  Sensor data analysis plays a key role in health assessment of critical
equipment. Such data are multivariate and exhibit nonlinear relationships. This
paper describes how one can exploit nonlinear dimension reduction techniques,
such as the t-distributed stochastic neighbor embedding (t-SNE) and kernel
principal component analysis (KPCA) for fault detection. We show that using
anomaly detection with low dimensional representations provides better
interpretability and is conducive to edge processing in IoT applications.
