Base Load Fabric Aging Data for Washing Machine Testing

A dataset of 5.5 KB contains image-derived features from fabric samples used in washing machine performance testing. The data, authored by Shaojin Ma and updated in April 2026, includes color, texture, and area features extracted from wrinkle and weave information of base loads subjected to 1–100 accelerated washing cycles. It supports training classifiers like logistic regression for objective age grading.

Use Cases

• Train machine learning classifiers for fabric age grading based on extracted color, texture, and area features.
• Validate computer vision techniques for objective assessment of textile degradation in standardized testing.
• Compare the predictive performance of features derived from wrinkle information versus plain weave structure.
• Benchmark classifier performance (e.g., logistic regression, kNN) for automated fabric lifecycle monitoring.

Strengths

• Dataset size is 5.5 KB, indicating a compact and focused collection.
• Age grading is validated against five distinct degradation stages (C1–C5) covering 1–100 washing cycles.
• Classifier performance metrics are provided, including logistic regression achieving accuracy up to 1.00 for the oldest stage.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Sample size is described as a potential area for future expansion, suggesting a limited initial scope.

Provenance

Source

figshare

Collection Method

Features extracted via computer vision from images of fabric samples undergoing accelerated washing cycles.

Freshness

Last updated 2026-04-10 17:50:12; freshness should be verified.