A computer-implemented method for debris particle detection with adaptive learning in a gas turbine engine, the method comprising: receiving oil debris monitoring 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 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.