Detecção de falhas em operação de bombas elétricas utilizando TinyML e sinais acústicos

Abstract

Most production processes begin with the generation of kinetic energy by electric motors. In fluid-related operations, electric pumps are responsible for the movement and pressure variation of fluids. Given the importance of this equipment, failures in their operation can negatively impact the entire production chain, causing reduced efficiency, unforeseen stoppages, and potential safety risks for workers. To mitigate these issues, industrial asset monitoring strategies are implemented, assessing factors such as the condition and performance of pumps, aiding in corrective and preventive maintenance decisions. Traditionally, vibration and current sensors are used to monitor the condition of this equipment, but due to cost and the need for physical contact, their application may be unfeasible in some cases. Acoustic sensors are a low-impact alternative, capable of detecting anomalies at early stages without physical contact, offering an efficient and non-intrusive monitoring solution. For detecting and diagnosing defects in electric pumps, machine learning techniques, especially neural networks, are widely used. This study investigates the feasibility of using miniaturized machine learning, known as TinyML, in combination with acoustic sensors for detecting defects in electric pumps. Specifically, an audio classification model based on convolutional neural networks (CNN) was developed, implemented, and embedded on the Arduino Nano platform. To validate the proposal, the "MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection" was used, containing audio recordings of defects from different pump models. To ensure the applicability of the embedded system, the relationship between neural network model complexity and its accuracy in detecting anomalies was analyzed, using quantization techniques to reduce the CNN by approximately 50%. Enabling the creation, training, and deployment of machine learning models on edge devices, the Edge Impulse platform was used for developing the fault detection model using acoustic sensors and its compression into a reduced model optimized for the Arduino. The results indicate an accuracy of over 96% in defect detection, providing a low-cost, highly efficient, and easy-to-implement solution with potential applications in various industrial areas, significantly improving asset monitoring and reducing maintenance-related costs.