Abstract: The invention validates propulsion data used in engine performance diagnostics based on heuristic knowledge of the physical relationships between engine parameters. The method identifies data anomalies, reduces overall scatter, and allows for the detection of true engine fault events. The method uses persistency to aid in differentiating between anomalous and fault event data, and it replaces anomalous data with the best-known level for that parameter, thereby preserving the overall mean signal level.

Abstract: A method of performing diagnostics on a system comprises receiving a plurality of measurement parameters, each corresponding to one of a plurality of parameters at a time k, forming a deviation vector from the plurality of measurement parameters, calculating an initial deviation vector from an initial fault vector, calculating a multiple fault isolation deviation vector using the initial deviation vector and the deviation vector, determining if an event is in progress using the multiple fault isolation deviation vector, performing statistical data validity to set a present inhibit flag and a past inhibit flag, and performing module performance analysis according to the present inhibit flag and the past inhibit flag.

Abstract: A physics based neural network (PBNN) for detecting trends in a series of data inputs comprising a neural filter comprising a plurality of nodes for receiving the series of data inputs and outputting a plurality of averaged outputs, at least one standard deviation node for receiving one of the plurality of averaged outputs and the series of data inputs to produce at least one standard deviation output, wherein at least one of the average outputs is a delayed average output and at least one of the standard deviation outputs is a delayed standard deviation output, and a neural-detector comprising a plurality of neural detector nodes receiving the plurality of averaged outputs and the delayed average output and outputting a neural detector output, a neural level change node receiving the plurality of averaged outputs and outputting a neural level change estimate output, a neural confidence node receiving a counter input, the delayed standard deviation output, and the neural level change estimate output and outpu

Abstract: A PBNN for isolating faults in a plurality of components forming a physical system comprising a plurality of input nodes each input node comprising a plurality of inputs comprising a measurement of the physical system, and an input transfer function comprising a hyperplane representation of at least one fault for converting the at least one input into a first layer output, a plurality of hidden layer nodes each receiving at least one first layer output and comprising a hidden transfer function for converting the at least one of at least one first layer output into a hidden layer output comprising a root sum square of a plurality of distances of at least one of the at least one first layer outputs, and a plurality of output nodes each receiving at least one of the at least one hidden layer outputs and comprising an output transfer function for converting the at least one hidden layer outputs into an output.

Abstract: A physics based neural network (PBNN) for validating data in a physical system comprising a plurality of input nodes each receiving at least one input comprising an average measurement of a component and a standard deviation measurement of a component of the physical system and comprising a transfer function for converting the at least one input into an output, a plurality of intermediate nodes each receiving at least one output from at least one of the plurality of input nodes and comprising a transfer function embedded with knowledge of the physical system for converting the at least one output into an intermediate output, and a plurality of output nodes each receiving at least one intermediate outputs from the plurality of intermediate nodes and comprising a transfer function for outputting the average measurement of a component when the transfer function evaluates to a value greater than zero wherein the PBNN is trained with a predetermined data set.

Abstract: A physics based neural network (PBNN) comprising a plurality of nodes each node comprising structure for receiving at least one input, and a transfer function for converting the at least one input into an output forming one of the at least one inputs to another one of the plurality of nodes, at least one training node set comprising the at least one input to one of the plurality of nodes, at least one input node set comprising the at least one input to the plurality of nodes, and a training algorithm for adjusting each of the plurality of nodes, wherein at least one of the transfer functions is different from at least one other of the transfer functions and wherein at least one of the plurality of nodes is a PBNN.