Work

Control signal based fault detection in streaming employing Principal Component Analysis and soft sensor in linear actuators

Year: 2026

Type: article

Abstract: The complexity of industrial systems has increased significantly, with many interconnected components, multiple control mechanisms, and a wide range of process variables and operational data. It is crucial to keep track of how these systems are functioning, as any deviation from normal operation due to a malfunction can result in significant financial losses and safety risks. Linear actuators are an essential component in many industrial and technological applications. Used for a wide range of tasks, such as moving, positioning, and controlling objects, they are a key component in many automated systems. This study presents a proposal for fault detection in streaming in linear actuators using Principal Component Analysis (PCA), Hotteling T 2 ( T 2 ), with a Bayesian soft sensor setup. Two different setups were tested: using a fixed threshold, and with an adaptive threshold. Results show that the two strategies were able to identify the three categories of faults, Lack of lubrication, Backlash, and Spalling in some degradation stage or load.

Source: Franklin Open

DOI: 10.1016/j.fraope.2025.100482

Authors: Tyene Zoraski Zanella, Giberto Reynoso-Meza

Institution: Pontifícia Universidade Católica do Paraná

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,900

Funder: Conselho Nacional de Desenvolvimento Científico e Tecnológico