MACHINE LEARNING POWERED ANOMALY DETECTION FOR MAINTENANCE WORK ORDERS

• Main Authors: Doulas, Peter, Hogan, William, Mirhoseininejad, Seyedmorteza, Esmalifalak, Mohammad, Mathewson, Taylor, Emery, Francis, Yu, Min Hua, Iyengar, Akshay
• Format: Patent
• Language: English
• Published: 08.08.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

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.