Xiang Li

Xiang Li
Xi'an Jiaotong University | XJTU · School of Mechanical Engineering

Homepage: https://gr.xjtu.edu.cn/web/lixiang/english

About

89
Publications
28,310
Reads
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3,947
Citations
Introduction
Deep learning, industrial AI, industrial big data, fault diagnosis, PHM, optimization etc.
Additional affiliations
May 2019 - present
University of Cincinnati
Position
  • PostDoc Position
May 2017 - April 2019
Northeastern University (Shenyang, China)
Position
  • Professor (Assistant)
September 2015 - September 2016
University of California, Merced
Position
  • Visiting Scholar
Education
September 2012 - January 2017
Tianjin University
Field of study
  • Mechanics
September 2008 - July 2012
Tianjin University
Field of study
  • Engineering Mechanics

Publications

Publications (89)
Article
Based on the features extracted from the condition monitoring data, data-driven prognostic approaches are able to predict the remaining useful life (RUL) of machinery. Existing methods usually assume that a certain feature contributes consistently to the prediction results during the operation. In fact, the degradation sensitivity of each feature v...
Article
Full-text available
The CO2 emission issue has triggered the promotion of carbon capture and storage (CCS), particularly bio-route CCS as a sustainable procedure to capture CO2 using biomass-based activated carbon (BAC). The well-known multi-nitrogen functional groups and microstructure features of N-doped BAC adsorbents can synergistically promote CO2 physisorption....
Article
Full-text available
Athlete balance control ability plays an important role in different types of sports. Accurate and efficient evaluations of the balance control abilities can significantly improve the athlete management performance. With the rapid development of the athlete training field, intelligent and automatic evaluations have been highly demanded in the past...
Article
Rotor system with bolted joints is widely used in aero-engine, and has the features of large axial span, high-degrees of freedom, and nonlinearity. There exist several researches on similitude of rotor systems, but these studies mainly focus on the simple and linear systems. The similitude investigation for the rotor system considering bolted joint...
Article
Full-text available
Cross-domain fault diagnosis methods have been successfully and widely developed in the past years, which focus on practical industrial scenarios with training and testing data from numerous machinery working regimes. Due to the remarkable effectiveness in such problems, deep learning-based domain adaptation approaches have been attracting increasi...
Article
Due to the successful implementation of intelligent data-driven approaches, these methods are gaining remarkable attention in predicting the remaining useful life (RUL) problems. Within this scope, transfer learning approaches are exploited to transfer the obtained knowledge from the source domain data to the target domain data. Due to the differen...
Article
In recent years, intelligent data-driven prognostic methods have been successfully developed, and good machinery health assessment performance has been achieved through explorations of data from multiple sensors. However, existing datafusion prognostic approaches generally rely on the data availability of all sensors, and are vulnerable to potentia...
Article
Despite the rapid development of deep learning-based intelligent fault diagnosis methods on rotating machinery, the data-driven approach generally remains a "black box" to researchers, and its internal mechanism has not been sufficiently understood. The weak interpretability significantly impedes further development and applications of the effectiv...
Article
Full-text available
In the past decades, data-driven methods for the machinery fault diagnosis problem have been developed successfully, especially for the tasks where the training data and the testing data are from the same distribution. In the real industrial scenarios, because of the diversity of the practical factors, the training data and the testing data are gen...
Article
Full-text available
The prediction of average Material Removal Rate (MRR) in Chemical Mechanical Planarization (CMP) process is regarded as a crucial research objective of Virtual Metrology (VM) for semiconductor manufacturing. In this paper, a novel VM model is proposed to predict MRR in CMP process based on the integration of Gaussian Process Regression (GPR) with a...
Article
Full-text available
In current research works, a number of intelligent fault diagnosis methods have been proposed with the assistance of domain adaptation approach, which attempt to distinguish the health modes for target domain data using the diagnostic knowledge learned from source domain data. An important assumption for these methods is that the label information...
Article
Federated learning has been receiving increasing attention in the recent years, which improves model performance with data privacy among different clients. The intelligent fault diagnostic problems can be largely benefited from this emerging technology since the private data generally cannot leave local storage in the real industries. While promisi...
