An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis
Zhejiang University（浙江大学）；University of Illinois Urbana–Champaign Institute（伊利诺伊大学厄巴纳-香槟分校学院）；University of Electronic Science and Technology of China（电子科技大学）
Fault detection and diagnosis is significant for reducing maintenance costs and improving health and safety in chemical processes. Convolution neural network (CNN) is a popular deep learning algorithm with many successful applications in chemical fault detection and diagnosis tasks. However, convolution layers in CNN are very sensitive to the order of features, which can lead to instability in the processing of tabular data. Optimal order of features result in better performance of CNN models but it is expensive to seek such optimal order. In addition, because of the encapsulation mechanism of feature extraction, most CNN models are opaque and have poor interpretability, thus failing to identify root-cause features without human supervision. These difficulties inevitably limit the performance and credibility of CNN methods. In this paper, we propose an order-invariant and interpretable hierarchical dilated convolution neural network (HDLCNN), which is composed by feature clustering, dilated convolution and the shapley additive explanations (SHAP) method. The novelty of HDLCNN lies in its capability of processing tabular data with features of arbitrary order without seeking the optimal order, due to the ability to agglomerate correlated features of feature clustering and the large receptive field of dilated convolution. Then, the proposed method provides interpretability by including the SHAP values to quantify feature contribution. Therefore, the root-cause features can be identified as the features with the highest contribution. Computational experiments are conducted on the Tennessee Eastman chemical process benchmark dataset. Compared with the other methods, the proposed HDLCNN-SHAP method achieves better performance on processing tabular data with features of arbitrary order, detecting faults, and identifying the root-cause features.
An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications
Center for Risk and Reliability, University of Maryland, College Park（马里兰大学帕克分校风险与可靠性中心）；Department of Civil and Environmental Engineering, University of California, Los Angeles（加利福尼亚大学洛杉矶分校土木与环境工程系）；Garrick Institute for the Risk Sciences, University of California, Los Angeles（加利福尼亚大学洛杉矶分校加里克风险科学研究所）；Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University（香港理工大学工业与系统工程系）；Centre for Advances in Reliability and Safety Limited (CAiRS)（可靠性及安全进展中心有限公司）
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
Zhejiang University（浙江大学）；University of Illinois Urbana–Champaign Institute（伊利诺伊大学厄巴纳-香槟分校学院）；Southeast University（东南大学）；Huazhong University of Science and Technology（华中科技大学）
Fault Diagnosis of Helical Gear Box using Large Margin K-Nearest Neighbors Classifier using Sound Signals
Indian Institute of Information Technology Design and Manufacturing Jabalpur（印度信息技术设计与制造学院）；Birla Institute of Technology and Science – Pilani（比尔拉理工学院）；National Institute of Technology Karnataka（卡纳塔克国立理工学院）；VIT University（韦洛尔理工大学）
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）；University of Ottawa（渥太华大学）；Sant Longowal Institute of Engineering and Technology（桑特隆戈瓦尔工程技术学院）
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery
School of Mechanical Engineering, Federal University of Uberlândia（Uberlândia联邦大学机械工程学院）；University of Padova（帕多瓦大学）；Federal University of São João del Rei（圣若昂德尔雷伊联邦大学）
Fault Diagnosis and Quantification for Photovoltaic Arrays based on Differentiable Physical Models
College of Mechanical and Electrical Engineering, Hohai University（河海大学机械与电子工程学院）；Concordia Institute for Information Systems Engineering, Concordia University（康科德大学信息系统工程研究所）；AAU Energy, Aalborg University（奥胡斯大学能源研究所）
Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks
Artificial Intelligence Research Institute, China University of Mining and Technology（中国矿业大学人工智能研究院）；School of Information and Control Engineering, China University of Mining and Technology（中国矿业大学信息与控制工程学院）；National Engineering Research Center of Coal Preparation and Purification, China University of Mining and Technology（中国矿业大学选煤国家工程研究中心）
National Space Science Center.CAS（中国科学院国家空间科学中心）；University of Chinese Academy of Sciences（中国科学院大学）；Science and Technology on Complex Aviation System Simulation Laboratory（复杂航空系统仿真科学与技术实验室）
数据驱动方法在故障诊断中取得了很大的进展，尤其是深度学习方法。深度学习适用于处理大数据，具有很强的特征提取能力，可以实现端到端的故障诊断系统。但设计神经网络架构需要丰富的专业知识和调试经验，还需要大量的实验来筛选模型和超参数，增加了深度学习模型的开发难度。近年来，神经结构搜索（Neural Architecture Search，NAS）发展迅速，成为深度学习的下一个发展方向。提出了一种基于强化学习的NAS故障诊断方法。一个递归神经网络被用来作为代理生成网络结构。生成的网络在验证数据集上的准确性作为奖励反馈给代理，并通过策略梯度算法更新代理的参数。利用PHM 2009 Data Challenge齿轮箱数据集验证了该方法的有效性，并与其他人工设计的网络结构进行了比较，取得了较好的效果。据作者所知，这是第一次将NAS应用于故障诊断。
Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data
CentraleSupélec（中央高等电力学院）；Université Paris-Saclay（巴黎-萨克雷大学）；Shenzhen Weimin Institute for Reliability Systems Engineering（深圳伟民可靠性系统工程研究院）；Laboratoie Genie Industriel（工业工艺实验室）
Class Incremental Fault Diagnosis under Limited Fault Data via Supervised Contrastive Knowledge Distillation
ZJU-UIUC Joint Institute at Zhejiang University（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所）；ZJU-UoE Institute at Zhejiang University（浙江大学-爱丁堡大学联合研究所）；College of Biomedical Engineering and Instrument Science at Zhejiang University（浙江大学生物医学工程与仪器科学学院）；College of Computer Science and Technology in Zhejiang University（浙江大学计算机科学与技术学院）
Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules
School of Instrumentation and Optoelectronic Engineering, Beihang University（北京航空航天大学仪器科学与光电工程学院）；School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；Department of Electrical and Computer Engineering, University of Calgary（卡尔加里大学电气与计算机工程系）
