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Updated: Jun 27, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

From Signals to Remaining Useful Life: Multimodal Sensor Fusion for Fault Diagnosis and Prognostics-Methods,

Cristina Floriana Pană¹, Camelia Adela Maican², Nicolae Răzvan Vrăjitoru¹

¹Department of Mechatronics and Robotics, University of Craiova, 200585 Craiova, Romania.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

Sensor fusion enhances robotic system prognostics but is vulnerable to sensor faults. This review details fault-aware fusion strategies and proposes standards for reliable diagnosis and Remaining Useful Life estimation in safety-critical applications.

Keywords:

domain shift fault diagnosis fault-tolerant systems multimodal sensor fusion predictive maintenance prognostics and health management remaining useful life sensor faults uncertainty quantification

Related Experiment Videos

Last Updated: Jun 27, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Area of Science:

Robotics and Mechatronics
Sensor Systems Engineering
Data Fusion and Signal Processing

Background:

Multimodal sensor fusion is vital for Remaining Useful Life (RUL) estimation in mechatronic and robotic systems.
Real-world sensor faults (bias, drift, dropouts, etc.) compromise fusion pipeline integrity, leading to inaccurate prognostics and poor generalization.
These issues are critical in safety-sensitive applications like collaborative robots and wearable devices.

Purpose of the Study:

To systematically review sensor fault taxonomies, fault-aware fusion strategies, and the impact of faults on diagnosis and RUL estimation.
To identify and synthesize current research on multimodal sensor fusion in the context of sensor faults.
To propose practical reporting standards for sensor-fusion-based diagnosis and prognostics to enhance research reproducibility and deployment readiness.

Main Methods:

Conducted a systematic scoping review of peer-reviewed literature on sensor fusion and fault diagnosis.
Extracted data on sensor modalities, fault characterization/injection, fusion architectures, validation settings, and reporting completeness.
Analyzed fault-aware fusion strategies at data-, feature-, and decision-levels.

Main Results:

Identified diverse sensor fault types and their impact on multimodal fusion pipelines.
Synthesized various fault-aware fusion strategies and their effectiveness.
Highlighted the critical need for standardized reporting of synchronization, fault ground truth, and uncertainty calibration.

Conclusions:

Sensor faults significantly challenge the reliability of multimodal sensor fusion for diagnosis and prognostics.
Fault-aware fusion strategies are essential for robust system performance and safety.
Adoption of proposed reporting standards will improve research comparability, reproducibility, and readiness for real-world deployment.