TY - JOUR

T1 - Multicondition Health Condition Assessment for Electric Motors Based on Knowledge Embedding Machine Learning and Statistical Data Fusion

AU - Hailati, Gulizhati

AU - Sun, Shengxin

AU - Xie, Da

AU - Zhou, Kai

AU - Ding, Feng

AU - Fan, Xiaochao

AU - Hu, Yiheng

AU - Zhao, Nan

PY - 2025/8/18

Y1 - 2025/8/18

N2 - In industrial applications, motor operational status is crucial for production efficiency. However, timely detection and prediction of motor faults present significant challenges, often resulting in production incidents and substantial maintenance costs. This paper presents a novel approach for assessing motor equipment health based on knowledge‐embedded machine learning and statistical data evaluation. Specifically, the methodology first employs mechanism‐based motor operational models and statistical methods to identify key variable parameters associated with typical operational states from extensive monitoring variables, serving as input layers for machine learning algorithms. Subsequently, the study utilises machine learning algorithms to predict labels for normal operation, phase loss faults and overload faults, incorporating health degradation levels as knowledge‐embedded foundations for the health state assessment. Finally, the Comprehensive Health Index (CHI) was evaluated, achieving 98.1% health assessment accuracy on test datasets in environments with data sampling frequencies below 1 Hz and relatively low data quality. This methodology establishes relationships between health states and actual fault records through a dynamic weight allocation strategy that provides quantified percentage values, reflecting actual equipment usage patterns and degradation trends. It bridges the gap between theoretical diagnostic accuracy and practical industrial implementation requirements, providing highly robust maintenance strategies for industrial scenarios.

AB - In industrial applications, motor operational status is crucial for production efficiency. However, timely detection and prediction of motor faults present significant challenges, often resulting in production incidents and substantial maintenance costs. This paper presents a novel approach for assessing motor equipment health based on knowledge‐embedded machine learning and statistical data evaluation. Specifically, the methodology first employs mechanism‐based motor operational models and statistical methods to identify key variable parameters associated with typical operational states from extensive monitoring variables, serving as input layers for machine learning algorithms. Subsequently, the study utilises machine learning algorithms to predict labels for normal operation, phase loss faults and overload faults, incorporating health degradation levels as knowledge‐embedded foundations for the health state assessment. Finally, the Comprehensive Health Index (CHI) was evaluated, achieving 98.1% health assessment accuracy on test datasets in environments with data sampling frequencies below 1 Hz and relatively low data quality. This methodology establishes relationships between health states and actual fault records through a dynamic weight allocation strategy that provides quantified percentage values, reflecting actual equipment usage patterns and degradation trends. It bridges the gap between theoretical diagnostic accuracy and practical industrial implementation requirements, providing highly robust maintenance strategies for industrial scenarios.

KW - knowledge embedding

KW - data analysis

KW - AC motors

KW - health condition

KW - fault diagnosis

U2 - 10.1049/elp2.70090

DO - 10.1049/elp2.70090

M3 - Journal article

VL - 19

JO - IET Electric Power Applications

JF - IET Electric Power Applications

SN - 1751-8660

IS - 1

M1 - e70090

ER -