Disclosed are systems, methods, and non-transitory computer-readable media for determining diagnostic coverage for achieving functional safety. A diagnostic coverage determination system employs an optimized process for efficiently determining a diagnostic coverage level of an electronic circuit. The diagnostic coverage determination system generates an optimized netlist that includes a reduced number of nodes by applying one or more node reduction techniques. The diagnostic coverage is determined based on the optimized netlist, thereby reducing the number of nodes that are injected with faults.

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 test system is includes a management server which is configured to provide predefined test instructions, a monitoring system, and at least one execution entity. The monitoring system is configured to convert test instructions provided by the management server into operating instructions for setting a test configuration on a control unit of a system using predefined assignment logic. The at least one execution entity is configured to set the test configuration on the control unit of the system on the basis of operating instructions transmitted by the monitoring system to the at least one execution entity.

The methods provide for detecting at least one operational parameter of a functional unit of the plant and calculating a current value of at least a first statistical function of the operational parameter in a current temporal window, the current value of the first statistical function defining a first coordinate of a point of current operation of the functional unit. A step is also provided of verifying whether the point of current operation is within a range of allowable values of the first statistical function, the values contained in the range of allowable values corresponding to a correct operation of the functional unit. In case the point of current operation is outside the range of allowable values, the position is determined of the point of current operation with respect to the range of allowable values and a statistical diagnosis is provided of the cause of the deviation of the current value from the range of allowable values based on the position.

A device management system includes a management apparatus and a transmitting apparatus. The management apparatus manages device information. The device information is information related to a device. The transmitting apparatus transmits the device information to the management apparatus. The management apparatus or the transmitting apparatus changes a condition for transmitting the device information from the transmitting apparatus to the management apparatus, in accordance with execution of a new installation or a configuration change.

A data processing method includes a sampling step of obtaining time series data based on a measurement result of a physical quantity in a substrate processing apparatus, an evaluation value calculation step of obtaining an evaluation value of the time series data by comparing the time series data with reference data, and a sampling period control step of controlling a sampling period used in the sampling step for each time series data. In the sampling period control step, all sampling periods are controlled to a normal period in an initial state, and when the evaluation value of the time series data is abnormal, the sampling period used when obtaining the time series data is controlled to an abnormal period shorter than the normal period.

An air conditioner, as provided herein, may include a cabinet, an outdoor heat exchanger, an indoor heat exchanger, a compressor, an internal temperature sensor, and a controller. The controller may be configured to initiate a conditioning operation. The conditioning operation may include detecting a learning condition at the air conditioner, identifying a first operating mode, and initiating a learning event at the first operating mode. The conditioning operation may further include measuring performance during the learning event, recording a baseline variable based on the measured performance during the learning event, and measuring performance at the first operating mode. The conditioning operation may still further include recording an operational variable based on the measured performance at the first operating mode, comparing the operational variable of the first operating mode to the baseline variable of the first operating mode, determining a fault state based on the comparison, and recording the fault state.

A failure diagnosis system flexibly responds to a change in a diagnosis target by using a difference in measurement data before and after maintenance in predictive failure diagnosis. A pre-maintenance data DB stores measurement data before maintenance, and a post-maintenance data DB stores measurement data after maintenance. A feature detection algorithm group DB is provided where a plurality of feature detection algorithms are stored. A first feature is detected based on the measurement data by using each of the plurality of feature detection algorithms read from the feature detection algorithm group DB. An algorithm search unit selects one of the plurality of algorithms based on the feature thus detected. A second feature is detected from the measurement data by using the feature detection algorithm, and a sign predictive of failure of diagnosis of target equipment is diagnosed using the detected second feature.

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 monitoring system that monitors a monitoring-target system is disclosed. The monitoring system includes one or more storage apparatuses that store a program, and one or more processors that operate according to the program. The one or more processors determine an estimated value of a monitoring-target response variable of the monitoring-target system on a basis of measurement data included in test data of the monitoring-target system and a causal structure model of the monitoring-target system. The one or more processors decide whether an abnormality has occurred in the monitoring-target system on a basis of a result of a comparison between a measurement value of the monitoring-target response variable included in the test data, and the estimated value.