LEVEL GAUGE

Abstract

Level measuring device for process automation in industrial or private environments, comprising a sensor and a computing arrangement which is set up to detect a change in the medium based on the evaluation of the recorded level measurement data and then trigger the recording of level measurement data with higher accuracy.

Description

TECHNICAL AREA

[0001] The present disclosure relates to process automation in industrial or private settings. In particular, the present disclosure relates to a level measuring device designed for process automation in industrial or private settings, as well as a method for acquiring level measurement data of a medium by a level measuring device, a program element, a computer-readable medium, and training data for training an algorithm based on machine learning and / or artificial intelligence for use by a level measuring device. BACKGROUND

[0002] In level measurement for process automation, the power supply of the measuring devices is often a limiting factor that must be considered. Furthermore, self-contained level gauges powered solely by a battery are frequently used today. When the battery is depleted, it must be replaced or the entire measuring device replaced. This results in a considerable maintenance effort. SUMMARY

[0003] Against this background, it is the purpose of the present disclosure to specify a level measuring device which can reduce maintenance costs.

[0004] This problem is solved by the features of the independent patent claims. Further developments of the invention result from the dependent claims and the following description of embodiments.

[0005] A first aspect of the present disclosure relates to a level measuring device designed for process automation in industrial or private settings. The level measuring device includes a sensor for detecting the level of a medium with a certain degree of accuracy. The medium is the material being measured, for example, a liquid or a bulk material.

[0006] A computing system is provided, designed to evaluate the recorded level measurement data. This computing system can be integrated into the public level gauge. However, parts of the computing system (or the entire system) can also be outsourced, for example, and integrated into the cloud and / or a remote control unit.

[0007] The computing arrangement is set up to detect a change in the medium based on the evaluation of the recorded level measurement data and then trigger the recording of level measurement data with a second accuracy, whereby the second accuracy is greater or higher than the first accuracy.

[0008] The change in the medium detected by the computing system is typically a change in the medium's fill level. If a pressure sensor is used for level measurement, the change in the medium can also be a pressure change.

[0009] According to one embodiment of the present disclosure, the level measuring device is a radar level measuring device. However, the level measuring device can also be an ultrasonic level measuring device or another type of measuring device.

[0010] According to a further embodiment of the present disclosure, the level measuring device is configured to perform the acquisition of the level measurement data of the medium with the first accuracy with a shorter measurement time than the acquisition of the level measurement data of the medium with the second accuracy.

[0011] According to a further embodiment of the present disclosure, the level measuring device is configured to perform the acquisition of the level measurement data of the medium with the first accuracy at a higher measurement rate than the acquisition of the level measurement data of the medium with the second accuracy. A less accurate measurement can thus be performed significantly more frequently than a more accurate one. The more accurate (second) measurement is, for example, only performed after a change in the medium has been detected.

[0012] According to a further embodiment of the present disclosure, the level measuring device is configured to perform the acquisition of the level measurement data of the medium with the first accuracy with a lower energy consumption than the acquisition of the level measurement data of the medium with the second accuracy.

[0013] Energy consumption can be reduced in various ways, for example by shortening the measurement duration. In radar, for instance, energy consumption is reduced primarily by shortening the measurement duration. A shorter measurement duration also results in less data being acquired and thus shorter signal processing times. Other possibilities include reducing the transmission power and lowering the operating voltage (Dynamic Voltage Scaling).

[0014] According to a further embodiment of the present disclosure, the computing arrangement is configured to determine, with first accuracy, a change in the position of a level echo, a change in pressure, a change in the shape of an echo curve and / or a change in a phase in the recorded level measurement data by evaluating the acquired level measurement data and to conclude from this a change in the medium, in particular a change in level.

[0015] According to a further embodiment of the present disclosure, the computing arrangement is configured to detect the change in the medium using an algorithm based on machine learning and / or artificial intelligence. This can be done in a manner typical of an ML or AI algorithm, i.e., not deterministically, but for example by using learned structures or determination methods, in particular by machine learning (ML), learned structures or determination methods, and / or based on neural networks, deep learning, or the like.

[0016] According to one embodiment, the computing arrangement can be configured to train the machine learning and / or artificial intelligence-based algorithm during the operation of the level gauge. The algorithm can be pre-trained or untrained when it is first used or trained.

[0017] According to a further embodiment of the present disclosure, the level gauge comprises a radar module configured to generate and transmit the radar measurement signal for acquiring the level measurement data. According to a further embodiment of the present disclosure, the level gauge is an FMCW radar level gauge (FMCW: Frequency Modulated Continuous Wave).

[0018] Another aspect of the present disclosure relates to a method for acquiring level measurement data of a medium by a level gauge, in which level measurement data of the medium are acquired with a first accuracy. Subsequently, the acquired level measurement data are evaluated by a computing arrangement. If the computing arrangement detects a change in the medium based on the evaluation of the acquired level measurement data, it triggers the acquisition of further level measurement data with a second accuracy, wherein the second accuracy is greater / higher than the first accuracy.