Conference Paper
In the wake of COVID-19, significant influence on the manufacturing industries has been observed in the past year due to the restrictions of in-person communications and interactions. As a consequence, manufacturing efficiency has reduced remarkably all over the world. Despite the great harm to the industrial operations under the pandemic, the oppo...
Article
Due to the benefits of reduced maintenance cost and increased operational safety, effective prognostic methods have always been highly demanded in real industries. In the recent years, intelligent data-driven remaining useful life (RUL) prediction approaches have been successfully developed and achieved promising performance. However, the existing...
Article
Intelligent data-driven machinery fault diagnosis methods have been popularly developed in the past years. While fairly high diagnosis accuracies have been obtained, large amounts of labeled training data are mostly required, which are difficult to collect in practice. The promising collaborative model training solution with multiple users poses hi...
Article
In the past years, the cross-domain machinery fault diagnosis problems have been attracting growing attention, where training and testing data are from different operating conditions. The recent advances in closed set domain adaptation have well addressed the basic problem where the fault mode sets are identical in the source and target domains. Wh...
Article
Wind speed prediction is an important research topic in the wind industry and many algorithms have been proposed to fulfill the prediction tasks. By reviewing the existing methods, one can find that the supplemental information, such as acceleration and turbulence intensity, that can be indirectly derived from wind speed is still less considered in...
Article
Intelligent data-driven machinery fault diagnosis methods have been successfully and popularly developed in the past years. While promising diagnostic performance has been achieved, the existing methods generally require large amounts of high-quality supervised data for training, which are mostly difficult and expensive to collect in real industrie...
Article
Intelligent data-driven system prognostic methods have been popularly developed in the recent years. Despite the promising results, most approaches assume the training and testing data are from the same operating condition. In the real industries, it is quite common that different machine entities work under different scenarios, that results in per...
Article
Data-driven machinery fault diagnosis methods have been successfully developed. However, the cross-domain diagnostic problems have not been well addressed, where the training and testing data are collected under different operating conditions. Recently, domain adaptation approaches have been popularly used to bridge this gap. Despite the effectiven...
Article
Limited condition monitoring data are recorded with label information in practice, which make the fault identification task more challenging. A semi-supervised learning (SSL) approach can be employed to increase the identification performance of the classifiers under such situation. In this study, a three-stage SSL approach using data augmentation...
Article
Intelligent data-driven fault diagnostics for rotating machinery is well established. However, ball screws pose a unique challenge of impractical sensor locations for long-term deployment due to their complex motion trajectory and sophisticated mechanical structure. To overcome this challenge, an indirect sensing method is proposed. While technique...
Article
Full-text available
As a promising modern technology, additive manufacturing (AM) has been receiving increasing research and industrial attention in the recent years. With its rapid development, the importance of quality monitoring in AM process has been recognized, which significantly affects the property of the manufactured parts. Since the conventional hand-crafted...
Article
Full-text available
Recently, the development of intelligent data-driven machinery fault diagnosis methods have received significant attention. In most studies, the training and testing data are assumed to be collected from the same sensor. However, in real practice, due to the mounting limitation and sensor malfunctioning, it cannot be generally guaranteed to obtain...
Article
Full-text available
With the rapid development of artificial intelligence, the data-driven methods have been extensively applied in the fields of machinery, power, civil engineering, transportation and other industries, such as fault diagnosis and prognosis. Whereas the data-driven based accurate dynamic modeling, system identification, and their applications in the d...
Preprint
Feature design and selection is challenging because of huge data volume and high-mix production systems. Most engineers still rely on human experts to suggest the specific sensor channel and specific time frames of data from which to design the features. This study proposes a novel approach for important sensor screening to prioritize the useful se...
Article
Full-text available
Prognostics and Health Management (PHM) is attracting the attention from both academia and industry due to its great potential to enhance the resilience and responsiveness of the equipment to the potential operation risks. In literature, many methodologies are proposed to predict the Remaining Useful Life (RUL) of the equipment. However, there are...
Article
Industrial robots are widely used in modern factories. Robot faults lead to the inevitable suspension of production lines. The prediction of robot failure can improve production capacity. However, it is challenging due to the variations of robots in dynamic working regimes. This paper presents a methodology of fault prognosis of industrial robots,...
