Measurement apparatus

A measurement apparatus comprising at least one device interface adapted to connect an auxiliary measurement device and/or a device under test, DUT, to said measurement apparatus; a user interface adapted to input by a user settings for performing a measurement by said measurement apparatus and an artificial intelligence, AI, module adapted to provide current settings of said measurement apparatus, wherein said artificial intelligence, AI, module is machine learned on the basis of connected devices and/or settings during historic measurements performed by said measurement apparatus.

TECHNICAL FIELD

The invention relates to a method and system for performing an automatic configuration or reconfiguration of a measurement apparatus, in particular of a handheld test and measurement device using artificial intelligence.

TECHNICAL BACKGROUND

Measurement devices are used by technicians and operated mostly by repeating certain measurements multiple of times or by using standardized measurement settings (e.g. so-called wizard sets). These measurement settings simplify measurements by automating, standardizing and optimizing test sequences. After a measurement sequence has been configured by an expert, it can be transferred to measurement instruments in the field. An operator working in the field only needs to start the wizard set, select a measurement sequence and follow predefined instructions. A technician uses most of the time the same wizard sets in order to run tests or diagnostics on a wide range of devices under test DUTs. However, the effort to input the same settings in multiple tests is quite big even when just using wizard sets, especially when the input of the settings has to be repeated for multiple devices under test DUTs. The repetitive use of certain measurement modes requires a higher effort for inputting settings or wizard sets. This increases significantly the time required for performing measurements in the field.

Accordingly, there is a need to provide a method and apparatus which increases the efficiency for performing measurements and reduces the required measurement time.

SUMMARY OF THE INVENTION

The invention provides according to a first aspect of the present invention a measurement apparatus comprisingat least one device interface adapted to connect an auxiliary measurement device and/or a device under test to said measurement apparatus,a user interface adapted to input by a user settings for performing a measurement by said measurement apparatus and an artificial intelligence module adapted to provide current settings of said measurement apparatus, wherein said artificial intelligence module is machine learned on the basis of connected devices and/or settings during historic measurements performed by said measurement apparatus.

In a possible embodiment of the measurement apparatus according to the first aspect of the present invention, a measurement usage history including connected devices and/or settings of measurements performed by said measurement apparatus is recorded over time in a memory.

In a possible embodiment of the measurement apparatus according to the first aspect of the present invention, the measurement usage history of the measurement apparatus is recorded in a local memory of said measurement apparatus and/or in a remote database connectable to said measurement apparatus.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the settings input by the user via the user interface comprise measurement parameter settings and/or measurement mode settings.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the machine learned artificial intelligence module of the measurement apparatus comprises an artificial neural network.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the auxiliary measurement device connected to the measurement apparatus comprises a localization device.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the machine learned artificial intelligence module provides the current settings to control measurement functions of said measurement apparatus automatically when the measurement apparatus is switched on or is booted up.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the machine learned artificial intelligence module is adapted to prompt the user via the user interface of said measurement apparatus about available software options to perform the current measurement by said measurement apparatus.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the artificial intelligence module is machine learned on the basis of its recorded measurement usage history in a separate machine learning process.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the measurement apparatus comprises a user identification module adapted to identify a user on the basis of the measurement usage history and/or on the basis of a user identification input into the user interface of said measurement apparatus or by biometric user identification means of said measurement apparatus.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the artificial intelligence module is learned on the basis of the measurement usage history and/or a recorded behaviour of the identified user.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the measurement apparatus comprises a mobile handheld measurement apparatus for performing measurements in the field in an outdoor environment.

In a further possible embodiment of the measurement apparatus according to the first aspect of the present invention, the measurement apparatus comprises a stationary measurement apparatus for performing measurements in an indoor environment.

The invention further provides according to a further aspect a measurement system comprisingat least one measurement apparatus havingat least one device interface adapted to connect an auxiliary measurement device and/or a device under test to said measurement apparatus,a user interface adapted to input by a user settings for performing a measurement by said measurement apparatus and an artificial intelligence module adapted to provide current settings of said measurement apparatus, wherein said artificial intelligence module of said measurement apparatus is machine learned on the basis of connected devices and/or settings during historic measurements performed by said measurement apparatus,wherein said measurement system further comprises a database adapted to store the measurement usage history of the measurement apparatus.

The invention further provides according to a further aspect a method for performing a configuration of a measurement apparatus comprising the steps of:recording a measurement usage history of said measurement apparatus,machine learning an artificial intelligence module of said measurement apparatus on the basis of the measurement usage history of said measurement apparatus andgenerating automatically settings of said measurement apparatus by said machine learned artificial intelligence module when the measurement apparatus is activated.

