Abstract: Documenting logbook data by one or more first field device, wherein each of the first field devices has a first resource power, wherein a second field device is provided with a second resource power is provided. The second resource power is greater than the first resource powers, wherein each of the first field devices continuously generates corresponding logbook data that contains security-relevant data and/or data relating to the operation of the corresponding first field device, said method comprising: establishing a secure communication link between one or more of the first field devices and the second field device; transmitting the current logbook data from the first field device or each of the first field devices at predetermined times via the communication link; accumulating the current logbook data by the second field device and storing the accumulated logbook data in a memory unit of the second field device, and a corresponding system.

Abstract: A method for verifying a first time recording unit of a field device that supplies the field device with a first date and/or a first time. The method includes receiving a ticket at the field device that comprises cryptographically secured information and a time stamp. The time stamp is obtained from a second time recording unit and contains a second date and/or second time. The method includes carrying out a trust assessment of the time stamp, wherein the trustworthiness of the second time recording unit is checked against the first time recording unit. If the trust assessment shows that the second time recording unit is more trustworthy than the first time recording unit, the method includes comparing the first date or the first time with the second date or the second time, and creating a warning message if a deviation greater than a predetermined factor is determined.

Abstract: A method includes, in response to at least one convergence criterion not being met: receiving a labeled dataset that includes a plurality of labeled samples; receiving an unlabeled dataset that includes a plurality of unlabeled samples; identifying a plurality of labeled-unlabeled sample pairs; applying a data augmentation transformation to each labeled sample and each corresponding unlabeled sample; computing, for each least one labeled-unlabeled sample pair, latent representation spaces using the machine learning model; generating, using the machine learning model, a label prediction for each unlabeled sample for each labeled-unlabeled sample pair; computing a loss function for each labeled-unlabeled sample pair of the plurality of labeled-unlabeled sample pairs based on respective latency representation spaces and respective label predictions; applying an optimization function to each respective loss function; and updating a weight value for each labeled-unlabeled sample pair of the plurality of labeled-un

Abstract: A method for identifying a field device for automation technology includes providing an operator unit, which includes at least one order ticket with an access authorization for at least one of the field devices. The method also includes searching for accessible field devices with the operator unit using a wireless communications protocol. For each of the field devices accessible from the operator unit, a temporary communication connection between the operator unit and the relevant field deices is established using a wireless communications protocol, the order ticket is transmitted to the relevant field device, the access authorization for validity is checked by the field device, and the field devices that were able to successfully carry out the check for the validity of the access authorization are signaled.

Abstract: A method for operating a field device between an operating unit and the field device includes creating an order ticket using a ticket server. The order ticket contains an authorization for the operating unit to carry out defined tasks on the field device and information on the temporal validity. The method also includes transferring the order ticket to an operating unit and to the field device. The operating unit is registered with the field device if the field device verifies the order ticket is valid. If the order ticket is successfully verified, execution of the tasks defined in the order ticket on the field device is authorized for a predetermined period of time, which has an end point that is determined by the information on time validity contained in the order ticket. The authorization is automatically terminated by the field device after the predetermined period of time has elapsed.

Abstract: Managing field device users includes establishing a first communications link between a transport medium and user database and sending a ticket from the database to the transport medium via the first communications link. The ticket includes first user data, field device identification information, and second user data. The first user data cannot be processed by the field device and the second user data can be processed by the field device. The user is authenticated to the transport medium based upon the first user data. A field device specific operating telegram is created using the transport medium if the user has been authenticated, wherein the operating telegram contains the second user data. The operating telegram is sent to the field device via a second communications link, verifying the second user data, and granting access from the transport medium to the field device based upon valid second user data.

Abstract: A method for integrating a field device into an operating system is provided. The operating system includes a ticket server communicating with a transport medium. The ticket server and existing field devices process tickets, which contain a cryptographically secured item of information and transmit the created tickets to the transport medium, where the transport medium transmits the tickets to the recipient(s) and the recipient(s) check(s) the authenticity and integrity of the tickets. The method includes placing an order for the field device and generating registration data by a manufacturer's server. The registration data comprise identification information of the field device and a first cryptographically relevant item of information. The method also includes registering the field device as an existing field device on the ticket server. The registration data are transmitted to the ticket server and a second cryptographically relevant information of the ticket server is stored in the field device.

