Online Distribution-Based Clustering Algorithm for Condition Monitoring in Rotating Machines

Continuous monitoring of industrial rotating machines is essential for ensuring health, reliability, and safety, while minimizing costs from unplanned downtime. Traditional systems require domain experts to configure parameters for each machine, limiting adaptability. Machine learning techniques offer alternatives, with supervised approaches demanding labeled datasets that are rarely available due to the extensive effort required from specialists. This study presents an algorithm for unsupervised condition monitoring based on an online distribution-driven clustering approach. The algorithm autonomously learns machine behavior, detecting new operating conditions in real time while recognizing previously observed states. Input data consist of signals from proximity and accelerometer sensors mounted on the rotor shaft. These signals are processed using time- and frequency-domain feature extraction methods combined with an autoencoder neural network. Validation was performed with four datasets: synthetically generated signals, real measurements from a rotating machine at the Federal University of Uberlandia, and benchmark datasets from Case Western Reserve University (CWRU) and Hanoi University of Science and Technology (HUST) bearing dataset. Evaluation metrics, including accuracy and F1 score, consistently reached values close to 0.96 across most tests. These results confirm the algorithm’s ability to reliably detect and classify machine condition variations in real time. Furthermore, results obtained with benchmark datasets were compared to supervised learning studies, showing comparable or superior performance. This demonstrates the proposed method’s robustness and practicality in industrial applications, where labeled data are scarce and real-time adaptability is critical. Overall, the algorithm provides an effective solution for unsupervised monitoring of rotating machinery, reducing reliance on expert intervention while maintaining high detection accuracy.