[0019] According to a further embodiment of the present disclosure, the method further includes the step of using and / or training an algorithm based on machine learning and / or artificial intelligence to detect the change.

[0020] Another aspect of the present disclosure relates to a program element which, when executed on a computing arrangement of a level measuring device, directs the level measuring device to perform the steps described above and below.

[0021] Another aspect of the present disclosure concerns a computer-readable medium on which the program element described above is stored.

[0022] Another aspect of the present disclosure relates to training data for training a machine learning and / or artificial intelligence-based algorithm for use by a level gauge, wherein the level gauge comprises a sensor configured to acquire level measurement data of a medium with a first accuracy, and a computing arrangement for evaluating the acquired level measurement data. The computing arrangement is configured to detect a change in the medium based on the evaluation of the acquired level measurement data and subsequently trigger the acquisition of level measurement data with a second accuracy, wherein the second accuracy is greater / higher than the first accuracy.The training data is based on the fill level measurement data and serves to train the algorithm based on machine learning and / or artificial intelligence to trigger the acquisition of further fill level measurement data with higher accuracy.

[0023] The program element and the training data may include any of the features or steps mentioned herein that are described in relation to the first aspect of the invention or the level measuring device, or the second aspect of the invention or the method, or that may apply analogously thereto.

[0024] For example, the training data can be at least partially generated during the operation of the level gauge. Alternatively or additionally, the training data can be at least partially generated outside the level gauge in which it is used, for example, training data generated in a large number of level gauges in the same or different applications. The training data can also include input data that detects and / or characterizes the change in the medium, where the input data is based, for example, on a manual evaluation of the cause of the change during maintenance of a field device. The input data can thus characterize the detected changes, for example, by nature, type, and / or magnitude of the change.For example, the input data can also or alternatively include instructions specifying certain changes and / or evaluations that the algorithm should use for learning. The input data could, for example, come from a technician.

[0025] The term "process automation in industrial environments" refers to a subfield of engineering that encompasses measures for operating machines and systems without human intervention. One goal of process automation is to automate the interaction of individual components within a plant in industries such as chemicals, food, pharmaceuticals, petroleum, paper, cement, shipping, or mining. A wide variety of sensors can be used for this purpose, specifically adapted to the requirements of the process industry, such as mechanical stability, resistance to contamination, extreme temperatures, and extreme pressures. Measurement data from these sensors is typically transmitted to a control room where process parameters such as fill level, limit level, flow rate, pressure, and density are monitored, and settings for the entire plant can be adjusted manually or automatically.

[0026] A subfield of process automation in industrial environments concerns the logistics automation of plants and supply chains. Using distance and angle sensors, logistics automation automates processes inside or outside a building or within a single logistics facility. Typical applications include baggage and freight handling at airports, traffic monitoring (toll systems), retail, parcel distribution, and building security (access control). What these examples have in common is that the respective application requires presence detection combined with precise measurement of the size and position of an object.For this purpose, sensors based on optical measurement methods using lasers, LEDs, 2D cameras or 3D cameras that detect distances according to the time-of-flight (ToF) principle can be used.

[0027] Another subfield of process automation in industrial settings concerns factory / production automation. Applications for this can be found in a wide variety of industries, such as automotive manufacturing, food production, pharmaceuticals, and packaging in general. The goal of factory automation is to automate the production of goods using machines, production lines, and / or robots, i.e., to allow it to proceed without human intervention. The sensors used here and the specific requirements regarding measurement accuracy in capturing the position and size of an object are comparable to those in the previous example of logistics automation.

[0028] The terms used in the claims should be interpreted in such a way as to give them the broadest possible reasonable interpretation in accordance with the foregoing description. For example, the use of the article "a" or "the" when introducing an element should not be interpreted as excluding a multitude of elements. Likewise, the mention of "or" should be interpreted as including a multitude of elements, so that the mention of "A or B" does not exclude "A and B" unless it is clear from the context or the preceding description that only one of A and B is meant.Furthermore, the phrase "at least one of A, B, and C" is to be understood as one or more elements from a group of elements consisting of A, B, and C, and not as requiring at least one of each of the listed elements A, B, and C, regardless of whether A, B, and C are related as categories or otherwise. Moreover, the mention of "A, B, and / or C" or "at least one of A, B, or C" should be interpreted as encompassing each individual unit of the listed elements, e.g., A; each subset of the listed elements, e.g., A and B; or the entire list of elements A, B, and C.

