An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.

The objective of the present invention is to acquire maintenance information easily when an alarm is generated. This control device for controlling an industrial machine is provided with: a monitoring unit which monitors the industrial machine to detect an abnormality in the industrial machine; an information acquiring unit which acquires alarm information relating to an alarm pertaining to the abnormality detected by the monitoring unit, and maintenance information relating to maintenance for dealing with the abnormality; and a display control unit which causes the acquired alarm information and maintenance information to be displayed on a display device.

Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises an interface component configured to display a graphical representation of a machine learning asset in an industrial automation environment, wherein the graphical representation includes a visual indicator representative of an output from the machine learning asset. The interface component is further configured to adjust the visual indicator based on the output from the machine learning asset. In addition, a process control component is configured to control an industrial process in the industrial automation environment based at least in part on the output from the machine learning asset.

A method for facilitating analysis of a fault in a building system. The method may include determining, by a processing circuit, occurrence of a fault, and capturing, by the processing circuit at a time of occurrence of the fault, a snapshot of conditions at the time by selecting a set of data points relating to the building equipment experiencing the fault and storing, by the processing circuit, event data comprising values of the set of data points at the time of occurrence of the fault. The method may also include facilitating analysis of the fault by providing, at a later time after the time of occurrence of the fault, the snapshot via a graphical user interface. The snapshot includes the event data.

The non-limiting technology described herein is a failure managing framework for complex systems that determines and restores functionality of failing systems and sub-systems using a function-based intervention approach having ontological content such as provided in a System State Graph directed graph. An integration framework allows integration of multiple intervention definition paradigms and selects the best for the current scenario; modifies procedures according to current context by encapsulating operator's tacit knowledge; provides an additional safety net during application of intervention and allows both autonomous operations and assistance to a human operator in the loop.

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.

A method of controlling and managing a process control system having a plurality of control loops includes implementing a plurality of control routines to control operation of the plurality of control loops, respectively, wherein the control routines may include at least one non-adaptive control routine. The method then collects operating condition data in connection with the operation of each control loop, and identifies a respective process model for each control loop from the respective operating condition data collected for each control loop. The identification of the respective process models may be automatic as a result of a detected process change or may be on-demand as a result of an injected parameter change. The process models are then analyzed to measure or determine the operation of the process control loops.

Provided is a cultivation assistance system including a cultivation condition acquisition unit configured to acquire a cultivation condition under which a plant is cultivated, a trouble acquisition unit configured to acquire a trouble occurrence situation in cultivation of the plant, a model generation unit configured to generate, by using the cultivation condition and the trouble occurrence situation, a model for predicting one of a cultivation condition or a trouble from the other, and an estimation unit configured to estimate, by using the model, a cultivation condition for suppressing occurrence of a trouble in cultivation of the plant. The cultivation assistance system includes a preprocessing unit to perform preprocessing on data of at least one of the cultivation condition or the trouble occurrence situation. The model generation unit is to generate, by using the preprocessed data, a model for predicting one of the cultivation condition or the trouble from the other.

A management method for managing a production line including a plurality of steps includes carrying out a first displaying process and carrying out a second displaying process, wherein, among a first indicator group (I1), an indicator corresponding to a certain step functions as a GUI element configured to change a screen to be displayed on a display from a top screen (G1) to a second screen.

In order to alleviate a user's burden of maintenance, the present invention calculates an actual lifetime of a cable, which is the intrinsic lifetime of the cable, and extends cable replacement cycles. Provided is a lifetime prediction device for a cable used in an industrial machine, the lifetime prediction device being provided with: a motion amount analysis unit that analyzes a motion amount of a motion axis of the industrial machine on the basis of a motion program for operating the industrial machine; and a lifetime calculation unit that calculates a predicted value of a lifetime of the cable by applying to the motion amount a relational expression between the motion amount and the lifetime of the cable based on the Eyring model.