A computer-implemented method for debris particle detection with adaptive learning in a gas turbine engine , the method comprising: receiving oil debris monitoring (ODM) sensor data from an oil debris monitor sensor and fleet data from a database; detecting a feature in the ODM sensor data; generating an anomaly detection signal based on detecting an anomaly by comparing the feature in the ODM sensor data to a limit defined by system information stored in the fleet data , wherein the anomaly includes the system operating outside normal operating conditions; selecting a maintenance action request based on the anomaly detection signal; adjusting one or more of the feature, the limit, and the maintenance action request by applying an adaptive learning algorithm that uses the ODM sensor data, fleet data , and feedback from field maintenance of one or more engines serving as ground truth of particle detection that evolves over time, training the adaptive learning algorithm using a set of historical ODM sensor data from the fleet data to train the adaptive learning algorithm to differentiate the characteristics of parameters with or without a particle, and applying the adaptive learning algorithm to on-board streaming parameters to detect particles in real-time, wherein the on-board streaming parameters are parameters that are collected on-board the ODM sensor, and wherein applying the adaptive learning algorithm includes applying a supervised machine learning classification algorithm in the form of a support vector machine to the on-board streaming parameters to improve the accuracy of differentiating characteristics of parameters with or without a debris particle. receiving oil debris monitoring (ODM) sensor data from an oil debris monitor sensor and fleet data from a database; detecting a feature in the ODM sensor data; generating an anomaly detection signal based on detecting an anomaly by comparing the feature in the ODM sensor data to a limit defined by system information stored in the fleet data , wherein the anomaly includes the system operating outside normal operating conditions; selecting a maintenance action request based on the anomaly detection signal; adjusting one or more of the feature, the limit, and the maintenance action request by applying an adaptive learning algorithm that uses the ODM sensor data, fleet data , and feedback from field maintenance of one or more engines serving as ground truth of particle detection that evolves over time, training the adaptive learning algorithm using a set of historical ODM sensor data from the fleet data to train the adaptive learning algorithm to differentiate the characteristics of parameters with or without a particle, and applying the adaptive learning algorithm to on-board streaming parameters to detect particles in real-time, wherein the on-board streaming parameters are parameters that are collected on-board the ODM sensor, and wherein applying the adaptive learning algorithm includes applying a supervised machine learning classification algorithm in the form of a support vector machine to the on-board streaming parameters to improve the accuracy of differentiating characteristics of parameters with or without a debris particle. <EOS>