Towards a more realistic evaluation of machine learning models for bearing fault diagnosis
João Paulo Vieira; Victor Afonso Bauler; Rodrigo Kobashikawa Rosa; Danilo Silva · 2026 · Mechanical Systems and Signal Processing
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery. While recent advances in machine learning (ML), particularly deep learning, have shown strong performance in controlled settings, many studies fail to generalize to real-world applications due to methodological flaws, most notably data leakage. This paper investigates the issue of data leakage in vibration-based bearing fault diagnosis and its impact on model evaluation. We demonstrate that common dataset partitioning strategies, such as segment-wise and condition-wise
Abstract by João Paulo Vieira; Victor Afonso Bauler; Rodrigo Kobashikawa Rosa; Danilo Silva, Mechanical Systems and Signal Processing (2026) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Aggregation Kinetics of Graphene Oxides in Aqueous Solutions: Experiments, Mechanisms, and Modeling
Negative / Null Result ReportThe reaction between metakaolin and limestone and its effect in porosity refinement and mechanical properties
Negative / Null Result ReportTuning Alginate Bioink Stiffness and Composition for Controlled Growth Factor Delivery and to Spatially Direct MSC Fate within Bioprinted Tissues
Negative / Null Result ReportEx-situ characterisation of gas diffusion layers for proton exchange membrane fuel cells
Negative / Null Result ReportCobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network
Negative / Null Result ReportDeterminants of residential water consumption: Evidence and analysis from a 10‐country household survey
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1016/j.ymssp.2026.114640