Method for determining a reliability state of an electrical network, the electrical network comprising a plurality of interconnected electrical devices, the method including the following steps: a) identifying an undesired event at a given location in the electrical network; b) traversing at least one subset starting from the given location; c) identifying an electrical device of the electrical network; d) determining a list of events of concern that are associated with the identified electrical device and could result in the undesired event; e) determining a total unavailability value associated with the identified electrical device; f) repeating steps b) to e); and g) calculating a reliability state of the electrical network on the basis of the total unavailability values respectively associated with the traversed electrical devices.

A method of diagnosing a root cause for an exhibited vehicle failure, comprises initiating a vehicle health management (VHM) algorithm, repeatedly monitoring, at a specified time interval, the state of health (SOH) for at least one vehicle component, wherein the SOH for the at least one vehicle component is one of Green (normal operation), Yellow, and Red, calculating a number of consecutive Green SOH results for the at least one vehicle component, and providing an indication of likelihood that the at least one vehicle component is not a root cause of the exhibited vehicle failure based on the number of consecutive Green SOH results for the at least one vehicle component.

Various embodiments herein relate to systems and methods for predictive maintenance for semiconductor manufacturing equipment. In some embodiments, a predictive maintenance system includes a processor that is configured to: receive offline data that indicates historical operating conditions and historical manufacturing information corresponding to manufacturing equipment that conducts a manufacturing process; calculate predicted equipment health status information by using a trained model that takes the offline data as an input; receive real-time data that indicates current operating conditions of the manufacturing equipment; calculate estimated equipment health status information by using the trained model that takes the real-time data as an input; calculate adjusted equipment health status information by combining the predicted equipment health status information and the estimated equipment health status information; and present the adjusted equipment health status information that includes an expected remaining useful life (RUL) of at least one component of the manufacturing equipment.

A display system includes a display device in production equipment. The display device includes a control unit, a display unit, a storage unit, and an input unit. The storage unit stores: schematic illustration data that represent a schematic illustration of the production equipment; and causal relationship model data in which one or more cause elements of one or more abnormalities that can occur in the production equipment are selected from driving means for driving the production equipment and monitoring means for monitoring the production, and the cause elements and the relationships between the cause elements are represented as a causal relationship model. The control unit displays the schematic illustration and the causal relationship model on the display unit such that the causal relationship model is superimposed on the schematic illustration so as to correspond to the schematic illustration.

An approach for diagnosing degradations in performance and malfunctions in sensor networks is disclosed. This approach is based on so-called “fault signatures”. Such fault signatures are generated for known fault conditions through a statistical analysis process that results in each known fault having a unique fault signature. Such unique fault signatures can then point to the root cause of a problem.

There is described a zone controller and method for identifying a root cause failure at a zone. The zone controller determines whether a temperature measurement deviates from a temperature setpoint of the temperature sensor, and generates a first repair code, a second repair code, and/or a third repair code. The first repair code replaces a temperature sensor in response to detecting that a reading of the temperature sensor has failed. The second repair code releases an operator override on the reading of the temperature sensor in response to detecting that the reading of the temperature sensor has been overridden. The third repair code releases an operator override on a setpoint of the temperature sensor in response to detecting that the setpoint of the temperature sensor is outside the predetermined setpoint range. One or more of these repair codes are provided to a remote device.

A method and apparatus for analyzing an investigated complex system the complex system including a plurality of system components, the method includes the steps of providing a base virtual object oriented data model including abstract components corresponding to system components of the investigated complex system, wherein each abstract component of the base virtual object oriented data model includes parameters and attributes of the respective system component of the investigated complex system; mapping sensor tags of sensors deployed in the investigated complex system (2) and/or event names of events received from the investigated complex system to the parameters of the abstract components of the provided base virtual object oriented data model to generate a dedicated data model for the system type of the investigated complex system; and performing a failure mode and/or a root-cause analysis of the investigated complex system on the basis of the dedicated data model.

Apparatus and methods for alert management in instrumentation are disclosed. An example method includes generating a set of alerts within a process control instrument, processing the set of alerts to compare the set of alerts to known combinations of alerts, determining if one of the known combinations of alerts matches the set of alerts based on the comparison of the set of alerts to the known combinations of alerts, and identifying a recommended action instruction based on the determination.

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 diagnosis unit (1) for detection, analysis, and data management of sensor data detected on an actuator (20), has at least one sensor (14, 15), a data processing unit (10), a data manager (11), and an interface unit (12). The at least one sensor (14, 15), the interface unit (12), and the data manager (11) are respectively, connected to the data processing unit (10), enabling data exchange. The diagnosis unit (1) is built into a module with the actuator (20).