A method for predicting surface failure of a surface, the method comprising the steps of: obtaining a group of measured datasets, each including: a measurement value of at least a first type for each of a plurality of grid elements, each grid element associated with a location on the surface; and a time value, such that the group of datasets includes datasets associated with a plurality of unique time values, characterised in that the method further comprises the steps of: by means of a computing device: for each measured dataset, analysing the measurement values to identify one or more clusters of measurement values and a plurality of outlier measurement values not associated with a cluster, wherein the, or each, cluster represents a plurality of grid elements having similar measurement values according to a predefined connection rule to one another and wherein the remaining grid elements not associated with a cluster are identified to be outliers, identifying an interface set of grid elements for each measured dataset, each interface set comprising a subset of grid elements of the associated measured dataset corresponding to the grid elements identified as outliers, determining a risk of surface failure in accordance with identification of a pattern defined by the physical locations of the grid elements of the interface sets which has a persistent location with respect to the surface over a plurality of measured datasets, such that the pattern has a persistent location over a period of time. obtaining a group of measured datasets, each including: a measurement value of at least a first type for each of a plurality of grid elements, each grid element associated with a location on the surface; and a time value, such that the group of datasets includes datasets associated with a plurality of unique time values, characterised in that the method further comprises the steps of: by means of a computing device: for each measured dataset, analysing the measurement values to identify one or more clusters of measurement values and a plurality of outlier measurement values not associated with a cluster, wherein the, or each, cluster represents a plurality of grid elements having similar measurement values according to a predefined connection rule to one another and wherein the remaining grid elements not associated with a cluster are identified to be outliers, identifying an interface set of grid elements for each measured dataset, each interface set comprising a subset of grid elements of the associated measured dataset corresponding to the grid elements identified as outliers, determining a risk of surface failure in accordance with identification of a pattern defined by the physical locations of the grid elements of the interface sets which has a persistent location with respect to the surface over a plurality of measured datasets, such that the pattern has a persistent location over a period of time. for each measured dataset, analysing the measurement values to identify one or more clusters of measurement values and a plurality of outlier measurement values not associated with a cluster, wherein the, or each, cluster represents a plurality of grid elements having similar measurement values according to a predefined connection rule to one another and wherein the remaining grid elements not associated with a cluster are identified to be outliers, identifying an interface set of grid elements for each measured dataset, each interface set comprising a subset of grid elements of the associated measured dataset corresponding to the grid elements identified as outliers, determining a risk of surface failure in accordance with identification of a pattern defined by the physical locations of the grid elements of the interface sets which has a persistent location with respect to the surface over a plurality of measured datasets, such that the pattern has a persistent location over a period of time.