Article
Despite the recent advances on intelligent data-driven machinery fault diagnostics, large amounts of high-quality supervised data are mostly required for model training. However, it is usually difficult and expensive to collect sufficient labeled data in real industries, and the difficulty in data preparation significantly hinders the application o...
Article
Intelligent data-driven fault diagnosis methods have been successfully developed in the recent years. However, as one of the most important machines in the industries, the ball screw health monitoring problem has received less attention, due to the complex operating patterns and sophisticated mechanical structures. In practice, the working conditio...
Article
Full-text available
Accurate battery capacity prediction is important to ensure reliable battery operation and reduce the cost. However, the complex nature of battery degradation and the presence of capacity regeneration phenomenon render the prediction task very challenging. To address this problem, this paper proposes a novel and efficient algorithm to predict the b...
Article
The prediction of the average Material Removal Rate (MRR) for Chemical Mechanical Planarization (CMP) process has been recognized to be a critical factor of Virtual Metrology (VM) modeling as well as wafer-to-wafer process control. In this paper, a novel method is proposed to dynamically predict MRR in CMP process by using a Just-in-time (JIT) mode...
Article
The prediction of the average material removal rate (MRR) in chemical mechanical planarization (CMP) process has been recognized to be a critical factor of virtual metrology (VM) modeling for advanced process control (APC). This paper proposes a Gaussian process regression (GPR)-based model to dynamically predict MRR in CMP process. The proposed me...
Article
Deep learning has been widely used nowadays to achieve an automated fault diagnosis of rolling bearings. However, most of deep learning based bearing fault diagnosis methods are based on the assumption that the recorded samples are labeled data, though most of field data are recorded without label information. To address this issue, an effective se...
Conference Paper
Full-text available
Additive manufacturing (AM) techniques have been successfully developed in the past years with the great potential of overcoming the existing obstacles in traditional manufacturing. In order to improve the quality of the manufactured parts and reduce costs, it is important to timely and accurately monitor the AM process during manufacturing. Howeve...
Article
As vast amounts of data are saved, hard drive failure prediction is critical to reducing the cost of data loss and backup. Most existing studies used to detect the anomalous status of a hard drive using self-monitoring, analysis, and reporting technology (SMART) attributes, and then predicted whether the drive would have an impending failure. Howev...
Article
Full-text available
This study proposes a novel 1D deep convolutional transfer learning method that is able to learn the high-dimensional domain-invariant feature from the labeled training dataset and perform diagnosis tasks on the unlabeled testing dataset subjected to a domain shift. To obtain the domain-invariant features, the cross-entropy loss in the source domai...
Article
Prognostics for lithium-ion batteries is very critical in many industrial applications, and accurate prediction of battery state of health (SOH) is of great importance for health management. This paper proposes a novel deep learning-based prognostic method for lithium-ion batteries with on-line validation. An effective variant of recurrent neural n...
Article
Recently, transfer learning has been receiving growing interests in machinery fault diagnosis due to its strong generalization across different industrial scenarios. The existing methods generally assume identical label spaces, and propose minimizing marginal distribution discrepancy between source and target domains. However, this assumption usual...
Conference Paper
Full-text available
Intelligent data-driven machinery health identification has been attracting increasing attention in the manufacturing industries, due to reduced maintenance cost and enhanced operation safety. Despite the successful development, the main limitation of most existing methods lies in the assumption that the training and testing data are collected from...
Conference Paper
Full-text available
In Liquid Crystal Display (LCD) panel cutting, in-line monitoring of tool wear is important to maintain the dimension precision of finished products and to avoid possible damages to the workpiece. However, limited by the space for camera installation and the miniature structure of the tool itself, monitoring the wear of LCD panel cutting wheel is n...
Article
Transient identification of condition monitoring data in nuclear reactor is important for system health assessment. Conventionally, the operating transients are correlated with the pre-designed ones by human operators during operations. However, due to necessary conservatism and significant differences between the operating and pre-designed transie...
Article
Full-text available
Quality inspection in semiconductor manufacturing is of great importance in the modern industries. In the recent years, intelligent data-driven condition monitoring methods have been successfully developed and applied in the industrial applications. However, despite the promising condition monitoring performance, the existing methods generally assu...