In a possible embodiment of the method according to the third aspect of the present invention, the measurement usage history including devices connected to said measurement apparatus and settings of measurements performed by said measurement apparatus is recorded in a local memory of said measurement apparatus and/or in a remote database connectable to the measurement apparatus.

DETAILED DESCRIPTION OF EMBODIMENTS

As can be seen from the block diagram ofFIG. 1, the measurement apparatus1according to the present invention comprises in the illustrated embodiment at least one device interface2adapted to connect one or more auxiliary measurement devices and/or devices under test DUT to said measurement apparatus1.

In the illustrated embodiment, the measurement apparatus1comprises device interfaces2-1,2-2. . .2-n. The number n of the device interfaces2-ican vary depending on the type of the respective measurement apparatus1. The device interfaces2-ican comprise interfaces for auxiliary or peripheral devices and device interfaces2-cfor one or more devices under test7. The auxiliary measurement device can for instance comprise a localization device adapted to localize the measurement apparatus1in the field. The localization device can for instance comprise a GPS receiver providing coordinates of the measurement apparatus1.

The auxiliary measurement device can further comprise a sensor device adapted to provide sensor data to the measurement apparatus1. The measurement apparatus1as illustrated inFIG. 1can be a mobile handheld measurement apparatus for performing measurements in the field in an outdoor environment. Alternatively, the measurement apparatus1can also comprise a stationary measurement apparatus1for performing measurements in an indoor environment.

The measurement apparatus1comprises besides the device interfaces2-ia user interface3adapted to input user settings for performing a measurement by said measurement apparatus1. The user interface3can comprise a graphical user interface GUT comprising a screen or display adapted to output measurement results to a user. The user input3can also comprise a touchscreen adapted to input current user settings for performing measurements. The user interface3can be integrated in the measurement apparatus1as illustrated in the embodiment ofFIG. 1. Alternatively, the user interface3can form an auxiliary measurement device connected via a device interface2-ito the measurement apparatus1. The user interface3is adapted to input settings by a user wherein the settings are used for performing a measurement by the measurement apparatus1, for instance in relation to a device under test DUT.

The measurement apparatus1comprises an artificial intelligence module4adapted to provide current settings of the measurement apparatus1. The artificial intelligence module4is machine learned on the basis of connected devices and/or settings during historic measurements performed by said measurement apparatus1. In the illustrated embodiment ofFIG. 1, the measurement apparatus1comprises a local memory5. The measurement usage history of the measurement apparatus1can be recorded in the local memory5of the measurement apparatus1. In an alternative embodiment, the measurement usage history can also be recorded in a remote database13to which the measurement apparatus1has access as also illustrated inFIG. 3. The measurement usage history including the connected auxiliary measurement devices and/or connected devices under test7can be recorded in the local memory5of the measurement apparatus1and/or in the remote database13of the system. In a possible embodiment, the local memory5is integrated in the measurement apparatus1as shown inFIG. 1. In a possible embodiment, the local memory5can also be connected to the measurement apparatus1via a device interface2. The local memory5can for instance comprise a data carrier such as a memory stick connectable to the measurement apparatus1via a device interface2. The artificial intelligence module4is learned on the basis of the measurement usage history in a machine learning process. In a possible embodiment, the artificial intelligence module4can be pretrained in a training phase to get an initial setting and then further machine learned during its operation lifetime using data recorded in the memory5. In the illustrated embodiment ofFIG. 1, the artificial intelligence module4provides an output applied to an internal control unit6of the measurement apparatus1which controls the internal measurement functions of the measurement apparatus1in response to the output data provided by the artificial intelligence module4.

The settings input by a user via the user interface3can comprise measurement parameter settings and/or measurement mode settings. The measurement parameter settings are used to adjust measurement parameters related to a current measurement setup. The measurement mode settings comprise different measurement modes and/or operation modes used by the measurement apparatus1to perform a measurement. In a possible embodiment, the machine learned artificial intelligence module4provides current settings to control measurement functions of the measurement apparatus1automatically when the measurement apparatus1is switched on or is booted up. In a possible embodiment, the user interface3comprises a switch which has a press button which can be used by the user to switch on the measurement apparatus1. When the measurement apparatus1is activated by the user the trained or machine learned artificial intelligence module4can provide current settings to control internally measurement functions of the measurement apparatus1. In a possible embodiment, the machine learned artificial intelligence module4is also adapted to prompt the user via the user interface3of the measurement apparatus1about available software options to perform a current measurement of the measurement apparatus1. The artificial intelligence module4is learned on the basis of the measurement usage history and/or a recorded behavior of an identified user operating the measurement apparatus1.