Abstract: A method for verifying a first time recording unit of a field device that supplies the field device with a first date and/or a first time. The method includes receiving a ticket at the field device that comprises cryptographically secured information and a time stamp. The time stamp is obtained from a second time recording unit and contains a second date and/or second time. The method includes carrying out a trust assessment of the time stamp, wherein the trustworthiness of the second time recording unit is checked against the first time recording unit. If the trust assessment shows that the second time recording unit is more trustworthy than the first time recording unit, the method includes comparing the first date or the first time with the second date or the second time, and creating a warning message if a deviation greater than a predetermined factor is determined.

Abstract: The present disclosure comprises a computer-implemented process for training an AI model, comprising providing training data, wherein the training data comprise input data and output data. The input data comprise identification and/or function data of a plurality of field devices, and the output data each comprise a result associated with the field devices as to whether or not logging into a user management system was permitted. The training data is fed to the AI model. The process also includes training the AI model using machine learning based upon the training data to identify one or more relationships between the identification and/or function data and the associated results, and using the trained AI model in a process for user management of field devices in automation technology.

Abstract: A method for transmitting security settings between a first automation engineering field device and a second automation engineering field device includes: identifying and authenticating an operator by means of a service unit; assigning an authorization group based on the identifying and authenticating of the operator; in the case, in which the operator is assigned the administrator authorization group: encrypting at least one security setting, which is present in cleartext, by the first field device; exporting the encrypted security setting; importing the encrypted security setting into the second field device; decrypting the encrypted security setting; loading the decrypted security setting into the data memory of the second field device and operating the second field device with the loaded security setting of the first field device.

Abstract: A method for generating comparison pairs and distributing them to individual comparison requests for experiment participants for subjective individual ratings of pair comparisons. The method includes: providing a data set comprising sensory samples; identifying potentially promising comparison samples for each sample based on a metric, wherein the metric is based on a relationship between a possible estimation of the rating by experiment participants and at least one quantitative characteristic of the sample; distributing a predetermined number of overall comparisons to the potentially promising comparison pairs; generating the comparison requests for experiment participants by assigning the comparison pairs to the comparison requests, taking into account the following conditions to the effect that each sample is not provided more frequently than once in a comparison request, each sample is equally often provided for predetermined subgroups of the entirety of all experiment participants.

Abstract: A method for adjusting a noise generated by a device. The method includes: ascertaining, for each device feature, a dependence of a contribution of the device feature to a noise evaluation characteristic number on the value of the device feature by training an EBM with combinations of values for the device features and, for each of the combinations, an associated value of the noise evaluation characteristic number, each of a plurality of decision trees of the EBM ascertaining a contribution to the noise evaluation characteristic number for a device that has the value for the device feature; ascertaining values of the device features for a device of which the noise is to be adjusted; ascertaining changes for one or more of the device features to the values of the device features for improving the noise evaluation characteristic number; and adjusting the device according to the ascertained changes.

Abstract: The invention relates to a field device of automation technology, wherein the field device has at least one sensor designed to detect a physical, chemical, or biological variable of a process engineering process and/or at least one actuator designed to influence a physical variable of a process engineering process, or such a sensor and/or actuator is assigned to the field device, wherein the field device has at least one electronics unit for operating the field device, wherein at least two operating phases are defined in the field device, wherein one or more authorization information items are assigned to each of the operating phases, wherein the authorization information items define at least one user authorized to access the field device and/or at least one permitted operating action on the field device, and wherein the electronics unit is designed to operate the field device in a current operating phase, wherein the current operating phase is one of the defined operating phases, as well as to a method for

Abstract: Machine learning is used to classify a pleasantness of a sound emitted from a device. A plurality of pleasantness ratings from human jurors are received, each pleasantness rating corresponding to a respective one of a plurality of sounds emitted by one or more devices. Differences between each pleasantness rating and each of the other pleasantness ratings is determined via pairwise comparisons. These differences are converted into binary values based on which pleasantness rating is higher or lower in each comparison. Measurable sound qualities are received associated with the sounds. Second differences between each of the measurable sound qualities and every other of the plurality of measured sound qualities is determined in pairwise fashion. A classification model is trained to classify sound pleasantness by comparing the binary values with the second differences.

