A field device (10) includes a detector (11) that detects a physical quantity and outputs a detection signal, a controller (121) that calculates, based on the detection signal, data for a measured trend indicating a temporal change in a parameter used for predictive diagnosis of the field device (10), and a memory (122) that stores the data. The controller (121) stores the data in the memory (122) at time intervals from the start time of measurement in the field device (10) to the current time and executes a compression process on the data stored in the memory (122) upon the amount of the data stored in the memory (122) reaching the upper limit of the storage capacity of the memory (122).

An apparatus is provided comprising an acquisition unit configured to acquire a plurality of types of measurement data indicating a state of a target, a supplying unit configured to supply, in response to the plurality of types of measurement data being input, measurement data acquired by the acquisition unit to a model that outputs a state indication value indicating a quality of a state of the target, a setting unit configured to set, as data to be displayed, at least one piece of measurement data, among the plurality of types of measurement data, having a larger influence on the state indication value than a reference, and a display control unit configured to display the state indication value output from the model along with a measurement value of the data to be displayed.

Embodiments herein describe coupling traditional fan and shaper control along with aggregated knowledge of the temperature history of a hardware device to optimally manage the temperature of the hardware device to preserve its expected life while also providing the lower power, best performing solution possible. In one embodiment, a cooling application manages the expected life by trading off performance and power versus temperature to achieve a desired (or accepted) lifetime. In one embodiment, the cooling application calculates a historical temperature value for the hardware device which is then used to determine the expected life of the hardware device.

A method for detecting and diagnosing sensor faults in an autonomous system that includes sensors and hardware components, according to which sensors are related to hardware components and correlations between data readings are recognized online and correlation between sensors is determined. Predefined suspicious patterns are identified by online and continuously tracking the data readings from each sensor and detecting correlation breaks over time. The readings from sensors that match at least one of the patterns are marked as uncertain. For each online reading of the sensors, whenever sensors that used to be correlated show a different behavior, reporting that the reading indicates a fault. Upon identifying fault detection, diagnosing which of the internal components or sensors caused the fault, based on a function that returns the state of the sensor which is associated with the fault detection.

There is provided a technique that includes: a main controller configured to execute a process recipe including a plurality of steps to perform a predetermined process on a substrate so as to acquire device data when executing the process recipe; and a storage part configured to store the acquired device data, wherein the main controller is configured to: acquire the device data in a predetermined specific step among the steps constituting the process recipe; calculate a value of the acquired device data in the specific step; compare the calculated value with a value of the device data in the specific step calculated at a time of previous execution of the process recipe; and generate an alarm when the calculated value shows a predefined tendency.

A system for threat detection in an industrial production environment comprises a programmable logic controller. This programmable logic controller includes a process image updated according to a scan cycle with process data associated with one or more field devices, a program configured to operate the one or more field devices using the process data, and one or more function blocks. These function blocks are configured to monitor the process data in the process image during each scan cycle, and identify a possible cyberattack based on the process data.

Techniques are provided for classifying runs of a recipe within a manufacturing environment. Embodiments monitor a plurality of runs of a recipe to collect runtime data from a plurality of sensors within a manufacturing environment. Qualitative data describing each semiconductor devices produced by the plurality of runs is determined. Embodiments characterize each run into a respective group, based on an analysis of the qualitative data, and generate a data model based on the collected runtime data. A multivariate analysis of additional runtime data collected during at least one subsequent run of the recipe is performed to classify the at least one subsequent run into a first group. Upon classifying the at least one subsequent run, embodiments output for display an interface depicting a ranking sensor types based on the additional runtime data and the description of relative importance of each sensor type for the first group within the data model.

The invention provides methods, systems and computer program products for monitoring field device states within a process control system, and for detecting and responding to state changes associated with one or more field devices. Implementation of the invention involves, for each field device within a selected sub-set of the plurality of field devices, (i) retrieving a reference set or template set of field device parameter state data associated with the field device (ii) retrieving a set of current state data associated with each field device within the selected sub-set of the plurality of field devices, (iii) comparing the two sets of parameter state data for each field device within the selected sub-set of the plurality of field devices and (iv) generating and/or displaying a report representing detected deviations between the two sets of parameter state data for each field device within the selected sub-set of the plurality of field devices.

Systems, methods, and other embodiments associated with providing long-term predictions for specific maintenance are described. In general, the system or method will: Retrieve a current amount of usage for a component part of a device, a failure probability curve for the component part, and a planned maintenance schedule for the device. Update the failure probability curve based on trends of data obtained from sensors associated with the component part, and causal factors associated with the device. Determine that the likelihood of failure for the component part after a first upcoming planned maintenance and before a second upcoming planned maintenance exceeds a threshold. Generate a work order for specific maintenance on the component part to be performed during the first upcoming planned maintenance. Transmit the work order to cause resources to be timely obtained and labor allocated to perform the specific maintenance during the first upcoming planned maintenance.

A method of remotely configuring one or more building system components at a building site uses a cloud-based server remote from the building site. The cloud-based server receives information from an intelligent gateway at the building site that identifies each of one or more building system components at the building site. The cloud-based server is used to create a site configuration that is based at least in part on the identified information for each of the one or more building system components. The site configuration is then downloaded from the cloud-based server to the intelligent gateway such that the intelligent gateway is able to pass configuration information to one or more local controllers that control operation of the one or more building system components.