A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics

Area of Science:

• * Industrial Engineering and Asset Management
• * Machine Learning and Artificial Intelligence

Background:

• * Proliferation of multi-sensor systems in asset management, driven by Industry 4.0 and the Internet of Things (IoT).
• * Increasing need for prognostics and health management (PHM) systems to predict system reliability and optimize maintenance.
• * Challenges posed by big machinery data and the limitations of traditional methods in handling multi-sensor fusion for Remaining Useful Life (RUL) prediction.

Purpose of the Study:

• * To propose a novel deep, adversarial approach for Remaining Useful Life (RUL) prediction.
• * To develop a non-Markovian, variational, inference-based model integrated with adversarial methodology.
• * To address the limitations of traditional prediction methods in processing multi-sensor data streams.

Main Methods:

• * Development of a novel deep adversarial model for RUL prediction.
• * Incorporation of non-Markovian, variational inference techniques.
• * Utilizing multi-sensor fusion for integrated prognostic capabilities.

Main Results:

• * The proposed deep adversarial approach demonstrated favorable results on public datasets.
• * Effective prediction of Remaining Useful Life (RUL) using multi-sensor data.
• * Performance comparison against similar deep learning models validated the approach's efficacy.

Conclusions:

• * The novel deep adversarial model offers a promising solution for RUL prediction in asset management.
• * The approach effectively handles complex, multi-sensor machinery data for enhanced reliability assessment.
• * This method advances the field of prognostics and health management systems.