# New approach for health assessment of high voltage motor using experimental case studies

## TL;DR

This paper presents a new method to assess the health of high-voltage motors using existing data and industry standards, avoiding the need for extra sensors.

## Contribution

A novel Health Index calculation method for high-voltage motors using multi-criteria analysis and operational data.

## Key findings

- The Health Index integrates diagnostic tests and condition factors using standardized criteria.
- Experimental case studies validate the method's robustness and applicability in real-world settings.
- The approach supports proactive maintenance and reduces unexpected motor failures.

## Abstract

This paper introduces a new approach for comprehensive Health Index (HI) assessment of high-voltage (HV) induction motors used in oil and gas plants. The proposed method is designed to be practical, relying on readily available operational and maintenance data without requiring the installation of additional sensors. It accounts for real-world limitations in data acquisition and incorporates internationally recognized criteria from IEC, IEEE, and CIGRE standards. The Health Index calculation integrates both conventional diagnostic test results, such as Partial Discharge (PD), Insulation Resistance (IR), Polarization Index (PI), Tan Delta (TD), vibration measurements, and complementary information/ Conditional Factors CF, including physical condition, maintenance history recorded in the Standard Assessment Procedure (SAP), and aging factors. Condition ratings, weighting factors, and parameter-specific scoring are systematically applied to provide a balanced assessment. By adopting a multi-criteria analysis framework, the proposed method consolidates diverse parameters into a unified, condition-based Health Index. The significance of this work lies in its ability to support proactive asset management, minimize unexpected failures, and ensure the reliable and efficient operation of HV motors. Experimental case studies further demonstrate the applicability and robustness of the approach.