A method for predicting failure of a noise-emitting apparatus, comprising - receiving noise signals from a noise-emitting apparatus during consecutive periods of time, and - for each of said consecutive periods of time, extracting from said received noise signals noise sections with respect to noise events contained in said noise sections and corresponding to detectable activity within the corresponding noise signal; - generating section clusters of noise sections belonging to a first period of time depending on a similarity measure between said noise sections, and assigning a corresponding section cluster timestamp; - for each subsequent period of time, at least rebuilding said section clusters, wherein noise sections belonging to said subsequent period of time are assigned depending on said similarity measure to said section clusters or newly generated section clusters, and assigning a subsequent corresponding section cluster timestamp, to generate sequences of connected section clusters; - after occurrence of a failure of the noise-emitting apparatus, assigning at least a failure timestamp to the sequence of connected section clusters that comprises at least the section cluster having assigned the last noise section prior to said failure; and - receiving query noise signals from the noise-emitting apparatus; - extracting a query noise section from the query noise signals and assigning said query noise section to a most similar query section cluster of the section clusters with respect to said similarity measure, and - predicting a remaining time to failure depending at least on a comparison between the corresponding section cluster timestamp of said most similar query section cluster and said failure timestamp assigned to the sequence of connected section clusters said most similar query section cluster belongs to. - receiving noise signals from a noise-emitting apparatus during consecutive periods of time, and - for each of said consecutive periods of time, extracting from said received noise signals noise sections with respect to noise events contained in said noise sections and corresponding to detectable activity within the corresponding noise signal; - generating section clusters of noise sections belonging to a first period of time depending on a similarity measure between said noise sections, and assigning a corresponding section cluster timestamp; - for each subsequent period of time, at least rebuilding said section clusters, wherein noise sections belonging to said subsequent period of time are assigned depending on said similarity measure to said section clusters or newly generated section clusters, and assigning a subsequent corresponding section cluster timestamp, to generate sequences of connected section clusters; - after occurrence of a failure of the noise-emitting apparatus, assigning at least a failure timestamp to the sequence of connected section clusters that comprises at least the section cluster having assigned the last noise section prior to said failure; and - receiving query noise signals from the noise-emitting apparatus; - extracting a query noise section from the query noise signals and assigning said query noise section to a most similar query section cluster of the section clusters with respect to said similarity measure, and - predicting a remaining time to failure depending at least on a comparison between the corresponding section cluster timestamp of said most similar query section cluster and said failure timestamp assigned to the sequence of connected section clusters said most similar query section cluster belongs to. <EOS>