- 专利标题: Machine learning powered anomaly detection for maintenance work orders
-
申请号: US17384181申请日: 2021-07-23
-
公开(公告)号: US11972398B2公开(公告)日: 2024-04-30
- 发明人: Mohammad Esmalifalak , Akshay Iyengar , Seyedmorteza Mirhoseininejad , Peter Doulas , Francis Emery , Taylor Mathewson , William Hogan , Min Hua Yu
- 申请人: Mohammad Esmalifalak , Akshay Iyengar , Seyedmorteza Mirhoseininejad , Peter Doulas , Francis Emery , Taylor Mathewson , William Hogan , Min Hua Yu
- 申请人地址: CA Toronto
- 专利权人: FIIX INC.
- 当前专利权人: FIIX INC.
- 当前专利权人地址: CA Toronto
- 代理机构: Amin, Turocy & Watson, LLP
- 主分类号: G06Q10/20
- IPC分类号: G06Q10/20 ; G06N5/04 ; G06N20/00 ; G06Q10/0635 ; G06Q10/0875
摘要:
An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.
公开/授权文献
信息查询