Predictive Maintenance of Induction Motors Using Ultra-Low Power Wireless Sensors and Compressed Recurrent Neural Networks

Abstract

In real-world applications-to minimize the impact of failures-machinery is often monitored by various sensors. Their role comes down to acquiring data and sending it to a more powerful entity, such as an embedded computer or cloud server. There have been attempts to reduce the computational effort related to data processing in order to use edge computing for predictive maintenance. The aim of this paper is to push the boundaries even further by proposing a novel architecture, in which processing is moved to the sensors themselves thanks to decrease of computational complexity given by the usage of compressed recurrent neural networks. A sensor processes data locally, and then wirelessly sends only a single packet with the probability that the machine is working incorrectly. We show that local processing of the data on ultra-low power wireless sensors gives comparable outcomes in terms of accuracy but much better results in terms of energy consumption that transferring of the raw data. The proposed ultra-low power hardware and firmware architecture makes it possible to use sensors powered by harvested energy while maintaining high confidentiality levels of the failure prediction previously offered by more powerful mains-powered computational platforms.

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

“…In a study proposed by Markiewicz [42], predictive maintenance of induction motors is considered. The paper touches upon the issues of data gathering and storage, as it is an energy intensive process.…”

“…The paper touches upon the issues of data gathering and storage, as it is an energy intensive process. Hence, Markiewicz [42] presents a solution where predictive maintenance is done locally on a set of ultra-low power wireless sensors. This is possible with a reduced computational complexity from applying a compressed RNN.…”

“…Markiewicz et al [42] finds LSTM-RNN to benefit from being relative stable, notably because it does not suffer from the vanishing or exploding gradient problem [73,74]. Zhang et al [33] states that LSTM-RNN has good performance on sequential data due to the recurrent feedback.…”

Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City

“…Depending on the frequency range of the vibrations to be measured, position sensors (0-10 kHz), speed sensors (10 Hz-1 kHz) or accelerometers (8 Hz-15 kHz) can be used [2]. However, the use of accelerometers is the most common, as has been seen in the vast majority of the articles consulted [11][12][13][14][15][16][17][18][19]. This type of analysis presents good results, since the most common faults always generate additional vibrations to those of the engine in normal operation.…”

Classificação de Defeitos em Rolamento de Motores Elétricos Utilizando Redes Convolucionais Compactas para Sistemas Embarcados

“…Em (Eren et al 2019) os autores apresentam redes neurais convolucionais compactas capazes de classificar falhas em rolamentos através de sinais de vibração, se utilizando de conjuntos de dados públicos (como o CWRU) para validar o trabalho. Em (Markiewicz et al 2019) os autores apresentam uma solução embarcada completa de baixo consumo para classificação de falhas utilizando redes Long Short-Term Memory.…”