Weiming Shen

National Research Council Canada | NRC · Institute for Research in Construction (IRC)

Federated learning (FL) guaranteeing data privacy is of great interest in decentralized fault diagnosis. However, limited research attention has been paid to the dynamic domain-shift issue due to varying working conditions. This paper proposes an active federated transfer algorithm based on broad learning to address the domain shift issue in FL. Fi...

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational e...

p>This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles (CAVs) at unsignalized intersections. The constraint-coupled bi-level optimization is operated with a limited view of incoming traffic using the rolling horizon...

Smart manufacturing system pursues automated modeling algorithms for industrial applications in dynamic environments. The prevalent deep transfer learning (DTL) has achieved promising results in cross-domain fault diagnosis. However, most DTL algorithms are dataset- and domain-specific. They require hyperparameter optimization (HPO) calling for pri...

Grasping a specified object from multi-object scenes is an essential ability for intelligent robots. This ability depends on the affiliation between the grasp position and the object category. Most existing multi-object grasp detection methods considering the affiliation rely on object detection results, thus limiting the improvement of robotic gra...

Personalized products have gradually become the main business model and core competencies of many enterprises. Large differences in components and short delivery cycles of such products, however, require industrial robots in cloud manufacturing (CMfg) to be smarter, more responsive and more flexible. This means that the deep learning models (DLMs)...

This paper proposes a coevolutionary algorithm to optimize longitudinal trajectories of multiple vehicles with an energy-aware non-linear objective during the cooperative platoon formation process. In this work, an adaptive encoding scheme is adopted to represent trajectories as knot vectors of parametric cubic splines, and therefore the original p...

Deep learning has led to tremendous success in machine maintenance and fault diagnosis. However, this success is predicated on the correctly annotated datasets. Labels in large industrial datasets can be noisy and thus degrade the performance of fault diagnosis models. The emerging concept of broad learning shows the potential to address the label...

Automated detection of anomalous trajectories is an important problem with considerable applications in intelligent transportation systems. Many existing studies have focused on distinguishing anomalous trajectories from normal trajectories, ignoring the large differences between anomalous trajectories. A recent study has made great progress in ide...

Recently, domain generalization techniques have been introduced to enhance the generalization capacity of fault diagnostic models under unknown working conditions. Most existing studies assume consistent machine health states between the training and testing data. However, fault modes in the testing phase are unpredictable, and unknown fault modes...

This paper proposes a cognitive digital twin framework for smart manufacturing, and especially for human-robot-collaboration cases. The proposed framework comprises three layers (field, edge, and cloud layers) based on the 5G communication network. In the field layer, the physical twin's data from the physical machine and human operators are transm...

Abstract Data‐driven fault diagnosis has prevailed in machine condition monitoring in the past decades. However, traditional machine‐ and deep‐learning‐based fault diagnosis methods assumed that the source and target data share the same distribution and ignored knowledge transfer in dynamic working environments. In recent years, knowledge transfer...

Domain adaptation techniques have attracted great attention in mechanical fault diagnosis. However, most existing methods work under the assumption that the source and target domains share the identical label space. Such methods are unable to handle a practical issue where the target label space is a subset of the source label space. To tackle this...

Domain generalization-based fault diagnosis has recently emerged to address domain shift problems. Most existing methods learn domain-invariant representations from multiple source domains. However, valuable fault samples from polytropic working conditions are difficult to be collected, and it is quite common that available data are from a single w...

Mechanical fault diagnosis is crucial to ensure safe operations of equipment in intelligent manufacturing systems. Deep learning-based methods have been recently developed for fault diagnosis due to their advantages in feature representation. However, most of these methods fail to learn relations between samples and thus perform poorly without suff...

Knowledge transfer with class-imbalanced data is a challenge in predictive maintenance and fault diagnosis. Deep learning algorithms have provided promising results in fault diagnosis. However, their prediction performance is affected by class-imbalanced data in cross-domain tasks. Broad learning algorithms present promising performance in handling...

This research focuses on the realization of rapid reconfiguration in a cloud manufacturing environment to enable flexible resource scheduling, fulfill the resource potential and respond to various changes. Therefore, this paper first proposes a new cloud and software-defined networking (SDN)-based manufacturing model named software-defined cloud ma...

Room allocation is a challenging task in detention centers since lots of related people need to be held separately with limited rooms. It is extremely difficult and risky to allocate rooms manually, especially for organized crime groups with close connections. To tackle this problem, we develop an intelligent room allocation system for detention ce...

The fifth-generation (5G) wireless communication networks are expected to play an essential role in the transformation of vertical industries. Among many exciting applications to be enabled by 5G, logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything (V2X) communications. In this paper, a multi-layer collabor...

This paper proposed a surrogate-assisted dominance-based multi-objective evolutionary algorithm to solve multi-objective computationally expensive problems with medium dimensions. Two infill criteria are collaboratively used to select promising individuals for exact evaluations. The convergence-based criterion is used to promote the exploitation of...

This paper addresses a collaborative multi-carrier vehicle routing problem (CMCVRP) where carriers tackle their orders collaboratively to reduce transportation costs. First, a hierarchical heuristics algorithm is proposed to solve the transportation planning problem. This algorithm makes order assignments based on two distance rules and solves the...

Previous studies on Connected and Automated Vehicles (CAVs) demonstrated the potential to coordinate the behaviors of multiple connected vehicles for traffic improvements. In this paper, we first propose a Conflict Duration Graph-based (CDG-based) coordination framework to resolve collisions and improve the traffic capacity of signal-free intersect...

With the Internet of Things, it is now possible to sense the real-time status of manufacturing objects and processes. For complex Service Selection (SS) in Cloud Manufacturing, real-time information can be utilized to deal with uncertainties emerging during task execution. Moreover, in the face of diversified demands, multiple manufacturing clouds...

