结合先进的异常检测和图同构网络进行剩余使用寿命预测

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Journal Pub Date : 2024-10-04 DOI:10.1109/JSEN.2024.3470231
Junyu Qi;Zhuyun Chen;Yuchen Song;Jingyan Xia;Weihua Li
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引用次数: 0

摘要

在工业 4.0 和数字化制造时代,状态监测(CM)受到广泛关注。监测机器的健康状况对于确保可靠性、安全性、生产质量和效率至关重要。先进的预测性维护策略可以通过准确预测故障来提高设备的可用性和有效性,从而促进维护工程决策并防止意外的机械故障。本研究提出了一种新颖的预测性维护策略,将异常检测和故障预报这两个智能维护领域的重大挑战整合到一个 CM 系统中。在异常检测方面,我们开发了一种基于跳转卷积生成对抗网络(SCGAN)的智能方法。该网络结合了卷积自动编码器 (CAE)、生成对抗网络 (GAN) 和跳转连接,形成了一个构建健康指标 (HI) 的稳健系统,可有效追踪滚动轴承的退化状态,并使用 $3\sigma $ 准则识别故障。在实际实验数据集上的验证表明,所开发的健康指标在健康阶段表现出稳定的趋势,而在检测到退化时则明显增加。随后,我们采用先进的图同构网络(GIN)进行剩余使用寿命(RUL)预测。GIN 利用图数据和图卷积 (GC) 来映射退化演变和剩余使用寿命之间的复杂关系。这种方法优于现有的深度学习模型,如卷积神经网络(CNN)、长短期记忆(LSTM)网络和 CNN-LSTM,可提供更准确的 RUL 预测。
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Remaining Useful Life Prediction Combining Advanced Anomaly Detection and Graph Isomorphic Network
Condition monitoring (CM) has garnered extensive attention in the era of Industry 4.0 and digital manufacturing. It is crucial to monitor the health status of machinery to ensure reliability, safety, production quality, and effectiveness. Advanced predictive maintenance strategies increase equipment availability and effectiveness by accurately predicting failures, thus facilitating maintenance engineering decisions and preventing unplanned machinery breakdowns. In this research, a novel predictive maintenance strategy is proposed by integrating anomaly detection and fault prognostics, two significant challenges in smart maintenance, into one CM system. For anomaly detection, we developed an intelligent methodology based on the skip convolution generative adversarial network (SCGAN). This network combines a convolutional autoencoder (CAE), generative adversarial network (GAN), and skip connections, forming a robust system to construct health indicators (HIs), effectively and efficiently tracking the degradation status of rolling element bearings with fault identified using the $3\sigma $ criterion. Validation on real experimental datasets demonstrates that the developed HIs show a stable trend during the healthy stage and a marked increase when deterioration is detected. Subsequently, we employ an advanced graph isomorphic network (GIN) for remaining useful life (RUL) prediction. GIN utilizes graph data and graph convolutions (GCs) to map complex relationships between degradation evolution and RUL. This approach outperforms existing deep learning models, such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and CNN-LSTM, providing more accurate RUL prediction.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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