Mari Simone, Bucci Giovanni, Ciancetta Fabrizio, Fiorucci Edoardo, Fioravanti Andrea
Dipartimento di Ingegneria Industriale e dell'Informazione e di Economia, Università dell'Aquila, 67100 L'Aquila, Italy.
Sensors (Basel). 2024 Aug 14;24(16):5263. doi: 10.3390/s24165263.
Classification systems based on machine learning (ML) models, critical in predictive maintenance and fault diagnosis, are subject to an error rate that can pose significant risks, such as unnecessary downtime due to false alarms. Propagating the uncertainty of input data through the model can define confidence bands to determine whether an input is classifiable, preferring to indicate a result of unclassifiability rather than misclassification. This study presents an electrical fault diagnosis system on asynchronous motors using an artificial neural network (ANN) model trained with vibration measurements. It is shown how vibration analysis can be effectively employed to detect and locate motor malfunctions, helping reduce downtime, improve process control and lower maintenance costs. In addition, measurement uncertainty information is introduced to increase the reliability of the diagnosis system, ensuring more accurate and preventive decisions.
基于机器学习（ML）模型的分类系统在预测性维护和故障诊断中至关重要，但存在可能带来重大风险的错误率，例如因误报导致不必要的停机时间。通过模型传播输入数据的不确定性可以定义置信区间，以确定输入是否可分类，更倾向于表明不可分类的结果而不是错误分类。本研究提出了一种基于人工神经网络（ANN）模型的异步电动机电气故障诊断系统，该模型通过振动测量进行训练。展示了振动分析如何能够有效地用于检测和定位电动机故障，有助于减少停机时间、改善过程控制并降低维护成本。此外，引入测量不确定性信息以提高诊断系统的可靠性，确保做出更准确和预防性的决策。
