公开(公告)号：US20230027594A1

公开(公告)日：2023-01-26

申请号：US17384181

申请日：2021-07-23

申请人： Mohammad Esmalifalak ,  Akshay Iyengar ,  Seyedmorteza Mirhoseininejad ,  Peter Doulas ,  Francis Emery ,  Taylor Mathewson ,  William Hogan ,  Min Hua Yu

发明人： Mohammad Esmalifalak ,  Akshay Iyengar ,  Seyedmorteza Mirhoseininejad ,  Peter Doulas ,  Francis Emery ,  Taylor Mathewson ,  William Hogan ,  Min Hua Yu

IPC分类号： G06Q10/00 ,  G06N20/00 ,  G06N5/04 ,  G06Q10/06

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(公告)号：US11972398B2

公开(公告)日：2024-04-30

申请号：US17384181

申请日：2021-07-23

申请人： Mohammad Esmalifalak ,  Akshay Iyengar ,  Seyedmorteza Mirhoseininejad ,  Peter Doulas ,  Francis Emery ,  Taylor Mathewson ,  William Hogan ,  Min Hua Yu

发明人： Mohammad Esmalifalak ,  Akshay Iyengar ,  Seyedmorteza Mirhoseininejad ,  Peter Doulas ,  Francis Emery ,  Taylor Mathewson ,  William Hogan ,  Min Hua Yu

IPC分类号： G06Q10/20 ,  G06N5/04 ,  G06N20/00 ,  G06Q10/0635 ,  G06Q10/0875

CPC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.