A method for identifying imbalances in facility operations. The method includes determining an existence of an imbalance between a first volume of a component at a first point in the facility and a second volume of the component at a second point in the facility, calculating a probability likelihood of the imbalance being caused by one or more likely errors of a set of known likely errors, determining an impact magnitude on the imbalance of each of the one or more likely errors, and assigning an importance ranking to each of the one or more likely errors based on the probability likelihood and the impact magnitude.

A method of commissioning or maintenance an alarm system is provided. The method including: receiving a fault report including one or more faults from an alarm system; categorizing the one or more faults; prioritizing the one or more faults into a list of highest priority; determining a first root cause of a first fault of the one or more faults in response to at least one of an alarm system configuration of the alarm system, a device mapping of the alarm system, and an event history of the alarm system; providing instructions, via a user interface, regarding how to resolve the first fault in response to the first root cause; and detecting whether the first fault is resolved.

A system includes a computer processor located within a cloud system. The system receives data from a sensor associated with an industrial or home automation control application. The sensor is configured to monitor a first condition of a device. The computer processor analyzes the data, and transmits a software update or software download from the cloud system to the industrial or home automation control application based on the data analysis.

A type identification unit identifies a request type included in input information input from the outside of a control apparatus; and a state storage unit stores state information when the request type identified by the type identification unit is a preset request, the state information including device information and the input information associated with each other, the device information including device data stored in a storage area specified in specification information, the input information being input to the control apparatus.

Provided are a diagnosing device, a diagnosing method, and a program with which it is possible to detect abnormalities accurately even if there is a small amount of data or the number of data points varies. This diagnosing device is provided with a Mahalanobis distance calculating unit which calculates the Mahalanobis distance (referred to as ‘MD value’ hereinbelow) of a detected value, and an abnormality determining unit which determines the presence or absence of an abnormality on the basis of the MD value, wherein the abnormality determining unit determines the presence or absence of an abnormality by arranging that a determination that there is no abnormality is more likely to occur if the number of samples per unit space is small than if the number of samples per unit space is large.

The present disclosure relates to diagnosing a fault in an electric drive of a process plant. The fault diagnosis method includes receiving fault data from an electric drive upon occurrence of the fault. The method further includes obtaining a fault code and a drive type associated with the electric drive from the fault data. In addition, the method comprises determining one or more drive parts to replace by comparing the fault code and the drive type with a mapped data for a plurality of drive types. The mapped data for each drive type includes a relation between a plurality of fault codes and a plurality of drive parts. The method further includes initiating a maintenance operation involving replacement of the one or more drive parts to address the fault.

An abnormal irregularity cause identifying device includes a process data acquisition unit that reads, from a storage device storing process data, the pieces of process data, an abnormality determination unit that calculates an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit, and a cause diagnosis unit that obtains accuracy of a cause of the irregularity based on a proportion of process data having the abnormality degree calculated by the abnormality determination unit satisfying a predetermined criterion to the plurality of types of pieces of process data, by using causal relation information defining a combination between the cause and the irregularity, which appears as an influence resulting from the cause, of each of the plurality of types of pieces of process data.

A method is described herein, comprising registering an event at a first processing unit of a processing facility comprising a plurality of processing units, using a coincidence probability array and an event probability to identify a second processing unit of the plurality of processing units based on the event, determining whether the second processing unit experienced a coincident event, if the second processing unit experienced a coincident event, remediating a condition of the second processing unit that caused the coincident event, and updating the coincidence probability array based on the event.

An abnormal irregularity cause identifying device includes a process data acquisition unit that reads, from a storage device storing process data, the pieces of process data, an abnormality determination unit that calculates an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit, and a cause diagnosis unit that determines, for each of the pieces of process data output by the corresponding one of the plurality of sensors, whether the abnormality degree calculated by the abnormality determination unit satisfies a predetermined criterion by using causal relation information defining a combination of a causal relation between a cause and the irregularity, which appears as an influence resulting from the cause, of the process data output by each of the plurality of sensors in a hierarchical manner along time series.

An abnormal irregularity cause identifying device includes a process data acquisition unit that reads process data output by sensors included in a production facility performing a batch stage and a continuous stage, a preprocessing unit that associates a range of a complete timing of the batch stage with an output timing of process data of the process data in the continuous stage based on a residence time of the processing target in the production facility, an abnormality determination unit that calculates an abnormality degree by using process data in the batch stage and process data in the continuous stage associated with each other by the preprocessing unit, and a cause diagnosis unit that determines, for each of the process data output by the corresponding one of the plurality of sensors, whether the abnormality degree calculated by the abnormality determination unit satisfies a predetermined criterion.