Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems

TL;DR

This paper introduces an online, adaptive anomaly detection method for predictive maintenance in manufacturing, inspired by neuroscience, which outperforms existing deep learning and statistical techniques in failure detection.

Contribution

The paper presents a novel real-time anomaly detection approach using Hierarchical Temporal Memory, inspired by the human neocortex, that adapts continuously and is robust to noise.

Findings

Outperforms state-of-the-art algorithms in failure detection

Achieves an average score of 64.71 on benchmark tests

Demonstrates robustness to noise and adaptability over time

Abstract

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.