A fault reporting device 8 for reporting a fault to a facility management arrangement 9, having a location module, wherein the location module is formed to determine a location position X(t) of the fault reporting device 8, having a position determining module, wherein the position determining module is formed to identify a fault position in a surrounding area of the location position X(t), having a transmission module for wirelessly transmitting a fault report, wherein the fault report comprises the fault position and fault information 12.

Fault tree analysis is performed using a combination of digital computer systems and quantum processing devices. For example, quantum annealers may be configured to analyze a fault tree for minimal cut sets. The quantum annealer may be particular good at identifying smaller minimal cut sets. Digital computer systems may be used to identify the remaining minimal cut sets. If the quantum annealer identifies one of the minimal cut sets of smallest size (i.e., lowest cardinality), this can be used as a constraint for the digital computer system, thus speeding up its search for other minimal cut sets.

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 method for monitoring a conveying system with a monitoring device includes recording a signal pattern progression in relation to a functional unit of the conveying system. A reference signal progression is stored in a database as is a correlation data set for the reference signal progression, the correlation data set including instants or time segments in the reference signal progression and reference to the functional unit. The reference is linked to each instant or time segment, the recorded signal pattern progression is compared with the reference signal progression by a processing unit, and in the event of deviation between the signal progressions an entry in the correlation data set belonging to the reference signal progression is determined by the processing unit on the basis of an instant or time segment of such a deviation, and the functional unit referenced there is output as a cause.

A computer-implemented method, computer program product and computing system for recording video information on the computing device during a monitored event. Execution information is recorded on the computing device during the monitored event. The video information and the execution information are temporally synchronized to form temporally-synchronized diagnostic content.

The present invention provides a method, a program, and a device that can accurately learn a causal relationship in a deterministic system and a system that performs anomaly analysis by using the causal relationship. A causal relationship learning device according to one example embodiment of the present invention includes: a correlation determination unit that determines a correlation between measurement values measured by two sensors; and a low correlation causal relationship estimation unit that, when the correlation is lower than a predetermined reference, determines a causal relationship between the two sensors by estimating one of the measurement values which is a cause from the other of the measurement values which is a result.

A method is provided for determining at least one faulty piece of equipment from amongst a plurality of pieces of equipment of an aircraft. A system is also provided that implements the method. The method comprises the steps of reading a plurality of predetermined parameters for monitoring said equipment; defining a list of symptoms associated with said parameters read, using a predetermined correspondence table; attributing a value associated with each symptom according to the parameters read, which value is selected from amongst a predetermined list of values; evaluating for each piece of equipment an occurrence of one or more fault modes for said piece of equipment on the basis of at least one predetermined truth table which associates a fault mode with each combination of symptom values; and determining at least one faulty piece of equipment from amongst one or more pieces of equipment, of which at least one fault mode is currently occurring, referred to as potentially faulty equipment.

Methods and apparatus are provided for an integrated vehicle health management system that uses a model based reasoner to develop real-time diagnostic and maintenance information about a vehicle. A model having nodes is used to represent sensors, components, functions and other elements of the system. Parameterized input sensors provide flexible definitions of each node. Once developed, the model is loaded into an operational flight program to provide real-time failure analysis of the vehicle.

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.

Methods and apparatus are provided for an integrated vehicle health management system that uses a model based reasoner to develop real-time diagnostic and maintenance information about a vehicle. A model having nodes is used to represent sensors, components, functions and other elements of the system. Parameterized input sensors provide flexible definitions of each node. Once developed, the model is loaded into an operational flight program to provide real-time failure analysis of the vehicle.