Abstract: To perform a prognostic health analysis for an asset (11-13), a stochastic simulation is performed to obtain a prognosis for the evolution of the asset health state. The prognosis is updated based on sensor measurements using a particle filter.

Abstract: To perform a prognostic health analysis for an asset, a plurality of independent stochastic simulations are performed using transition probabilities of a discrete Markov Chain model. A prognostic asset health state evolution is computed over a time horizon from the plurality of independent stochastic simulations. An output is generated based on the computed prognostic asset health state evolution.

Abstract: To perform a prognostic health analysis for an asset, a plurality of independent stochastic simulations are performed using transition probabilities of a discrete Markov Chain model. A prognostic asset health state evolution is computed over a time horizon from the plurality of independent stochastic simulations. An output is generated based on the computed prognostic asset health state evolution.

Abstract: To perform a prognostic health analysis for an asset (11-13), a stochastic simulation is performed. Transition probabilities for transitions between states of a discrete state model (41-44) used in the stochastic simulation are updated as information on asset degradation becomes available.

Abstract: To perform a prognostic health analysis for an asset (11-13), a stochastic simulation is performed to obtain a prognosis for the evolution of the asset health state. The prognosis is updated based on sensor measurements using a particle filter.

Abstract: To perform a prognostic health analysis for an asset, a plurality of independent stochastic simulations are performed using transition probabilities of a discrete Markov Chain model. A prognostic asset health state evolution is computed over a time horizon from the plurality of independent stochastic simulations. An output is generated based on the computed prognostic asset health state evolution.

Abstract: Method for configuring an apparatus or method for prediction of a malfunction of a unit, includes providing components of the unit; providing single malfunctions for each component; providing probabilities for the single malfunctions of each component; providing a parameter of each component for each single malfunction of this component; dividing each parameter into discrete value states; providing values of the parameters; determining a transition matrix for each parameter on the basis of the measurements of this parameter, wherein the transition matrix includes for the plurality of discrete value states of each parameter the probabilities to switch from one of the discrete value states to another of the discrete value states within a certain time period; determining the probabilities of the discrete value states on the basis of the measurements of the parameters; and providing the conditional probabilities of the discrete value states given the corresponding single malfunction.

Abstract: Predicting a malfunction of a component of a unit includes providing a transition matrix of a parameter of the component, wherein the transition matrix includes for a number of discrete value states of the parameter probabilities to switch from one discrete value state to another within a certain time period; providing the conditional probability distribution for the malfunction given the discrete value states; providing a current discrete value state of the parameter; determining a conditional probability distribution of the discrete value states given the current discrete value state for a future point in time based on the current discrete value state and on the transition matrix by use of a Markov chain; and determining a probability for the malfunction for the future point in time based on the conditional probability distribution of the discrete value states for the future point in time and the conditional probability distribution for the malfunction.

Abstract: Predicting a malfunction of a component of a unit includes providing a transition matrix of a parameter of the component, wherein the transition matrix includes for a number of discrete value states of the parameter probabilities to switch from one discrete value state to another within a certain time period; providing the conditional probability distribution for the malfunction given the discrete value states; providing a current discrete value state of the parameter; determining a conditional probability distribution of the discrete value states given the current discrete value state for a future point in time based on the current discrete value state and on the transition matrix by use of a Markov chain; and determining a probability for the malfunction for the future point in time based on the conditional probability distribution of the discrete value states for the future point in time and the conditional probability distribution for the malfunction.

Abstract: Method for configuring an apparatus or method for prediction of a malfunction of a unit, includes providing components of the unit; providing single malfunctions for each component; providing probabilities for the single malfunctions of each component; providing a parameter of each component for each single malfunction of this component; dividing each parameter into discrete value states; providing values of the parameters; determining a transition matrix for each parameter on the basis of the measurements of this parameter, wherein the transition matrix includes for the plurality of discrete value states of each parameter the probabilities to switch from one of the discrete value states to another of the discrete value states within a certain time period; determining the probabilities of the discrete value states on the basis of the measurements of the parameters; and providing the conditional probabilities of the discrete value states given the corresponding single malfunction.