Department of Mechatronics and Mechanical Systems Engineering, Polytechnic School, University of São Paulo, São Paulo 05508-010, SP, Brazil.
Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo 01525-000, SP, Brazil.
Sensors (Basel). 2022 Dec 12;22(24):9738. doi: 10.3390/s22249738.
This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced proportion between faulty and non-faulty data in an industrial context to improve systems' safety and reliability. In contrast to supervised methods, the approach requires less manual data labeling and can find previously unknown patterns in data. The technique focuses on detecting and isolating possible measurement divergences and tracking their growth to signalize a fault's occurrence while individually evaluating each monitored variable to provide fault detection and prognosis. Additionally, the paper also provides an appropriate set of metrics to measure the accuracy of the models, which is a common disadvantage of unsupervised methods due to the lack of predefined answers during training. Computational results using the Commercial Modular Aero Propulsion System Simulation (CMAPSS) monitoring data show the effectiveness of the proposed framework.
本文提出了一种使用基于自动编码器的深度学习方法进行故障预测的通用框架。所提出的方法依赖于自动编码器重构误差的半监督外推，可用于处理工业环境中故障数据和非故障数据之间的不平衡比例，以提高系统的安全性和可靠性。与监督方法相比，该方法需要较少的手动数据标记，并且可以在数据中找到以前未知的模式。该技术侧重于检测和隔离可能的测量偏差，并跟踪其增长，以发出故障发生的信号，同时单独评估每个被监控的变量，以提供故障检测和预测。此外，本文还提供了一组适当的指标来衡量模型的准确性，这是由于在训练过程中缺乏预定义的答案，因此无监督方法的常见缺点。使用商用模块化航空推进系统仿真（CMAPSS）监测数据的计算结果表明了所提出框架的有效性。
