10. A method, comprising: receiving, from one or more sensors of an asset, on-site monitoring (OSM) data, the OSM data comprising time-series data associated with one or more respective units of the asset; remove a first respective portion of the time-series data associated with noise, wherein a second respective portion of the time-series data that is not removed comprises principal information for use in predicting a fault; extracting the principal information from the time-series data; combining the extracted principal information; and predicting the fault in the asset based at least in part on the combined principal information, wherein the prediction comprises determining a probability that the fault will occur at each of the one or more respective units for which time-series data was received.
10. A method, comprising: receiving, from one or more sensors of an asset, on-site monitoring (OSM) data, the OSM data comprising time-series data associated with one or more respective units of the asset; remove a first respective portion of the time-series data associated with noise, wherein a second respective portion of the time-series data that is not removed comprises principal information for use in predicting a fault; extracting the principal information from the time-series data; combining the extracted principal information; and predicting the fault in the asset based at least in part on the combined principal information, wherein the prediction comprises determining a probability that the fault will occur at each of the one or more respective units for which time-series data was received. 18. The method of claim 10 , wherein extracting the principal information from the time-series data comprises conducting wavelet signal analysis to segregate at least one of the noise, error, or incoherence information followed by conducting discrete wavelet packet analysis to decompose the denoised signal into multi-resolution, time-frequency domains.