  If machine failures can be detected preemptively, then maintenance and
repairs can be performed more efficiently, reducing production costs. Many
machine learning techniques for performing early failure detection using
vibration data have been proposed; however, these methods are often power and
data-hungry, susceptible to noise, and require large amounts of data
preprocessing. Also, training is usually only performed once before inference,
so they do not learn and adapt as the machine ages. Thus, we propose a method
of performing online, real-time anomaly detection for predictive maintenance
using Hierarchical Temporal Memory (HTM). Inspired by the human neocortex, HTMs
learn and adapt continuously and are robust to noise. Using the Numenta Anomaly
Benchmark, we empirically demonstrate that our approach outperforms
state-of-the-art algorithms at preemptively detecting real-world cases of
bearing failures and simulated 3D printer failures. Our approach achieves an
average score of 64.71, surpassing state-of-the-art deep-learning (49.38) and
statistical (61.06) methods.
