Abstract: A method performed by a node for handling drift in data. The node obtains a dataset including a plurality of datapoints corresponding to a plurality of values of one or more dependent variables for a plurality of first features over a time period. The node determines, using machine learning and explainability, in the absence of determining whether or not the plurality of datapoints has a drift, whether or not there has been a change in respective one or more characteristics of a subset of the plurality of first features having a largest contribution to a variability of the datapoints in the plurality of datapoints based on a threshold from a first time period to a second time period. The node then initiates application of a drift policy on the plurality of datapoints based on a result of the determination.

Abstract: There is provided a method for consolidating explanations associated with actions proposed based on a current state of a system and an intent. The method includes acquiring first and second explanations, the first and second explanations being associated with a proposed action or with different actions, wherein each of the first and second explanations includes one or more constraints, combining constraints from the first and second explanations to form a set of constraints D, generating a planning problem P=<K, A, I, G, Cost>, wherein K consists of a set of predicates F and the set of constraints D, wherein A represents a set of possible actions, I represents an initial state of the system, G represents a goal state of the system, and Cost represents cost values associated with each constraint, and determining a solution for the planning problem.

Abstract: Methods and systems for power supply unit (PSU) control. A method includes measuring one or more properties of the PSU to obtain property measurements, and initiating transmission of the property measurements to a machine learning (ML) agent hosting a trained ML model. The method further includes receiving the property measurements at the ML agent, and processing the received property measurements using the trained ML model to generate suggested actions to be taken by the PSU. The method further includes predicting the effect of each of the suggested actions on the measured PSU properties, and selecting a subset of the suggested actions predicted to have a significant impact on the measured PSU properties. The method further includes initiating transmission of the selected subset of suggested actions to the PSU, and performing, at the PSU, the selected subset of suggested actions.

Abstract: According to one or more embodiments, a node (15) for a communication network is provided. The node includes processing circuitry (98) configured to determine a plurality of key performance indicators, KPIs, for a plurality of cells in the communication network, predict a trend of values for each of the plurality of KPIs, the predicted trend of values indicating at least one predicted network attribute, and perform at least one action based at least in part on the at least one predicted network attribute.

Abstract: Methods and systems for power supply unit (PSU) control. A method includes measuring one or more properties of the PSU to obtain property measurements, and initiating transmission of the property measurements to a machine learning (ML) agent hosting a trained ML model. The method further includes receiving the property measurements at the ML agent, and processing the received property measurements using the trained ML model to generate suggested actions to be taken by the PSU. The method further includes predicting the effect of each of the suggested actions on the measured PSU properties, and selecting a subset of the suggested actions predicted to have a significant impact on the measured PSU properties. The method further includes initiating transmission of the selected subset of suggested actions to the PSU, and performing, at the PSU, the selected subset of suggested actions.

Abstract: The present disclosure relates to monitoring a condition of an electric drive in an industrial network. A method comprises obtaining values of input parameters, state parameters, and one or more temperatures associated with one or more components, at a first time instant. The method further comprises estimating values of the state parameters at a second time instant with a first layer of a state space model of the electric drive and the values of input parameters at the first time instant. In addition, the method comprises estimating values of the one or more temperatures at a third time instant with a second layer of the model, the values estimated for the state variables and the values of the temperatures at the first time instant. A condition of the electric drive is determined from the values of the temperatures estimated for the third time instant and one or more predetermined thresholds.

Abstract: One illustrative method comprises retrieving a set of drive information associated with an operation of an electric drive in a time period from a drive control unit and one or more sensor units. The method further includes obtaining reliability information associated with the electric drive from a server. The reliability information is computed in the server, with the set of drive information and a model corresponding to a drive type of the electric drive. The reliability information includes a probability of occurrence of the abnormal condition in a specified future time period. The method further includes, providing at least one condition indication regarding the abnormal condition to one of a human machine interface and a drive controller based on the probability of occurrence of the abnormal condition.

Abstract: One illustrative method comprises retrieving a set of drive information associated with an operation of an electric drive in a time period from a drive control unit and one or more sensor units. The method further includes obtaining reliability information associated with the electric drive from a server. The reliability information is computed in the server, with the set of drive information and a model corresponding to a drive type of the electric drive. The reliability information includes a probability of occurrence of the abnormal condition in a specified future time period. The method further includes, providing at least one condition indication regarding the abnormal condition to one of a human machine interface and a drive controller based on the probability of occurrence of the abnormal condition.

Abstract: The present disclosure relates to monitoring a condition of an electric drive in an industrial network. A method comprises obtaining values of input parameters, state parameters, and one or more temperatures associated with one or more components, at a first time instant. The method further comprises estimating values of the state parameters at a second time instant with a first layer of a state space model of the electric drive and the values of input parameters at the first time instant. In addition, the method comprises estimating values of the one or more temperatures at a third time instant with a second layer of the model, the values estimated for the state variables and the values of the temperatures at the first time instant. A condition of the electric drive is determined from the values of the temperatures estimated for the third time instant and one or more predetermined thresholds.