Article
This article presents a multi-objective optimal design method for a squeeze film damper (SFD) with centring spring. The proposed method is able to obtain the optimal design parameters for a flexible rotor system. The objectives are minimizing the amplitude of the disk and transmitted force, and maximizing the SFD effectiveness simultaneously. These...
Article
Effective and reliable machinery health assessment and prognostic methods have been highly demanded in modern industries. In the past years, promising prognostic results have been achieved by the intelligent data-driven approaches. However, the existing methods generally rely on the availability of the complete system information. In the real indus...
Article
The past years have witnessed the successful development of intelligent machinery fault diagnostic methods. Besides the basic data-driven fault diagnosis tasks where the training and testing data are collected from the same distribution, the more practical cross-domain problems have also attracted much attention considering variations of machine op...
Article
In the past years, deep learning-based machinery fault diagnosis methods have been successfully developed, and the basic diagnostic problems have been well addressed where the training and testing data are collected under the same operating conditions. When the training and testing data are from different distributions, domain adaptation approaches...
Article
Recently, intelligent data-driven machinery prognostics and health management have been attracting increasing attention due to the great merits of high accuracy, fast response and easy implementation. While promising prognostic performance has been achieved, the first predicting time for remaining useful life is generally difficult to be determined...
Article
Despite the recent success in data-driven machinery fault diagnosis, cross-domain diagnostic tasks still remain challenging where the supervised training data and unsupervised testing data are collected under different operating conditions. In order to address the domain shift problem, minimizing the marginal domain distribution discrepancy is cons...
Article
Full-text available
Intelligent machinery fault diagnosis system has been receiving increasing attention recently due to the potential large benefits of maintenance cost reduction, enhanced operation safety and reliability. This paper proposes a novel deep learning method for rotating machinery fault diagnosis. Since accurately labeled data are usually difficult to ob...
Article
Full-text available
In the recent years, the intelligent data-driven fault diagnosis methods on the rotating machines have been popularly developed. Especially, deep learning algorithms have been adopted in several studies and promising results have been obtained. However, the cross-domain fault diagnostic problem still remains a challenging issue, where the training...
Article
Full-text available
With the advancement of intelligent manufacturing, different kinds of industrial robots have been applied in modern factories. The liquid crystal display transfer robot (LCDTR) has been widely used in LCD production lines to transport panels. Effective fault diagnosis and prognosis of the industrial robots are of great importance, since unplanned d...
Article
Full-text available
The cutting wheel is an important tool in the television liquid crystal display (LCD) panel manufacturing process. The degradation of the cutting wheel significantly affects the LCD panel quality. Currently, there is few effective approaches that can detect the degradation of the cutting wheel at the working station for health monitoring purpose, d...
Article
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating machines, balanced training data for different machine health conditions are assumed in most studies. However, the signals in machine faulty states are usually difficult and expensive to collect, resulting in imbalanced training dataset in most cases. That si...
Article
In the recent years, data-driven machinery fault diagnostic methods have been successfully developed, and the tasks where the training and testing data are from the same distribution have been well addressed. However, due to sensor malfunctions, the training and testing data can be collected at different places of machines, resulting in the feature...
Article
Rotating machinery fault diagnosis problems have been well addressed when sufficient supervised data of the tested machine are available using the latest data-driven methods. However, it is still challenging to develop effective diagnostic method with insufficient training data, which is highly demanded in real industrial scenarios since high-quali...
Article
Increasing traffic volumes result in congestion especially at intersections in urban areas. Effective regulation of vehicle flows at intersections has always been an important issue in the traffic control system. This paper presents a multi-objective optimization method for improving traffic performance at intersections. Vehicle conflicts and pedes...
Article
In the recent years, deep learning-based intelligent fault diagnosis methods of rolling bearings have been widely and successfully developed. However, the data-driven method generally remains a “black box” to researchers and there is a gap between the emerging neural network-based methods and the well-established traditional fault diagnosis knowled...
Article
Traffic congestion in urban network has been a serious problem for decades. In this paper, a novel dynamic multi-objective optimization method for designing predictive controls of network signals is proposed. The popular cell transmission model (CTM) is used for traffic prediction. Two network models are considered, i.e., simple network which captu...