are recorded locally in the system and, if necessary, temporarily stored, and that the determined measured values ​​are transferred to an externally arranged evaluation unit and stored and evaluated in the evaluation unit as series of measured values. [15] Method according to any one of claims 1 to 14, characterized by, that feedback is generated to the system based on the determined status information and that the operation of the system is adjusted depending on the feedback, e.g. by interrupting or changing the operation of the system depending on status information or feedback. [16] Electromechanical system, e.g. wind direction tracking of a wind turbine, with at least one movable machine element, e.g. a bearing ring (2) of an azimuth bearing (1), and with one or more electric drives (M, M2, M3, M4) acting on the movable machine element, characterized by that the plant is set up to carry out a method according to one of claims 1 to 15. [17] Plant according to claim 16, characterized by that the drives are equipped or connected with one or more measuring devices (5) for measuring the motor currents. [18] Plant according to claim 16 or 17, wherein the plant is equipped with a local control device (4), characterized by , that the measured values ​​are measured with the control unit (4) and temporarily stored locally in the control unit and that the measured values ​​are transferred to an externally arranged evaluation unit (7), which e.g. has a data server and / or evaluation server with an evaluation program. [19] Plant according to any one of claims 16 to 18, characterized by , that the external evaluation unit has an interface through which the status information can be transmitted to one or more terminal devices or queried with one or more terminal devices (8, 9, 10).

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