Provided are an abnormality diagnosis system and an abnormality diagnosis method that can prevent wrongly diagnosing equipment as having an abnormality when the equipment actually does not have an abnormality. An abnormality diagnosis system 20 comprises a sampler 21 and a calculator 24. The calculator 24 is configured to: perform first abnormality determination of whether there is an abnormality based on a result of first principal component analysis; in the case where a result of the first abnormality determination is that there is an abnormality, and perform second abnormality determination of whether there is an abnormality based on a result of second principal component analysis; and in the case where a result of the second abnormality determination is that there is an abnormality, diagnose the equipment as having an abnormality.

A system and method for enabling access to information included in a safety requirement specification (SRS) for a process plant, the process plant controlled by a process control system including displaying, in a user interface, a cause and effect matrix (CEM) having a set of elements including a set of causes and a set of effects, wherein each of the set of causes represents a condition within the process plant and each of the set of effects represents an effect to be performed within the process plant, and wherein at least some of the set of causes and the set of effects are related as cause-effect pairs whereby the corresponding effect activates in response to an occurrence of the corresponding condition. The system and method further including receiving, via the user interface, a selection of an element of the set of elements, and, in response to receiving the selection: (i) accessing, from the SRS, a set of information associated with the element of the set of elements, and (ii) displaying the set of information in the user interface.

Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to estimate and recover normal signals for the some tags based on the signal subset U*, the first learning signal subset X*, the feature information F, the recovery information P on the plurality of recovery models, and similarity Z.

A system performs factor selection processing, which includes performing a factor selection operation for selecting one or two or more factors from one or a plurality of factors; factor filtering processing which includes: determining, about each of the one or the plurality of factors before the factor selection processing is performed, whether a factor value for prediction input to a prediction model is within a range of a plurality of factor values for specimen used to identify the prediction model; and excluding a factor, a result of the determination of which is false, and outputting a factor not excluded; and prediction processing which includes calculating the prediction value of the prediction target by inputting, to the prediction model, a factor value for prediction about each of one or more factors including the factor selected in the factor selection processing and not including the factor excluded in the factor filtering processing.

Disclosed is an apparatus for estimating disturbance flowing into a control system on the basis of input/output data. The apparatus includes an input unit, and an estimator configured to, when input data (u) is provided to the control system through the input unit and thus output data is acquired, estimate the input data (u) from the acquired output data, based on a system model matrix corresponding to an input/output relation model of the control system. Accordingly, efficiency of the apparatus may be improved.

This verification processing device is provided with: an inspection unit that performs model inspection on an inspection target model including a plurality of elements; a selection unit that selects at least one of the plurality of elements included in a counterexample outputted as a result of the model inspection; and an exclusion history generation unit that generates exclusion history information indicating an exclusion frequency for each of the plurality of elements. The inspection unit further performs another model inspection on the inspection target model obtained by excluding the selected element. When another counterexample has been outputted as a result of another model inspection, the exclusion history generation unit increases the exclusion frequency of the selected element and updates the exclusion history information. The selection unit selects an element that is high in the exclusion frequency, on the basis of the exclusion history information.

A control apparatus is communicably connected to a first apparatus to perform a control on the first apparatus. The control apparatus includes a data collecting unit, a collection interval determining unit, and a storing unit. The data collecting unit collects, by polling and from the first apparatus, data in order to perform the control. The collection interval determining unit determines, based on a setting content of the control, a collection interval of the data collected by the data collecting unit. The control apparatus performs a plurality of the controls on one first apparatus. The storing unit stores, for each of the controls, one data item or a plurality of data items constituting the data. The storing unit further stores, for each of the data items, the collection interval.

The invention relates to the field of operating, monitoring and controlling mills of the mining industry. It specifically relates to a system and a method for in-line determination of the characteristics of worn balls and pieces thereof, which have been ejected from a semi-autogenous mineral grinding (SAG) mill to the external classifiers.

A building management system is provided. The building management system includes one or more variable air volume units and a controller. The controller includes a processor and a memory. The memory stores processor executable instruction. The processor executable instruction when executed by the processor cause the building management system to perform functional tests on the one or more variable air volume units of the building management system to generate building data; analyze the building data to produce health information per functional test; and visualize a condition of the building system based on the health information.

An inspection system and method of operation may include receiving sensor data for a component of a powered system, and determining an expected failure time of the component at which the component is expected to fail based at least in part on the sensor data. The expected failure time occurring on a timeline. A window start time may be determined on the timeline that is after a current time but is prior to the expected failure time. The window start time and the expected failure time may define a window time range that extends between the window start time and the expected failure time on the timeline. One or both of a repair action or a maintenance action of the powered system may be scheduled at a scheduled time occurring during the window time range and at a time prior to the expected failure time on the timeline.