From data to fault diagnosis: Using predictive maintenance image transformation and nature-inspired optimization techniques for fault classification

Abstract

Using smart sensors and data analytics tools, predictive maintenance (PdM) aims to reduce downtime by forecasting potential faults; however, accurately distinguishing between failure and normal conditions remains a significant challenge. To address this challenge, we propose a fault classification system that transforms 1D PdM sensor signals into 2D images using a novel Predictive Maintenance Image Transformation (PdM-IT) technique, followed by feature extraction with pre-trained convolutional neural networks (CNNs). The extracted features are then combined across all CNN models. We additionally optimize the combined feature set using three nature-inspired optimization algorithms, including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), and then evaluate four different classifier such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), and Decision Trees (DT) on the obtained data using cross-validation. In our experimental evaluation, the best-performing model, SVM with features selected via ACO, achieved 98.22% accuracy, 98.39% F1-score, 98.51% precision, and 98.26% recall. These results show the effectiveness of the proposed hybrid image-based fault classification approach and its potential to support more reliable fault diagnosis and operational planning in PdM systems.