Enhancing industrial machinery maintenance through advanced fault and novelty detection using variational autoencoder and hybrid transformer model

In the context of Industry 4.0, new sensing and communication technologies have unlocked vast amounts of process data, offering significant potential for its transformation into actionable insights to support manufacturing decisions. The reliable detection and diagnosis of faults in rolling element bearings pose a significant challenge for condition-based maintenance and fault detection and diagnosis (FDD), which are critical strategies for enhancing equipment reliability and reducing operational costs. Deep learning methods, such as convolutional neural networks (CNNs), can extract features from vibration signals compared to traditional signal processing. However, these methods in isolation are insufficient to reliably detect novel fault conditions and faults in variable working environments. Also, existing novelty and anomaly detection criteria are not accurate enough to correctly distinguish novel or unseen faults. This study introduces a multi-fault detection framework leveraging a variational autoencoder with Mahalanobis distance (MD) novelty scores for unknown condition detection and a hybrid CNN-Swin transformer (Swin-T) model for incremental learning and fault classification. Using frequency-domain transformation and image-based representation of vibration signals, a hybrid model with a CNN-based feature extractor after projecting to the patch embedding layer of a simplified Swin-T model is trained incrementally with novel conditions to allow continuous learning and adaptation. Extensive validation with three separate datasets from fault simulation test rigs demonstrates the superior performance of the method over traditional and cutting-edge models in FDD and novelty detection (ND), achieving near-perfect accuracy (99.7%), precision (99.8%), recall (99.6%), and F1 score (99.7%). ND outperformed traditional approaches with an MD novelty score threshold yielding a true-positive rate of 98.9% and a false-positive rate of 1.2%. Additionally, incremental learning improved classification accuracy by up to 5.4% for newly introduced fault types, highlighting its adaptability. These results demonstrate the framework’s ability to enhance reliability and efficiency in industrial machinery maintenance by identifying both known and novel fault conditions with high precision.