Abstract: Machine learning is used to predict a pleasantness of a sound emitted from a device. A plurality of pleasantness ratings from human jurors are received, each pleasantness rating corresponding to a respective one of a plurality of sounds emitted by one or more devices. A microphone system detects a plurality of measurable sound qualities (e.g., loudness, tonality, sharpness, etc.) of these rated sounds. A regression prediction model is trained based on the jury pleasantness ratings and the corresponding measurable sound qualities. Then, the microphone system detects measurable sound qualities of an unrated sound that has not been rated by the jury. The trained regression prediction model is executed on the measurable sound quality of the unrated sound to yield a predicted pleasantness of the unrated sound.

Abstract: A system includes a processor, wherein the processor is programmed to receive sound information and vibrational information from a device in a first environment, generate a training data set utilizing at least the vibrational information and a sound perception score associated with the corresponding sound of the vibrational information, wherein the training data set is fed into an un-trained machine learning model, in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model, receive real-time vibrational information from the device in a second environment, and based on the real-time vibrational information as an input to the trained machine learning model, output a real-time sound perception score indicating characteristics associated with sound emitted from the device.

Abstract: A system includes a processor in communication with one or more sensors. The processor is programmed to receiving, from the one or more sensors, vibrational information and sound information associated with the vibrational information from a test device, generating a training data set utilizing at least the vibrational data and the sound information associated with the vibrational data, wherein the training data set is sent to a machine learning model configured to output sound predictions, receiving real-time vibrational data from a run-time device running an actuator or electric dive emitting the real-time vibrational data, and based on the machine learning model and the real-time vibrational data, output a sound prediction indicating a purported sound emitted from the run-time device.

Abstract: A method includes, in response to at least one convergence criterion not being met: receiving a labeled dataset that includes a plurality of labeled samples; receiving an unlabeled dataset that includes a plurality of unlabeled samples; identifying a plurality of labeled-unlabeled sample pairs; applying a data augmentation transformation to each labeled sample and each corresponding unlabeled sample; computing, for each least one labeled-unlabeled sample pair, latent representation spaces using the machine learning model; generating, using the machine learning model, a label prediction for each unlabeled sample for each labeled-unlabeled sample pair; computing a loss function for each labeled-unlabeled sample pair of the plurality of labeled-unlabeled sample pairs based on respective latency representation spaces and respective label predictions; applying an optimization function to each respective loss function; and updating a weight value for each labeled-unlabeled sample pair of the plurality of labeled-un

Abstract: A method includes receiving a labeled dataset that includes a plurality of labeled samples and initially training the machine learning model using the labeled dataset. The method also includes receiving an unlabeled dataset that includes a plurality of unlabeled samples. The method also includes computing latent representation spaces for each respective sample of the plurality of labeled samples and each respective sample of the plurality of the unlabeled samples. The method also includes generating a k-nearest neighbor similarity graph based on the latent representation spaces, generating a combined similarity graph by augmenting the k-nearest neighbor similarity graph using an expert-derived similarity graph, and propagating, using the combined similarity graph, labels to each respective sample of the plurality of unlabeled samples.

Abstract: Disclosed is a method for obtaining emergency device access for field devices in process automation technology by means of a security token. The method includes the field device receiving and storing a public key before an emergency occurs; connecting the security token to the field device; sending a challenge from the field device to the security token; calculating a response to the challenge by means of a private key on the security token and sending the response from the security token to the field device; and granting emergency access if the response is correct.