2 research outputs found
Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data
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
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations
Reinforcement Learning (RL) provides a powerful framework for decision-making
in complex environments. However, implementing RL in hardware-efficient and
bio-inspired ways remains a challenge. This paper presents a novel Spiking
Neural Network (SNN) architecture for solving RL problems with real-valued
observations. The proposed model incorporates multi-layered event-based
clustering, with the addition of Temporal Difference (TD)-error modulation and
eligibility traces, building upon prior work. An ablation study confirms the
significant impact of these components on the proposed model's performance. A
tabular actor-critic algorithm with eligibility traces and a state-of-the-art
Proximal Policy Optimization (PPO) algorithm are used as benchmarks. Our
network consistently outperforms the tabular approach and successfully
discovers stable control policies on classic RL environments: mountain car,
cart-pole, and acrobot. The proposed model offers an appealing trade-off in
terms of computational and hardware implementation requirements. The model does
not require an external memory buffer nor a global error gradient computation,
and synaptic updates occur online, driven by local learning rules and a
broadcasted TD-error signal. Thus, this work contributes to the development of
more hardware-efficient RL solutions