Work

Early fault detection in gearboxes via dynamic principal component analysis–driven multivariate statistical process control

Year: 2026

Type: article

Abstract: Early detection of gearbox failure is essential due to their critical role in industrial operations. Therefore, effective condition monitoring techniques are required to identify incipient deviations in operational behaviour. Therefore, this study proposes a dynamic principal component analysis methodology, integrated within a multivariate statistical process control framework, to detect progressive failures in spur gearboxes from vibration signals. The signal is segmented into sub-windows and characterised using condition indicators in the time and frequency domains. Diagnosis is based on Hotelling's T2 statistic and the squared prediction error, which define statistical control limits to discriminate between normal and failure conditions. Empirical validation uses an experimental dataset covering combinations of load, speed, and failure severity. The results demonstrate high sensitivity to progressive degradation and accurate early-stage detection, supporting the multivariate statistical process control approach with dynamic principal component analysis as an effective tool for diagnosis and predictive maintenance in high-criticality industrial environments.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0348497

Authors: Antonio Pérez-Torres, Jean Navarrete-Campos, Reinier Fernández-López, Jorge Figueroa-Zúñiga, Susana Barceló-Cerdá

Institutions: Politecnica Salesiana University, Universitat Politècnica de València, University of Concepción, Universidad Central de Chile

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,382