A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance

The area of predictive maintenance has taken a lot of prominence in the last couple of years due to various reasons. With new algorithms and methodologies growing across different learning methods, it has remained a challenge for industries to adopt which method is fit, robust and provide most accurate detection. Fault detection is one of the critical components of predictive maintenance; it is very much needed for industries to detect faults early and accurately. In a production environment, to minimize the cost of maintenance, sometimes it is required to build a model with minimal or no historical data. In such cases, unsupervised learning would be a better option model building. In this paper, we have chosen a simple vibration data collected from an exhaust fan, and have fit different unsupervised learning algorithms such as PCA T² statistic, Hierarchical clustering, K-Means, Fuzzy C-Means clustering and model-based clustering to test its accuracy, performance, and robustness. In the end, we have proposed a methodology to benchmark different algorithms and choosing the final model.