A computer implemented method comprising receiving one or more predictive maintenance models each defining a time-based probability of failure for one or more components, receiving current performance data for the components, defining a failure function for each component from a predictive maintenance model for the component and the current performance data for the component, the failure function defining the probability of failure of the component in each of a set of time periods, defining a value loss function for each component from the failure function for the component and a time-based component cost, the value loss function defining the expected value loss due to a planned replacement of the component in a given time period before the component fails or reaches its scheduled end-of-life, receiving data defining one or more factors that have an impact on the cost of a maintenance option.

Systems and methods include receiving an indication of a selection of a first piece of equipment in an industrial monitoring system. The systems and methods also include determining a first feature of interest in a plot corresponding to a first sensor. Additionally, the systems and methods include matching the first feature of interest with corresponding second features of interest in a second plot. Furthermore, the systems and methods include overlaying the first plot with the second plot based at least in part on the first feature of interest and the corresponding second feature of interest.

A computer implemented method comprising receiving one or more predictive maintenance models each defining a time-based probability of failure for one or more components, receiving current performance data for the components, defining a failure function for each component from a predictive maintenance model for the component and the current performance data for the component, the failure function defining the probability of failure of the component in each of a set of time periods, defining a value loss function for each component from the failure function for the component and a time-based component cost, the value loss function defining the expected value loss due to a planned replacement of the component in a given time period before the component fails or reaches its scheduled end-of-life, receiving data defining one or more factors that have an impact on the cost of a maintenance option.

A data collection apparatus includes a data collection section configured to receive sequential time-series output data pieces for each of data sources, a data shaping section configured to perform data shaping processing on the sequential time-series output data pieces based on a predetermined data shaping rule set for each of the data sources such that the resulting data pieces are reduced in number or in data amount as compared with the output data pieces output from the data source, a data transmission section configured to transmit the output data pieces to the monitor control apparatus, and a data shaping rule control section configured to receive the data shaping rule set for each of the data sources from the monitor control apparatus and to set the received data shaping rule in the data shaping section.

An information processor system for monitoring a complex system and including a mechanism receiving at least one piece of event detection information associated with a detection time and a mechanism generating at least one remanent confidence level value that decreases over time starting from the detection time.

Provided is a method and system for conducting automatically a process failure mode and effect analysis, PFMEA, for a factory adapted to produce a product in a production process using a meta model, MM, stored or loaded in a data storage, wherein the stored meta model, MM, comprises abstract factory model elements modeling an abstract factory, AF, including one or more service declarations modeling abstract services across different factories, wherein each service declaration comprises failure mode declarations for different failure modes.

A computer-implemented method for detecting faults and events related to a system includes receiving sensor data from a plurality of sensors associated with the system. A hierarchical failure model of the system is constructed using (i) the sensor data, (ii) fault detector data, (iii) prior knowledge about system variables and states, and (iii) one or more statistical descriptions of the system. The failure model comprises a plurality of diagnostic variables related to the system and their relationships. Probabilistic reasoning is performed for diagnostic or prognostic purposes on the system using the failure model to derive knowledge related to potential or actual system failures.

A computer system is configured to process alarm activations received from technical systems, where an alarm activation represents a deviation of the technical status of a technical system from normal. The system includes: a data storage interface for receiving alarm activations in data storage, where the recorded alarm activations correspond to alarms; a data processor for: determining, from the recorded alarm activations, time intervals for alarm analysis; and computing similarity measures between the time intervals that depend on the occurrence of the recorded alarm activations in the time intervals, and where the contribution of an alarm activation to the similarity of two time intervals is reduced with an increasing occurrence of the alarm in the time intervals; and a user interface configured to provide pairs of time intervals to an operator of the one or more technical systems that include time intervals with similarity measures indicating similar alarm.

A system/method of diagnosing combinations of failures in a system includes receiving symptom data (116) including information relating to observed or detected symptoms in a system. The system/method generates (D4a, D4b, D5) failure data (118) including information relating to at least one most probable failures in the system based on the symptom data, and processes (D9) the failure data and the symptom data using an L-best inference (e.g. a Ranked Algorithm (RA)) technique in order to generate failure set data (120), the failure set data including information relating to at least one most probable combination of the failures that explain the symptoms.

Systems and methods are disclosed for monitoring operation of an automation system that includes a plurality of interconnected logical objects. The systems and methods may build a fuzzy cognitive map to model an interdependence of the plurality of interconnected logical objects upon one another. In some examples, the systems and methods may identify a non-ideality associated with at least one of the plurality of logical objects and determine from the fuzzy cognitive map an effect of the non-ideality on the operation of the automation system. In some examples, the systems and methods may determine from the fuzzy cognitive map a first one of the plurality of logical objects that affects a second one of the plurality of logical objects to a greater extent than do the remaining ones of the plurality of logical objects.