Max-margin temporal transduction for automatic prognostics, diagnosis and change point detection

A non-transitory computer readable medium containing

• computer-readable instructions stored therein for causing a control processor to perform a method of detecting status changes, and a corresponding point-in-time, in one or more monitored entities, the method comprising:
• receiving, via one or more sensors, one or more signals being representative of an operational state of the one or more monitored entities;
• transferring, via the one or more sensors, one or more elements of time-series data derived from the one or more signals, to the processor being communicatively coupled to the one or more sensors, the time-series data comprising both labeled and unlabeled data;
• wherein the labeled data includes:
• initial measurements from all of the monitored entities and final measurements from monitored entities with known unhealthy states;
• creating a predictive model from the time-series data in a datastore memory;
• applying a transduction classifier to the predictive model;
• the transduction classifier detecting a change from healthy to unhealthy in the time-series data, and the corresponding point-in-time when the change occurred;
• classifying, by the transduction classifier, the unlabeled data, generating classified data to (i) classify a plurality of final states of monitored entities with unknown final diagnoses to identify those with disorders, and (ii) classify intermediate observations to identify the corresponding point-in-time when the change occurred;
• providing an identification of the change in the time-series data and the corresponding point-in-time;
• and
• training the predictive model for optimized performance based on the unlabeled data, the labeled data, and the classified data.

A method of detecting status changes, and a corresponding point-in-time, in one or more monitored entities, the method comprising:

• receiving, via one or more sensors, one or more signals being representative of an operational state of the one or more monitored entities;
• transferring, via the one or more sensors, one or more elements of time-series data derived from the one or more signals, to a processor being communicatively coupled to the one or more sensors the time-series data comprising both labeled and unlabeled data;
• wherein the labeled data includes:
• initial measurements from all of the monitored entities and final measurements from monitored entities with known unhealthy states;
• creating a predictive model from the time-series data in a datastore memory;
• applying a transduction classifier to the predictive model;
• the transduction classifier detecting a change from healthy to unhealthy in the time-series data, and the corresponding point-in-time when the change occurred;
• classifying, by the transduction classifier, the unlabeled data, generating classified data to (i) classify a plurality of final states of monitored entities with unknown final diagnoses to identify those with disorders, and (ii) classify intermediate observations to identify the corresponding point-in-time when the change occurred;
• incrementing a chronological counter when a prediction based on a current weight vector for a selected element is chronologically incorrect;
• incrementing a label counter when (i) a true health status of the selected element is known and (ii) a predicted health status does not match the true health status;
• incrementing an unlabel counter when (i) the true health status of the selected element is unknown and (ii) a confidence in the predicted health status is low;
• updating the weight vector using the chronological counter, the label counter, and the unlabel counter;
• and
• training the predictive model for optimized performance based on the unlabeled data, the labeled data, and the classified data.

A system for detecting status changes, and a corresponding point-in-time, in one or more monitored entities, the system comprising:

• a server having a control processor and a transductive learning unit, the transductive learning unit including a max-margin temporal classifier unit;
• the server in communication with at least one computing device across an electronic network, the at least one computing device having a memory with time-series data;
• the server configured to receive the time-series data;
• the control processor configured to execute executable instructions that cause the system to perform a method of:
• receiving, via one or more sensors, one or more signals being representative of an operational state of the one or more monitored entities;
• transferring, via the one or more sensors, one or more elements of time-series data derived from the one or more signals, to a processor being communicatively coupled to the one or more sensors, the time-series data comprising both labeled and unlabeled data;
• wherein the labeled data includes:
• initial measurements from all of the monitored entities and final measurements from monitored entities with known unhealthy states;
• creating a predictive model from the time-series data in a datastore memory;
• applying a transductive classifier to the predictive model, the transductive classifier detecting a change from healthy to unhealthy in the time-series data, and the corresponding point-in-time when the change occurred;
• classifying, by the transduction classifier, the unlabeled data, generating classified data to (i) classify a plurality of final states of monitored entities with unknown final diagnoses to identify those with disorders, and (ii) classify intermediate observations to identify the corresponding point-in-time when the change occurred;
• providing an identification of the change in the time-series data and the corresponding point-in-time;
• and
• training the predictive model for optimized performance based on the unlabeled data, the labeled data, and the classified data.