Enhancing Industrial Operations through Integrated FBG Vibration Sensors in Predictive Maintenance Decision Support Systems

Abstract

In the industrial environment of today, maximizing operations and reducing downtime are essential objectives. By enabling proactive equipment servicing based on real-time data insights, the advent of Predictive Maintenance (PM) has completely transformed asset management. This study aims to determine whether Fiber Bragg Grating (FBG) vibration sensors can be integrated into a Decision Support System (DSS) to improve PM in industrial operations.

Method: The methods used in this study include a thorough analysis of the vibration of a rotating machine by acquiring real-time vibration signals from the FBG sensor and integrating a DSS scheme for predictive decisions. An experimental case study is also portrayed to showcase the practical approach.

Findings: A rotating machine is used to measure vibration using a conventional piezoelectric sensor. Simultaneously, an FBG sensor is also installed to perform similar vibration measurement activity, and the real-time signal from the FBG sensing setup is acquired to highlight the experimental use case. It is found that the FBG sensor performs high precision sensing with a wide span of frequency spectrum.

Novelty: The FBG sensor signal is largely influenced by noisy components and the effect of the noisy signature needs to be nullified before analyzing the spectrums. A pre-processing scheme is implemented to nullify the effect of noisy signatures present in the raw signal. A decision support system receives the sensor data and uses machine learning algorithms to analyze and interpret it. Machine faults can be identified from the vibration signatures of the FBG sensors. The results of this study demonstrate the significant advantages of FBG vibration sensors in preventive maintenance and DSS. As a result, maintenance tasks can be scheduled throughout pre-arranged downtimes, minimizing operational disruptions and related expenses. This strategy is novel because it combines cutting-edge FBG sensor technology with a decision support system to provide a complete answer for improving industrial operations through preventive maintenance.