Asset condition monitoring method with automatic anomaly detection, comprising the steps: receiving (S10) local condition data (DC) from an asset fleet , relating to a plurality of assets; wherein the local condition data (DC) relate to a condition of an asset of the asset fleet , wherein the local condition data comprise time series data of the condition of the asset of the asset fleet ; the method being characterized in the steps of: identifying (S20) at least one anomaly (A) in the received condition data (DC), wherein the identified anomaly (A) relates to unexpected condition data of the asset; wherein the anomaly is identified by an anomaly detection method, which is configured for processing the time series data comprising running a generic test that identifies the anomaly; identifying (S30) a new potential failure case (F), relating to a new failure in an asset, dependent on the identified anomaly (A) and additional system data provided by the respective asset; determining (S40) a specific condition model dependent on the identified new potential failure case (F), wherein the specific condition model is configured for predicting the new failure; and providing (S50) the specific condition model to the plurality of assets and/or to digital models of the plurality of assets. receiving (S10) local condition data (DC) from an asset fleet , relating to a plurality of assets; wherein the local condition data (DC) relate to a condition of an asset of the asset fleet , wherein the local condition data comprise time series data of the condition of the asset of the asset fleet ; the method being characterized in the steps of: identifying (S20) at least one anomaly (A) in the received condition data (DC), wherein the identified anomaly (A) relates to unexpected condition data of the asset; wherein the anomaly is identified by an anomaly detection method, which is configured for processing the time series data comprising running a generic test that identifies the anomaly; identifying (S30) a new potential failure case (F), relating to a new failure in an asset, dependent on the identified anomaly (A) and additional system data provided by the respective asset; determining (S40) a specific condition model dependent on the identified new potential failure case (F), wherein the specific condition model is configured for predicting the new failure; and providing (S50) the specific condition model to the plurality of assets and/or to digital models of the plurality of assets. identifying (S20) at least one anomaly (A) in the received condition data (DC), wherein the identified anomaly (A) relates to unexpected condition data of the asset; wherein the anomaly is identified by an anomaly detection method, which is configured for processing the time series data comprising running a generic test that identifies the anomaly; identifying (S30) a new potential failure case (F), relating to a new failure in an asset, dependent on the identified anomaly (A) and additional system data provided by the respective asset; determining (S40) a specific condition model dependent on the identified new potential failure case (F), wherein the specific condition model is configured for predicting the new failure; and providing (S50) the specific condition model to the plurality of assets and/or to digital models of the plurality of assets. <EOS>