{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11487996","patent":{"patent_number":"US-11487996","title":"Real-time predictive maintenance of hardware components using a stacked deep learning architecture on time-variant parameters combined with a dense neural network supplied with exogeneous static outputs","assignee":null,"inventors":[],"filing_date":"2019-06-03T00:00:00.000Z","publication_date":"2022-11-01T00:00:00.000Z","cpc_codes":["G06N","G06F","G06N","G06N","G06N","G06N","G06N"],"num_claims":20,"abstract":"A system, method, and computer-readable medium are provided for a hardware component failure prediction system that can incorporate a time-series dimension as an input while also addressing issues related to a class imbalance problem associated with failure data. Embodiments utilize a double-stacked long short-term memory (DS-LSTM) deep neural network with a first layer of the DS-LSTM passing hidden cell states learned from a sequence of multi-dimensional parameter time steps to a second layer of the DS-LSTM that is configured to capture a next sequential prediction output. Output from the second layer is combined with a set of categorical variables to an input layer of a fully-connected dense neural network layer. Information generated by the dense neural network provides prediction of whether a hardware component will fail in a given future time interval."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Real-time predictive maintenance of hardware components using a stacked deep learning architecture on time-variant parameters combined with a dense neural network supplied with exogeneous static outputs","description":"A system, method, and computer-readable medium are provided for a hardware component failure prediction system that can incorporate a time-series dimension as an input while also addressing issues rel","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11487996","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11487996","citation_suggestion":"Patentable. \"Real-time predictive maintenance of hardware components using a stacked deep learning architecture on time-variant parameters combined with a dense neural network supplied with exogeneous static outputs\" (US-11487996). https://patentable.app/patents/US-11487996","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11487996","json":"https://patentable.app/api/llm-context/US-11487996","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T05:37:49.348Z"}