Evidence Chain Selection and Cross-Domain Physical Context of a Durability-Sensitive Vibration Feature During a 300 h Diesel Engine Endurance Test
Long-duration diesel engine condition monitoring requires vibration descriptors that remain comparable over time despite path-dependent structural responses and physical measurements acquired on different schedules. A methodological need therefore remains for transparent multi-location screening followed by retention of one unchanged descriptor across a complete endurance sequence. This study develops an evidence chain workflow using a 300 h WP4.6 whole-engine endurance test at 2100 r/min and 600 N·m. In a representative synchronized record, cylinder pressure and nine vibration channels were sampled at 200 kHz, while complete eleven-node longitudinal histories were available only for liner X/Y/Z. After operating boundary and record integrity checks, multi-domain screening selected cylinder-2 Liner-Y 3–7 kHz integrated band power and retained this definition unchanged across all endurance nodes. The descriptor increased overall from 78.57 to 613.68 a.u.2 (R2=0.827; Spearman ρ=0.936) but remained non-monotonic, including a 40.0% decrease from 180 to 210 h. Mean cumulative bearing shell loss progressed from 3.126 to 6.590 and 10.776 mg at 100, 200, and 300 h, while post-test liner geometry showed a self-referenced circumferential-mean peak of 12.17 μm and maximum ovality of 26.67 μm. These non-equivalent physical measurements provide complementary context rather than synchronized wear labels. The workflow therefore supports auditable long-duration response screening and inspection prioritization for the present engine–sensor configuration, without establishing calibrated wear prediction or universal transferability of the selected channel or frequency band.
Authors
- Haiyong Peng (ORCID: https://orcid.org/0000-0002-1575-9218)
- Hanwen Ma
Institutions
- Shanghai University of Engineering Science (CN)
Publication Details
- Journal
- Applied Sciences
- Published
- 2026-09-29
- DOI
- https://doi.org/10.3390/app16199682
- Primary Topic
- Machine Fault Diagnosis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00