Fiix Inc.

Rockwell Automation Technologies Incorporated

Taylor Mathewson

Peter Doulas

Mohammad Esmalifalak

Akshay Iyengar

Seyedmorteza Mirhoseininejad

Francis Emery

William Hogan

Min Hua Yu

Ryan P Dunn

Chirayu S Shah

Nabin Acharya

Anoop Mohan

Meraj Imani

Sneha R Jadhav

Stuart Fergusson

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.

Machine learning powered anomaly detection for maintenance work orders

An industrial information hub (IIH) serves as a single industrial ecosystem platform where multiple participants can deliver repeatable and standardized services relevant to their core competencies. The IIH system can interface with a work order management system to facilitate automated creation and submission of work orders for maintenance tasks. To this end, the IIH system can monitor real-time data from industrial devices and identify conditions indicative of a performance issue requiring a maintenance action. When such a condition is identified, the IIH system can initiate creation of a work order in the work order management system for tracking and recording of the maintenance task.

Industrial machine monitoring path for computerized maintenance management system

Growing community of inventors

Toronto, Canada

Taylor Mathewson