Abstract
Winding deformities in distribution transformers pose significant risks to operational
reliability and system safety. Frequency response analysis (FRA) is a well-established
technique for identifying mechanical faults; however, its diagnostic reliability is hindered
by subjectivity in interpreting response signatures. This study proposes a novel diagnostic
technique, termed FRA6σ, which integrates Six Sigma (6σ) statistical tools with FRA to enable
objective fault detection. The methodology employs control charts (X chart, R-chart) to
monitor deviations from baseline signatures and utilizes process capability indices (Cp and
Cpk) to quantify the severity of deviations. Three transformer cases were evaluated across
five defined frequency regions (10 Hz to 2 MHz), each associated with distinct physical
fault types. The FRA6σ approach successfully identified early-stage faults across all cases.
In one instance, axial and radial winding deformation was detected with a Cp of 1.0 and
corresponding range chart violations, preceding any visible damage. Another case revealed
inter-turn insulation degradation in the 100 kHz–1 MHz band with Cpk values below
0.9, prompting immediate intervention. Compared to traditional FRA interpretation, the
proposed method improved diagnostic sensitivity by 31.25% and enabled fault detection
earlier based on retrospective physical inspection benchmarks. The integration of Six Sigma
with FRA provides a structured, quantifiable, and repeatable approach to transformer fault
diagnostics. FRA6σ enhances early detection of winding deformities and dielectric issues,
offering a robust alternative to subjective analysis and supporting predictive maintenance
strategies in power systems.
Keywords: six sigma; control chart; range chart; process capability index; process capability
performance index; distribution transformer; frequency response analysis; winding deformation