论文标题
组件级预后和健康管理（PHM）的整体故障检测和诊断系统（稀缺，多域（ISMD）数据设置）（PHM）
Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)
论文作者
论文摘要
在当前的工业4.0革命中，预后和健康管理（PHM）是一个新兴的研究领域。从工业环境中获取机电系统数据的困难与自动化行业的规模和可访问性成比例地增加，导致插值较少的PHM系统。换句话说，每个工业系统的准确PHM系统的开发都需要在指定条件下获得的独特数据集。在大多数情况下，很难获得此独一无二的数据集，并且所得数据集具有重大失衡，缺乏某些有用的信息和多域知识。为了解决这个问题，本文提供了一个故障检测和诊断系统，该系统评估和预处理不平衡，稀缺，多域（ISMD）数据是从使用信号处理（SP）技术和基于深度学习（DL）领域知识传递的工业机器人中获取的。域知识转移用于生产具有高插值率的合成数据集，其中包含有关每个域的所有有用信息。对于域知识传输和数据生成，使用了具有生成对抗网络（GAN）的连续小波变换（CWT），以及卷积神经网络（CNN），使用转移学习并对几个故障进行分类来测试建议的方法。提出的方法是在包括现代机器人公司创建的工业机器人的真实实验台上测试的。这种开发导致令人满意的分辨率，并通过在多种CNN基准模型上传输学习来获得99.7％（最高）分类的精度。
In the current Industrial 4.0 revolution, Prognostics and Health Management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the scale and accessibility of the automated industry, resulting in a less interpolated PHM system. To put it another way, the development of an accurate PHM system for each industrial system necessitates a unique dataset acquired under specified conditions. In most circumstances, obtaining this one-of-a-kind dataset is difficult, and the resulting dataset has a significant imbalance, a lack of certain useful information, and multi-domain knowledge. To address this, this paper provides a fault detection and diagnosis system that evaluates and pre-processes Imbalanced, Scarce, Multi-Domain (ISMD) data acquired from an industrial robot utilizing Signal Processing (SP) techniques and Deep Learning-based (DL) domain knowledge transfer. The domain knowledge transfer is used to produce a synthetic dataset with a high interpolation rate that contains all the useful information about each domain. For domain knowledge transfer and data generation, Continuous Wavelet Transform (CWT) with Generative Adversarial Network (GAN) was used, as well as Convolutional Neural Network (CNN) to test the suggested methodology using transfer learning and categorize several faults. The proposed methodology was tested on a real experimental bench that included an industrial robot created by Hyundai Robotics Co. This development resulted in a satisfactory resolution with 99.7% (highest) classification accuracy achieved by transfer learning on several CNN benchmark models.
