基于感应电机异常检测的预测性维护模型：一种使用实时IoT数据的机器学习方法
Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data
AI总结：
本文提出一种基于实时IoT数据的感应电机异常检测与预测性维护方法，结合FFT、WT等预处理技术与低计算成本机器学习模型，通过多目标优化平衡检测率、误报率与推理速度，并利用低成本传感器采集的振动、温度和噪声融合数据验证了该端到端方案的有效性。
摘要：
在物联网设备支持下，可以获取退化现象的数据，并设计数据驱动模型来执行工业设备的异常检测。这种方法不仅能识别潜在异常，还可以作为构建预测性维护策略的第一步。在本研究中，我们在用于泵、压缩机、风机及其他工业机器的感应电机上展示了一种新颖的异常检测系统。本研究评估了预处理技术与低计算成本的机器学习模型的组合。我们使用了快速傅里叶变换、小波变换和分箱等预处理技术的组合，这些是从原始数据中提取特征的知名方法。我们还旨在保证多个相互冲突的参数之间的最佳平衡，如异常检测率、误报率和解决方案的推理速度。为此，对评估的模型进行了多目标优化和分析。我们提出了Pareto最优解，以选择哪些模型在分类指标和计算工作量方面具有最佳结果。与该领域大多数使用公开可用数据集验证其模型的工作不同，我们提出了一种结合低成本且易获取的IoT传感器的端到端解决方案。该方法通过从感应电机获取自定义数据集来进行验证。此外，我们将这些传感器的振动、温度和噪声数据融合，作为所提出的机器学习模型的输入。因此，我们旨在提出一种足够通用的方法，以便未来应用于不同的工业环境。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
