Fault Detection and Diagnosis – what’s up?​

The relationship between ship maintenance and data technology remains foggy, but we hope this will change. While they can work together, it is challenging because new technologies are being adopted slowly—perhaps too slowly—by maritime stakeholders.

I’ve been studying the current state-of-the-art methods of data-driven maintenance applied to ships, and it is interesting how the research is growing in this field. Even class societies are looking at new applications for Fault Detection and Diagnosis Systems (FDD).

Today, I read another paper by Valesco-Gallego et al. (2023). In this paper, the authors conducted a literature review on recent advancements in data-driven methods for FDD, focusing on data preprocessing, fault diagnosis, and prognosis in marine systems from 2016 to 2022.

The idea of applying data-driven methods to maintenance arises because the effectiveness of a Machine Learning algorithm is way higher than that of a human when evaluating a high volume of data and its attributes. The possibility of identifying the initial phase of a failure enables the ship operator to take action, avoiding unexpected breakdowns.

What is being applied for FDD?
Machine Learning methods such as Deep Learning, Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs) are among the most commonly applied techniques in many of the papers reviewed by the authors. 

For many researchers, however, the lack of labeled data (fault/no fault or fault classification) remains a significant challenge. As a result, many are turning to anomaly detection and unsupervised learning methods to achieve the primary goal: fault detection.

New equipments are being delivered with high-tech sensors, and with possibilities of data collection. With good data analytics and data science, it give the operator good insights into the state of the equipment and opens an opportunity to apply condition-based maintenance and predictive maintenance, which can improve current maintenance strategies.

The image attached is an overall mind map I made connecting the information from Valesco-Gallego’s paper.