  The modern industrial environment is equipping myriads of smart manufacturing
machines where the state of each device can be monitored continuously. Such
monitoring can help identify possible future failures and develop a
cost-effective maintenance plan. However, it is a daunting task to perform
early detection with low false positives and negatives from the huge volume of
collected data. This requires developing a holistic machine learning framework
to address the issues in condition monitoring of high-priority components and
develop efficient techniques to detect anomalies that can detect and possibly
localize the faulty components. This paper presents a comparative analysis of
recent machine learning approaches for robust, cost-effective anomaly detection
in cyber-physical systems. While detection has been extensively studied, very
few researchers have analyzed the localization of the anomalies. We show that
supervised learning outperforms unsupervised algorithms. For supervised cases,
we achieve near-perfect accuracy of 98 percent (specifically for tree-based
algorithms). In contrast, the best-case accuracy in the unsupervised cases was
63 percent :the area under the receiver operating characteristic curve (AUC)
exhibits similar outcomes as an additional metric.
