a new failure prediction technique Special math needed for explanations: Probability theory Special math needed to use results: None Results useful to: Error log analysis and failure prediction Abstract- Most error log analysis studies perform a statistical fit to the data assuming a single underlying error process. This paper presents the results of an analysis that demonstrates the log is composed of at least two error processes: transient and inter-mittent. The mixing of data from multiple processes requires many more events to verify a hypothesis using traditional statistical analysis. Based on the shape of the interarrival time function of the intermittent errors observed from actual error logs, a failure prediction heuristic, the Dispersion Frame Technique (DFT), is developed. The DFT was implemented in a distributed on-line monitoring and predictive diagnostic system for the campus-wide Andrew file system at Carnegie Mellon University. Data collected from 13 file servers over a 22 month period were analyzed using both the DFT and conventional statistical methods. It is shown that the DFT can extract intermittent errors from the error log and uses only one fih of the error log entry points required by statistical methods for failure prediction. The DFT achieved a 93.7 % suc-cess rate in failure prediction of both electromechanical and elec-tronic devices. I.