[0029] Further embodiments of the present disclosure are described below with reference to the figures. The representations in the figures are schematic and not to scale. BRIEF DESCRIPTION OF THE FIGURES Fig. Figure 1 shows a level measurement system with a level gauge. Fig. Figure 2 shows the temporal relationship between emitted measurement signals and time in an FMCW level gauge. Fig. Figure 3 shows a flowchart of a procedure according to one implementation. DETAILED DESCRIPTION OF EXECUTION FORMS

[0030] Fig. Figure 1 shows a level measurement system with a level gauge 100, a Cloud 200 and an external control unit 300.

[0031] The level gauge 100 includes, in particular, a computing arrangement 102 to which a computer-readable medium in the form of a memory 104 and a radar module 103 are connected. The radar module generates a radar measurement signal, which is transmitted via the antenna 101. The antenna 101 is the sensor described above.

[0032] The level gauge 100 can also have a communication antenna 105 to send level measurement data to the cloud 200 or the external computing unit 300.

[0033] The measuring system is capable of performing a low-energy radar measurement with reduced accuracy, whereby a change in the medium detected in this radar measurement triggers a more precise radar measurement.

[0034] It should be noted that when radar measurements are mentioned here and in the following, the statements can also apply to other measurements, such as ultrasound measurements.

[0035] Using this "shortened," less precise radar measurement, which requires significantly less energy, the level measurement system monitors changes in the medium, and in particular, changes in the fill level. Due to the significant reduction in energy per measurement, a much higher measurement rate is possible, allowing for more frequent monitoring of the medium. The term "measurement rate" here refers to the number of level measurements taken per unit of time, i.e., the frequency of measurements. A detected change in the medium triggers an event, such as the start of a more precise measurement.

[0036] A "shortened" radar measurement is characterized by a short measurement duration. Depending on the application, a change in fill level can be detected by the position of the echo and / or the evaluation of the phase.

[0037] Phase analysis cannot always be performed successfully because it is very sensitive. Under very static conditions, it allows even the smallest changes to be detected. Furthermore, it is susceptible to vibrations and can detect them.

[0038] The echo obtained through a Fourier transform is complex and is converted to a dB value by calculating its magnitude and taking its logarithm. The phase of the complex echo, however, reacts to the smallest changes. By considering the phase, it is possible to detect changes on the micrometer scale.

[0039] In static applications, e.g. liquids in a tank that is not subject to major vibrations, a slight inflow or outflow can be detected by the phase, which is not yet visible via the echo position alone.

[0040] Continuous, coarse, and low-energy radar measurement allows for the rapid detection of level changes. A higher-energy, more precise measurement can be performed when the medium changes.

[0041] An example of such a measurement procedure is in Fig. Figure 2 shows the process by plotting the frequency of the radar measurement signal against time. The first seven measurements occur very quickly in succession, but only last a relatively short time, so little energy is consumed for each individual measurement. If the level gauge then detects that the state of the medium has changed, for example, because its level has risen, a longer-lasting measurement is triggered, which covers a larger frequency range and is therefore more accurate.

[0042] The following describes a specific implementation example. An IoT sensor with a battery monitors a medium. Due to the limited battery capacity, precise radar measurements can only be performed at a rough interval of 5 to 60 minutes.

[0043] In applications where the fill level can change rapidly (e.g., stormwater retention basins), it is important to detect these changes quickly. Low-energy measurement allows for more frequent measurements and thus faster detection of changes. Low-energy measurement is characterized by shorter radar measurement durations and shorter activation / active periods for the radar subsystem, resulting in lower overall energy consumption. This usually leads to a decrease in the range resolution and accuracy of the system. Nevertheless, changes, provided they are not too small, are still sufficiently detectable.

[0044] A detected change in the medium can lead to a more precise measurement being performed, a notification being sent to the cloud, and / or the measurement process being modified in other ways. For example, more precise measurements can also be performed more frequently.

[0045] A change to the medium can also lead to several of the aforementioned actions simultaneously.

[0046] In another embodiment, the level gauge performs a coarse measurement every minute and a precise measurement every half hour. A detected change in the fill level triggers a precise measurement. This precise measurement confirms the change in the fill level, and the level gauge then switches its measurement sequence. It now measures exclusively with a precise measurement every minute. After several minutes in which no further change is detected, the level gauge returns to its initial mode, performing a coarse measurement every minute and a precise measurement every half hour.

[0047] Fig.Figure 3 shows a flowchart of a method according to one embodiment. In step 301, the level sensor acquires level measurement data of the medium with a first measurement accuracy. In step 302, the acquired level measurement data is evaluated by a computing arrangement, and in step 303, the computing arrangement detects a change in the medium based on the evaluation of the acquired level measurement data. Therefore, in step 304, the acquisition of level measurement data with a second measurement accuracy is triggered, the second measurement accuracy being greater than the first measurement accuracy.

[0048] In step 305, the level measuring device concludes that the level is no longer changing and begins again to record level measurement data of the medium with the first, lower accuracy.

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