The artificial intelligence module4may use algorithms to parse data and to learn from said parsed data. The artificial intelligence module4then applies what it has learned to make informed decisions. The artificial intelligence module4can implement an algorithm to parse the data that was generated when a technician or user was previously using the same measurement apparatus1. The artificial intelligence module4can learn frequently used settings, frequently used modes, and/or frequently pressed user interface elements such as pressed buttons, etc. The artificial intelligence module4can recommend from the machine learning process to the user, for instance which page to open once the apparatus1is booted up or once a specific button or user interface element has been pressed by the user. For instance, if a user is always using a Smith chart when operating the measurement apparatus1, the next time the measurement apparatus1boots up a machine learning algorithm implemented in the artificial intelligence module4will boot up the measurement apparatus1in a Smith chart operation mode, since it has learned that this was the mode frequently used by that technician. Other settings may remain set at default. While machine learning can be used to provide algorithms that parse, learn and apply what they had learned, deep learning can be used to structure these algorithms in layers to create an artificial neural network. The artificial intelligence module4comprises in a preferred embodiment at least one artificial neural network that can learn and make intelligent decisions on its own. In this embodiment, the deep learning artificial neural network does not just recommend a correct page once the measurement apparatus1boots up or once a specific button has been pressed but it can instead fill up the settings with values that it determines as being correct in the given situation. By using a deep learned artificial intelligence module4, for example a user having used always a Smith chart when operating the measurement apparatus1the next time the same measurement apparatus1is booted up, the deep learning algorithm executed by the artificial intelligence module4of the measurement apparatus1does boot up the measurement apparatus1in a Smith chart mode since it has been learned that this was the frequently used mode by the user. Further, the artificial intelligence module4will also set the most used settings just as points, start and stop frequency, markers, etc., i.e. filling up the current settings with values that the artificial intelligence module4determines as being correct in the given measurement setup. The artificial intelligence module4can also prompt the user about available software options that the user may find useful when doing certain measurements. The artificial intelligence module4can adapt dynamically to a user's behaviour by profiling its usage and predicting what settings will be used the next time the measurement apparatus1is powered up. In this way, routine work of inputting settings into the measurement apparatus1can be avoided and the required measurement time can be reduced.

FIG. 2shows a further possible exemplary embodiment of a measurement apparatus1according to the first aspect of the present invention. The measurement apparatus1, in particular a measurement apparatus which is assigned to a specific user performing measurements in the field, can be trained not only on the measurement usage history of the measurement apparatus but also on the recorded measurement behavior of the respective user. Users such as technicians show a user-specific behavior when inputting settings into the measurement apparatus1. In a possible embodiment, the measurement apparatus1comprises a user identification module adapted to identify a user on the basis of the measurement usage history and/or on the basis of a user identification input into the user interface3. Further, the measurement apparatus1may comprise biometric user identification means to identify the current user of the measurement apparatus1(e.g. finger print sensor or voice recognition). In the illustrated embodiment ofFIG. 2, the artificial intelligence module4can comprise a first trained artificial neural network4A and a second trained artificial neural network4B. The first artificial neural network4A is trained on a measurement usage history of user settings irrespective of what kind of users have used the measurement apparatus1. The second artificial neural network4B can be trained on a recorded behavior of the specific identified user currently operating the measurement apparatus1. The output of the two trained artificial neural networks4A,4B can be combined (e.g. concatenated) in a possible embodiment to provide a result applied to the internal control unit6of the measurement apparatus1triggering matching measurement settings of the measurement apparatus1. In the illustrated embodiment ofFIG. 2, current measurement settings are adjusted according to the learned usage profile of the identified user. The artificial intelligence module4can predict what kind of operation mode and/or parameter settings a user requires when using the measurement apparatus1. The measurement apparatus1can also suggest available software options that can be loaded by the user as to help him in performing data analysis. Each artificial neural network4A,4B can comprise an input layer IL, several hidden layers HL and an output layer OL providing an output feature vector applied to the internal control unit6which performs internal control functions to execute measurements in response to the received feature vector.

FIG. 3shows a schematic diagram for illustrating a possible exemplary embodiment of a measurement system according to the present invention. In the illustrated embodiment, the measurement apparatus1is connected via a device interface2to a device under test DUT7. The device under test7can comprise for instance a printed current board PCB of a machine to be tested. Further, several devices under test7can be connected to the measurement apparatus1in parallel. In the illustrated example, a sensor device8can be connected to another device interface2-iof the measurement apparatus1. The sensor device8can for instance comprise a current probe used to measure an electrical current I flowing within the device under test7. A plurality of different kinds of sensor devices8can be connected to the measurement apparatus1such as voltage sensors, temperature sensors etc. In the illustrated example ofFIG. 3, a localization device9such as a GPS receiver can be connected to the measurement apparatus1as well. The localization device9can provide localization data indicating a current position of the apparatus1in the field. In the illustrated setup ofFIG. 3, the measurement apparatus1is connected via a further device interface2to a data network10such as the Internet. A backend platform11can comprise a web server12having access to a database13. In a possible embodiment, the measurement usage history of the measurement apparatus1can be recorded in the remote database13of the system illustrated inFIG. 3. The measurement usage history of the measurement apparatus1can be used to train the artificial intelligence module4of the measurement apparatus1in the background continuously. The system shown inFIG. 3can be used to generate automatically user setting configurations for the measurement apparatus1. The measurement apparatus1can comprise a processing unit where the artificial intelligence module4is implemented. The processing unit is able to adjust user settings and user configurations based on information about connected sensor and/or auxiliary measurement devices and/or measured devices under test data in combination with information of the measurement apparatus usage history. The user settings can be saved or memorized after each measurement process.

