Predictive Maintenance Model Based on Anomaly Detection in Induction
  Motors: A Machine Learning Approach Using Real-Time IoT Data
Sergio F. Chevtchenko Monalisa C. M. dos Santos Diego M. Vieira Ricardo L. Mota Elisson Rocha Bruna V. Cruz Danilo Araújo Ermeson Andrade
With the support of Internet of Things (IoT) devices, it is possible to
acquire data from degradation phenomena and design data-driven models to
perform anomaly detection in industrial equipment. This approach not only
identifies potential anomalies but can also serve as a first step toward
building predictive maintenance policies. In this work, we demonstrate a novel
anomaly detection system on induction motors used in pumps, compressors, fans,
and other industrial machines. This work evaluates a combination of
pre-processing techniques and machine learning (ML) models with a low
computational cost. We use a combination of pre-processing techniques such as
Fast Fourier Transform (FFT), Wavelet Transform (WT), and binning, which are
well-known approaches for extracting features from raw data. We also aim to
guarantee an optimal balance between multiple conflicting parameters, such as
anomaly detection rate, false positive rate, and inference speed of the
solution. To this end, multiobjective optimization and analysis are performed
on the evaluated models. Pareto-optimal solutions are presented to select which
models have the best results regarding classification metrics and computational
effort. Differently from most works in this field that use publicly available
datasets to validate their models, we propose an end-to-end solution combining
low-cost and readily available IoT sensors. The approach is validated by
acquiring a custom dataset from induction motors. Also, we fuse vibration,
temperature, and noise data from these sensors as the input to the proposed ML
model. Therefore, we aim to propose a methodology general enough to be applied
in different industrial contexts in the future.