In some implementations, a control system may obtain historical data associated with usage of a distillation column during a historical time period. The control system may configure a prediction model to monitor the distillation column for a hazardous condition. The prediction model may be trained based on training data that is associated with occurrences of the hazardous condition. The control system may monitor, using the prediction model, the distillation column to determine a probability that the distillation column experiences the hazardous condition within a threshold time period. The prediction model may be configured to determine the probability based on measurements from a set of sensors of the distillation column. The control system may perform, based on the probability satisfying a probability threshold, an action associated with the distillation column to reduce a likelihood that the distillation column experiences the hazardous condition within the threshold time period.

A state display device 100 includes a display device 30 for displaying a temporal change of a first parameter representing an operation state of a thermal power generation facility 10, and a correlation between the first parameter and a second parameter representing an operation state of the thermal power generation facility 10, an input device 70 for respectively selecting and inputting a first period t1 and a second period t2 different from the first period t2 in the temporal change of the first parameter, and a processing device 50. The processing device 50 is configured to display the temporal change of the first parameter on the display device 30, and to display, on the display device 30, the correlation between the first parameter and the second parameter in each of the first period t1 and the second period t2 input via the input device 70.

A method for derivation of a machine signature includes receiving sensor information from a primary sensor, where the primary sensor is positioned to receive information from a portion of an industrial operation, and receiving sensor information from one or more secondary sensors. The secondary sensors are arranged to provide additional information about the industrial operation indicative of current operating conditions of the industrial operation. The method includes using the sensor information from the secondary sensors and machine learning to determine if the portion of the industrial operation is operating in a normal condition and, in response to determining that the portion of the industrial operation is operating normally, using sensor information from the primary sensor during the normal operating condition to derive a primary sensor signature for the sensor information from the primary sensor.

A system and method for assessing the effectiveness of automation and control units (HVAC, Elevator, water supply, etc.) implemented in a Building Management System (BMS) of a building is illustrated. Initially, the system reads/receives a metadata corresponding to all the sensing and control requirements based on the design and usage of the building, and further, assesses the implementation of the control routines for determining how the automation objectives are being met in the building by each of the currently implemented automation and control units in the building. The system further assesses how all the automation and control units work together in tandem. This assessment is then interpreted on based on different vectors. Finally, the system identifies gaps based on the assessment and generates recommendations to address these gaps.

A method for derivation of a machine signature includes receiving sensor information from a primary sensor, where the primary sensor is positioned to receive information from a portion of an industrial operation, and receiving sensor information from one or more secondary sensors. The secondary sensors are arranged to provide additional information about the industrial operation indicative of current operating conditions of the industrial operation. The method includes using the sensor information from the secondary sensors and machine learning to determine if the portion of the industrial operation is operating in a normal condition and, in response to determining that the portion of the industrial operation is operating normally, using sensor information from the primary sensor during the normal operating condition to derive a primary sensor signature for the sensor information from the primary sensor.

A diagnosis apparatus includes: a sensor that detects diagnosis target information generated by a diagnosis target device; a threshold setting unit that sets a threshold for the diagnosis target information; and a diagnosis unit that diagnoses the diagnosis target device based on the diagnosis target information detected by the sensor and the threshold, in which the threshold setting unit sets the threshold based on the diagnosis target information in a predetermined period before a diagnosis time point, and the diagnosis unit performs diagnosis based on a current time point threshold set at the diagnosis time point and at least one past threshold set in the past.

A controller for controlling a system having uncertainties in its dynamics subject to constraints on an operation of the system is provided. The controller is configured to acquire historical data of the operation of the system, and determine, for the system in a current state, a current control action transitioning a state of the system from the current state to a next state. The current control action is determined according to a robust and constraint Markov decision process (RCMDP) that uses the historical data to optimize a performance cost of the operation of the system subject to an optimization of a safety cost enforcing the constraints on the operation, wherein a state transition for each of state and action pairs in the performance cost and the safety cost is represented by a plurality of state transitions capturing the uncertainties of the dynamics of the system.

The present application generally pertains to scaling of a production process to produce a chemical, pharmaceutical and/or biotechnological product and/or of a production state of a respective production equipment. Particularly, there is provided a computer-implemented method of scaling a production process to produce a chemical, pharmaceutical and/or biotechnological product, the scaling being from a source scale to a target scale, wherein the production process is defined by a plurality of steps specified by one or more process parameters controlling an execution of the production process, the method comprising: (a) retrieving: parameter evolution information that describes the time evolution of the process parameter(s); a plurality of recipe templates, wherein a recipe comprises the plurality of steps defining the production process, and wherein a recipe template is a recipe in which at least one of the process parameters specifying the plurality of steps is a parameter being variable and having no predetermined value at the outset; (b) receiving: a source setup specification of a source setup to be used for executing the production process at the source scale, the source setup specification comprising the source scale value; a target setup specification of a target setup to be used for executing the production process at the target scale, the target setup specification comprising the target scale value; a source recipe defining the production process at the source scale; at least one acceptability function defining conditions for the values of the process parameter(s) at the source scale and/or at the target scale; (c) simulating the execution of the production process at the source scale using the source setup specification, the source recipe and the parameter evolution information; (d) determining, from the simulation, one or more source trajectories for the process parameter(s), wherein a trajectory corresponds to a time-based profile of values recordable during the simulated execution of the production process; (e) performing a target determination step comprising: selecting a recipe template pertinent to the production process out of the plurality of recipe templates; providing an input value for the at least one variable parameter in the selected recipe template; simulating the execution of the production process at the target scale using the target setup specification, the selected recipe template, the input value for the at least one variable parameter and the parameter evolution information; determining, from the simulation, one or more target trajectories for the process parameters; comparing the source trajectory(ies) and the target trajecto

A method includes obtaining data associated with operation of a vehicle and determining a first operational phase of the vehicle based on the data. The method includes identifying a candidate operational phase transition from the first operational phase to a candidate operational phase based on a first portion of the data satisfying a first condition associated with the candidate operational phase, the first portion of the data associated with a first time. The method includes evaluating a second portion of the data based on a second condition associated with the candidate operational phase, the second portion of the data associated with a second time that is subsequent to the first time. The method further includes, based on the second condition being satisfied, generating an operational phase transition indication associated with the first time and that indicates an operational phase transition to the candidate operational phase.

A method for analyzing data onboard an aircraft is provided. The method obtains aircraft parameter data; identifies a current phase of flight; determines standard operating procedure (SOP) compliance onboard the aircraft, based on the aircraft parameter data and the current phase of flight; identifies relevant safety events, based on the aircraft parameter data and the current phase of flight; and presents data associated with the SOP compliance and the relevant safety events.