Users of high availability (HA) computing require systems that run continuously, with little or no downtime. Modern PCs address HA needs by monitoring operating system parameters such as voltage, temperature, and hard drive status in order to anticipate possible system failure. However, one modality for PC monitoring that has been underutilized is sound. The application described here uses wavelet theory to analyze sounds produced by PC hard drives during standard operation. When twenty-nine hard drives were tested with the application and the results compared with the drives' Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) data, the binomial distribution's low p-value of 0.012 indicated better than chance agreement. While the concurrence between the two systems shows that sound is an effective tool in detecting hardware failures, the disagreements between the systems show that the application can complement S.M.A.R.T. in an HA system.
","abstract_html":"<p>Users of high availability (HA) computing require systems that run continuously, with little or no downtime. Modern PCs address HA needs by monitoring operating system parameters such as voltage, temperature, and hard drive status in order to anticipate possible system failure. However, one modality for PC monitoring that has been underutilized is sound. The application described here uses wavelet theory to analyze sounds produced by PC hard drives during standard operation. When twenty-nine hard drives were tested with the application and the results compared with the drives' Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) data, the binomial distribution's low p-value of 0.012 indicated better than chance agreement. While the concurrence between the two systems shows that sound is an effective tool in detecting hardware failures, the disagreements between the systems show that the application can complement S.M.A.R.T. in an HA system.</p>","abstract_has_math":false,"creators":["Henry, Robert Karns"],"institution":null,"degree_name":"MS (Master of Science)","degree_level":"Thesis - unrestricted","degree_discipline":"Computer and Information Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-12-17T08:00:00Z","date_published":"2005-12-17T08:00:00Z","updated_at":"2026-07-24T02:19:43Z","subjects":["failure prediction","Linux","ALSA","SM.A.R.T.","Monitor","DSP","Daubechies","Wavelet","remote","vibration analysis.","Computer Sciences","Physical Sciences and Mathematics"],"languages":[],"rights":["Copyright by the authors."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dc.etsu.edu/etd/1076","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Henry, Robert Karns"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2005-12-17T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer and Information Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - unrestricted"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS (Master of Science)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["failure prediction","Linux","ALSA","SM.A.R.T.","Monitor","DSP","Daubechies","Wavelet","remote","vibration analysis.","Computer Sciences","Physical Sciences and Mathematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Copyright by the authors."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dc.etsu.edu/context/etd/article/2233/viewcontent/HenryR110905f.pdf","https://dc.etsu.edu/etd/1076"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Users of high availability (HA) computing require systems that run continuously, with little or no downtime. Modern PCs address HA needs by monitoring operating system parameters such as voltage, temperature, and hard drive status in order to anticipate possible system failure. However, one modality for PC monitoring that has been underutilized is sound. The application described here uses wavelet theory to analyze sounds produced by PC hard drives during standard operation. When twenty-nine hard drives were tested with the application and the results compared with the drives' Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) data, the binomial distribution's low p-value of 0.012 indicated better than chance agreement. While the concurrence between the two systems shows that sound is an effective tool in detecting hardware failures, the disagreements between the systems show that the application can complement S.M.A.R.T. in an HA system.
"]},{"key":"dc:title","label":"Title","values":["Monitoring PC Hardware Sounds in Linux Systems Using the Daubechies D4 Wavelet."]}]}],"canonical_facts":{"dc:creator":["Henry, Robert Karns"],"dc:date.issued":["2005-12-17T08:00:00Z"],"dc:description.abstract":["Users of high availability (HA) computing require systems that run continuously, with little or no downtime. Modern PCs address HA needs by monitoring operating system parameters such as voltage, temperature, and hard drive status in order to anticipate possible system failure. However, one modality for PC monitoring that has been underutilized is sound. The application described here uses wavelet theory to analyze sounds produced by PC hard drives during standard operation. When twenty-nine hard drives were tested with the application and the results compared with the drives' Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) data, the binomial distribution's low p-value of 0.012 indicated better than chance agreement. While the concurrence between the two systems shows that sound is an effective tool in detecting hardware failures, the disagreements between the systems show that the application can complement S.M.A.R.T. in an HA system.
"],"dc:identifier":["https://dc.etsu.edu/context/etd/article/2233/viewcontent/HenryR110905f.pdf","https://dc.etsu.edu/etd/1076"],"dc:rights":["Copyright by the authors."],"dc:subject":["failure prediction","Linux","ALSA","SM.A.R.T.","Monitor","DSP","Daubechies","Wavelet","remote","vibration analysis.","Computer Sciences","Physical Sciences and Mathematics"],"dc:title":["Monitoring PC Hardware Sounds in Linux Systems Using the Daubechies D4 Wavelet."],"thesis:degree_discipline":["Computer and Information Science"],"thesis:degree_level":["Thesis - unrestricted"],"thesis:degree_name":["MS (Master of Science)"]},"updated_at":"2026-07-24T02:19:43Z"}