Methods, computer program products, and systems are presented. The methods include, for instance: obtaining an input including an image of a plant, identifying a species of the plant by use of visual recognition of the image. The image is analyzed for symptoms and candidate diagnoses are selected from a diagnostic repository according to the symptoms. With respective candidate diagnoses, a confidence score and a treatment regimen are associated. According to a treatment mode, the candidate diagnoses and treatment regimen are delivered.

An industrial sensor selection system guides the process of selecting a suitable industrial sensor for use in an industrial sensing application based on information provided by the user about the application. The system can allow the user to initially specify a target product or object that is to be detected or measured by the sensing application. Based on the product selection, the sensor selection system allows the user to select from among a set of sensor use cases commonly applied to the selected product. The selection system may also prompt the user to provide additional contextual information about the selected use case. Based on the user's selection of a target product, use case, and any applicable contextual information, the sensor selection system identifies one or more suitable industrial sensors registered within a sensor profile library suitable for use within the user's sensing application.

A state display device 100 includes a display device 30 for displaying a temporal change of a first parameter representing an operation state of a thermal power generation facility 10, and a correlation between the first parameter and a second parameter representing an operation state of the thermal power generation facility 10, an input device 70 for respectively selecting and inputting a first period t1 and a second period t2 different from the first period t2 in the temporal change of the first parameter, and a processing device 50. The processing device 50 is configured to display the temporal change of the first parameter on the display device 30, and to display, on the display device 30, the correlation between the first parameter and the second parameter in each of the first period t1 and the second period t2 input via the input device 70.

The present disclosure includes a method for monitoring an automation system having a multiplicity of field devices arranged in a spatially distributed fashion and configured to acquire or set a primary process variable that is dependent on the process. The field devices are also configured to each acquire at least one secondary environmental variable and have at least one superordinate process control unit for controlling the process. The method includes transmitting the acquired environmental variables to a superordinate database and analysis platform and monitoring the automation system using the database and analysis platform. To monitor the automation system, the database and analysis platform compares the acquired transmitted secondary environmental variables with comparative environmental variables which represent a usual state of the automation system, in order to detect an unusual state in the automation system.

A method includes obtaining data associated with operation of an aircraft and determining a first operational phase of the aircraft based on the data. The method includes identifying a candidate operational phase transition from the first operational phase to a candidate operational phase based on a first portion of the data satisfying a first condition associated with the candidate operational phase, the first portion of the data corresponding to a first time. The method includes evaluating a second portion of the data based on a second condition associated with the candidate operational phase, the second portion of the data corresponding to a second time that is subsequent to the first time. The method further includes, based on the second condition being satisfied, generating an operational phase transition indication that indicates an occurrence of an operational phase transition to the candidate operational phase at the first time.

An apparatus including a sensing device configured to be coupled to an electrical outlet is provided. The sensing device can include a data acquisition receiver configured to receive electrical noise via the electrical outlet when the sensing device is coupled to the electrical outlet. The electrical outlet can be electrically coupled to an electrical power infrastructure. One or more electrical devices can be coupled to the electrical power infrastructure and can generate at least a portion of the electrical noise on the electrical power infrastructure. The data acquisition receiver can be configured to convert the electrical noise into one or more first data signals. The apparatus also can include a processing module configured to run on a processor of a computational unit. The sensing device can be in communication with the computational unit. The processing module can be further configured to identify each of two or more operating states of each of the one or more electrical devices at least in part using the one or more first data signals. The two or more operating states of each electrical device of the one or more electrical devices can be each different user-driven operating states of the electrical device when the electrical device is in an on-power state. Other embodiments are provided.

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.

Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.

A device includes a processor configured to obtain flight data of one or more aircraft of an aircraft type. The processor is configured to identify a first portion of the flight data as first phase flight data associated with a first phase of one or more flights of the one or more aircraft, and to apply a first aircraft performance model to the first phase flight data to determine first parameter values. The processor is configured to identify a second portion of the flight data as second phase flight data associated with a second phase of the one or more flights, and to apply the first aircraft performance model to the second phase flight data to determine second parameter values. The processor is configured to generate, based on the first parameter values and the second parameter values, a second aircraft performance model of a particular aircraft of the aircraft type.

The present invention relates to a method for analyzing energy used for producing a unit of mass or volume of compressed gas (Specific Energy Consumption) in relation to a common output flow in a compressor system, said method comprising the following steps: for time interval, T.sub.ref, collecting reference measured data points of common output flow F.sub.ref and energy (or power) consumption E.sub.ref (or P.sub.ref) in the compressor system; calculating energy (or power) use as a function of the common output flow E.sub.ref (F) (or <P.sub.ref>.sub.t(F)) from the measured data points and calculating volume output as a function of the common output flow V.sub.ref(F): calculating average energy consumed for producing a unit of mass or volume of compressed gas as a function of the common output flow <SEC.sub.ref>.sub.t(F) from equation E.sub.ref(F)/V.sub.ref(F) (Or <P.sub.ref>.sub.t)/P.sub.ref); for time interval, T.sub.sav, collecting measured data points of common output flow F.sub.sav and energy (or power) consumption E.sub.sav (P.sub.sav) in the compressor system; calculating energy consumed for producing a unit of mass or volume of compressed gas as a function of the common output flow <SEC.sub.sav>.sub.t(F) from equation E.sub.sav(F)/V.sub.sav (F) (or <P.sub.sav>.sub.t(F)/F sav) or SEC.sub.sav(t,F) from P.sub.sav/F.sub.sav; calculating the difference between <SEC.sub.ref>.sub.t(F) and <SEC.sub.sav>.sub.t(F) or SEC.sub.sav(t,F) over a range of common output flow F in the compressor system.