FIG. 4shows a flowchart of a possible exemplary embodiment of a method for performing a configuration of a measurement apparatus1such as the measurement apparatus1illustrated inFIGS. 1 to 3.

In a first step S1, a measurement usage history of the respective measurement apparatus1can be recorded. The measurement usage history can be recorded in a local memory5of the respective measurement apparatus1and/or in a remote database13of a backend platform11. The measurement usage history can be stored in a memory area of the database13associated with a unique measurement apparatus identifier of the measurement apparatus1.

The artificial intelligence module4of the measurement apparatus1is machine learned in a further step S2on the basis of the stored measurement usage history of the measurement apparatus1. The machine learning process can be performed in an initial training phase to provide an initial setting of the measurement apparatus1. Further, the machine learning can be performed during the operation of the measurement apparatus1continuously in the background to improve the performance of the artificial intelligence module4. The machine learning can be performed in a supervised or unsupervised manner.

In a further step S3, the settings of the measurement apparatus1are generated automatically by the machine learned artificial intelligence module4when the measurement apparatus1is activated.

FIG. 5shows a flowchart of a further possible embodiment of the method according to the present invention. The process is initiated in step S50. In a first step S51, it is checked whether enough historic data has been collected for the respective measurement apparatus1. Accordingly, it is checked whether the measurement usage history of the measurement apparatus1identified by the measurement apparatus identifier comprises enough data to train the artificial intelligence module4in such a way that it provides a sufficient performance. If not enough measurement usage history data is available, a process is triggered in step S52to get more measurement usage history data, e.g. from the remote database13. If enough measurement usage history data is available, it can be checked in a further step S53, whether peripheral devices have been connected to the apparatus1. Further, it can be identified in step S54, which applications have been used most by the user to perform measurements in the past. In a further step S55, it can be inquired what kind of device under test7has been connected to the measurement apparatus1. In a step S56, the artificial intelligence module4can provide automatically current settings of the measurement apparatus1on the basis of the connected devices, i.e. connected peripheral devices and/or devices under test7and/or other settings during historic measurements performed by the same measurement apparatus1. It can further launch required user applications for the current measurement.

FIG. 6shows a front view on the measurement apparatus1with a front panel user interface3. In the illustrated example, the measurement apparatus1is a handheld spectrum analyzer having a touch-sensitive display area3A which can be used to show a spectrum to a user. The user interface3further comprises in the illustrated example soft keys3B as well as system keys3C. Further, the user interface3can comprise a key pad3D with function keys and a rotary knob3E with ENTER function. The key pad3D can include an alphanumeric key pad and a power key to switch on the measurement apparatus1. The measurement apparatus1further comprises a housing with a plurality of different kinds of device interfaces such as an RF input2A, a BNC connector2B, a platform interface2C and USB ports2D on top of the housing of the measurement apparatus1. Further, the measurement apparatus1can comprise a DC connector2E. Other device interfaces2F include an interface for a local area network LAN and one or more several USB ports2F. The handheld spectrum analyzer1illustrated inFIG. 6comprises a processing unit with an implemented artificial intelligence module4adapted to provide current settings of the spectrum analyzer1. The artificial intelligence module4of the spectrum analyzer1illustrated inFIG. 6is machine learned on the basis of connected devices and/or settings input by a user during historic measurements performed by the measurement analyzer apparatus1. The measurement results can be saved automatically as soon as the measurement has been completed. The measurement results or measurement data and/or the associated measurement settings can be transferred to a tablet or PC and can also be stored in the remote database13of the system. The measurement time is reduced thanks to the automatic instrument setting performed by the trained artificial intelligence module4of the measurement apparatus1. As soon as a user presses the power key of the key pad3D of the user interface3, the measurement apparatus1is switched on and the trained machine learned artificial intelligence module4provides an output applied to the internal control unit6to provide current settings to control measurement functions of the measurement apparatus1. Further, the artificial intelligence module4may launch required user applications.