According to one aspect of a technique the present disclosure, there is provided a processing apparatus including: an apparatus controller including a memory storing apparatus data containing monitor data containing sensor information and an alarm indicating a failure detected based on the sensor information and an alarm analysis table containing analysis items. The apparatus controller is capable of outputting the alarm containing an alarm ID for specifying a type and an occurrence time of the alarm; specifying candidates of the analysis items from the alarm ID; acquiring monitor data corresponding to the candidates; determining a rank of the cause of the alarm according to a difference between monitor data at the occurrence time and a predetermined threshold value; and displaying a relationship between the monitor data and the threshold value. The display screen displays an occurrence history of the alarm and history of acquiring the monitor data.

Systems and methods of preventing an event occurrence or mitigating effects of an event occurrence in an industrial facility are disclosed herein. In some embodiments, a first input is received from a first sensor and, based at least in part on the first input, an initial action is automatically generated. In response to the initial action, a second input is received from a second sensor and, based at least in part of the received first and second inputs, a likelihood of an event occurrence is determined. Based at least in part of the determined likelihood, a remedial action configured to prevent the occurrence of the event occurrence is automatically generated. In some embodiments, the remedial action is generated in real-time and can be directed to a process condition, environmental condition, or secondary source.

Disclosed is a method of predictively maintaining equipment by means of a distribution map. The method can: extract a peak value based on a change in the amount of energy required for the equipment to perform a working process in a normal state; generate the distribution map based on the extracted peak value; and predictively detect, in advance, abnormalities of the equipment on the basis of a change in a distribution probability of a detection section having a low distribution probability and a somewhat high risk in the generated distribution map, so as to induce maintenance and replacement of the equipment to be carried out in a timely manner. Thus, enormous financial losses due to equipment failure may be prevented.

A method for predictive maintenance of equipment via a distribution chart is disclosed. Peak values are extracted based on a change in an amount of energy required for performing a work process by the equipment in a normal state, a distribution chart of the extracted peak values is constructed, and an abnormal symptom of the equipment is predictively detected in advance based on a change in distribution probability of a detection section having a low distribution probability and somewhat high risk in the constructed distribution chart thereof such that maintenance and replacement of the equipment are induced to be carried out at an appropriate time. Thus, an enormous monetary loss caused by a failure in the equipment may be prevented in advance.

A method for correlating data from sensors includes receiving sensor information from a plurality of sensors of an industrial operation. Sensor information from component sensors is used for functionality of a component of the industrial operation and sensor information from additional sensors monitor conditions of a portion of the industrial operation different from the component. The method includes deriving, using the sensor information, correlations between component sensors and additional sensors and deriving a baseline signature from the sensor information and the correlations. The baseline signature encompasses a range of normal operating conditions. The method includes identifying an abnormal operating condition based on a comparison between additional sensor information and the baseline signature. The sensor information is used differently for functionality of the component than for deriving the correlations and baseline signature and identifying the abnormal operating condition. The method includes sending an alert with the abnormal operating condition.

A plant monitoring device (20) is provided with: a detection value acquisition unit (211) that acquires a bundle of detection values; a first Mahalanobis distance calculation unit (212) that calculates a first Mahalanobis distance; a plant state determination unit (213) that determines whether the operation state of a plant is normal or abnormal; a cause detection value estimation unit (214) that estimates a cause detection value which represents a cause of the abnormality of the plant; a second Mahalanobis distance calculation unit (215) that calculates a second Mahalanobis distance by increasing or decreasing the detection value estimated as the cause detection value; and an identification unit (216) that identifies whether the abnormality can be relieved by increasing or decreasing the detection value estimated as the cause detection value.

Systems, program products, and methods for detecting thermal stability within gas turbine systems are disclosed. The systems may include a computing device(s) in communication with a gas turbine system, and a plurality of sensors positioned within or adjacent the gas turbine system. The sensor(s) may measure operational characteristics of the gas turbine system. The computing device(s) may be configured to detect thermal stability within the gas turbine system by performing processes including calculating a lag output for each of the plurality of measured operational characteristics. The calculated lag output may be based on a difference between a calculated lag for the measured operational characteristics and the measured operational characteristic itself. The calculated lag output may be also be based on a time constant for the measured operational characteristics. The computing device(s) may also determine when each of the calculated lag outputs are below a predetermined threshold.

Currently solutions for early detection of failures in manufacturing utilize predefined threshold levels of the process variables associated with equipment in manufacturing unit/industry plants. The pre-defined threshold and levels thereof are compared with the real values obtained from the manufacturing unit to check behavior of process variables (also referred as ‘process parameters’) and thus are prone to error. The present disclosure provides systems and method for predicting early warning of operating mode of equipment operating in industry plants which is based on transforming conditions on process parameters into conditions on corresponding fuzzy indices based on their thresholds. The fuzzy indices (concordance index, discordance index) of individual conditions are combined into a composite fuzzy index (composite index or degree of credibility) that describes the failure scenario in the process parameter space. A fuzzy logic-based detection is useful for detecting a failure mode early and providing alerts to operators for necessary action.

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.

Parameter settings and operational data are received from machines for a current predefined time interval. For each machine, a corresponding health metric value is calculated based on the received operational data and machine health data, and stored in association with the received corresponding parameter settings. Associated unknown health metric values are estimated for machines associated with combinations of parameter settings different from the received parameter settings having at least one of the combinations of parameter settings with an associated previously determined health metric value, and at least one other of the combinations of parameter settings with the associated unknown health metric value, based on the corresponding calculated health metric value and the corresponding previously determined health metric value. Associated parameter settings for at least one healthiest machine and at least one least healthy machine are determined based on the stored health metric values and are automatically adjusted.