With the rapid development of 5G communication technology, various applications of this emerging technology are being developed and deployed in industrial parks. Under a major ongoing project, we are developing a multi-layer collaboration framework to coordinate IoT devices and other resources for the efficient operation of smart factories and the...

Industrial trends and new generation information and communication technologies have become driving forces for advancement in the process control and manufacturing industry. This paper thoroughly investigates the future industrial trends from the perspectives of market, engineering system, product, innovation, etc., then incorporates the concept of...

Label Propagation Algorithm (LPA) is a fast community detection algorithm. However, since each node is randomly assigned a different label at first, there is serious randomness in the label updating process of LPA, resulting in great instability of detection results. This paper proposes a modularity-based incremental LPA (MILPA) to address this pro...

While COVID-19 has affected the daily life of almost everyone around the world, it has also caused major disturbances to the global economy and to the operations of many businesses in the manufacturing industry. Reginal lockdowns resulted in supply chain breakages. Workforce shortages caused difficult shop floor operations. This study tries to disc...

In the practice of cloud manufacturing, there still exist some major challenges, including: 1) cloud based big data analytics and decision-making cannot meet the requirements of many latency-sensitive applications on shop floors; 2) existing manufacturing systems lack enough reconfigurability, openness and evolvability to deal with shop-floor distu...

The evaluation of a link prediction algorithm requires to estimate the possibility of the existence of all unobserved links in a network. However, the number of unobserved links grows exponentially with the increase of the number of nodes, which limits link prediction in large networks. In this paper, we propose a new evaluation scheme for link pre...

Anomaly detection is an important issue in trajectory data mining. Various approaches have been proposed to address this issue. However, most previous studies focus only on outlier detection but rarely on pattern mining of anomalous trajectories. Mining patterns of anomalous trajectories can reveal the underlying mechanisms of these outliers. This...

To meet the current development requirements of the new industrial Internet of things and intelligent manufacturing, this paper studies 5G technologies and their application scenarios in intelligent manufacturing. Firstly, 5G key technologies are introduced, including 5G-supported application scenarios, network slicing, NFV/SDN, multi-access edge c...

Fault detection and diagnosis (FDD) is crucial for stable, reliable, and safe operation of industrial equipment. In recent years, deep learning models have been widely used in data-driven FDD methods because of their automatic feature learning capability. In general, these models are trained on historical sensor data, and therefore, it is very diff...

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in so...

Due to ubiquitous Internet connectivity, widely available cloud services, and popular mobile devices, mobile networks have become service delivery and consumption platforms for many industries worldwide. To recommend optimal mobile Web services with trustworthy Quality-of-Service (QoS) and dynamic user preferences, this paper proposes a novel servi...

High-end equipment oriented maintenance, repair and operation (MRO) management is crucial for asset intensive industries. The existing works mainly focus on providing the best possible joint optimisation for production and maintenance management without aiming at the complicated relationships among them. In the intelligence-connected era, the rapid...

Link prediction refers to predicting the likelihood of the existence of an unknown link or a future link based on the observed information. It plays an important role in complex network analysis. Classical similarity indices based on common neighbor nodes consider that each common neighbor has the same effect to the link likelihood. However, in rea...

During the machining process of thin-walled parts, machine tool wear and work-piece deformation always co-exist, which make the recognition of machining conditions very difficult. Existing machining condition monitoring approaches usually consider only one single condition, i.e., either tool wear or work-piece deformation. In order to close this ga...

As one kind of product service supply chains, maintenance, repair, and overhaul (MRO) service chain manages the physical industrial products and the related MRO services, and performs MRO service supply activities depending on demand planning and operational measures. The spare part and service demands in MRO service chain are typically uncertain b...

Operators’ work order descriptions in computerized maintenance management systems (CMMS) represent an untapped opportunity to benchmark a facility’s maintenance and operation performance. However, it is challenging to carry out analytics on these large and amorphous databases. This paper puts forward a text-mining method to extract information abou...

During the machining process, cutting forces cause deformation of thin-walled parts and cutting tools because of their low rigidity. Such deformation can lead to undercut and may result in defective parts. Since there are various unexpected factors that affect cutting forces during the machining process, the error compensation of cutting force indu...

The future Internet of Things (IoT) is expected to enable a new and wide range of decentralized systems (from small-scale smart homes to large-scale smart cities) in which ''things'' are able to sense/actuate, compute, and communicate, and thus play a central and crucial role. The growing importance of such novel networked cyber-physical context de...

In the last decade, we have witnessed the dramatic development of the smart home industry. Smart home systems are currently facing an explosive growth of data. Making good use of this vast amount of data has become an attractive research topic in recent years. In order to develop smart home systems’ abilities for learning users’ behaviors autonomou...

Expert knowledge has become an important factor in optimization decision-making for complex equipment maintenance. Motivated by the challenges of quantifying expert knowledge as a decision basis, we presented an expert knowledge-based dynamic maintenance task assignment model by using discrete stress–strength interference (DSSI) theory. We construc...

The maintenance, repair & operation (MRO) spare parts that are vital to machine operations are playing an increasingly important role in manufacturing enterprises. MRO spare parts supply chain management planning must be coordinated to ensure spare part availability while keeping the total cost to a minimum. Due to the specificity of MRO spare part...

The Internet of Things (IoT) envisions the seamless interconnection of the physical world and the cyber space. This provides a promising opportunity to build powerful services and applications for manufacturing. Many researchers are vigorously pursuing the topics in this area. However, there still lacks a big and unified picture. Thus this paper tr...

Monitoring solutions using the Internet of Things (IoT) techniques, can continuously gather sensory data, such as temperature and pressure, and provide abundant information for a monitoring center. Nevertheless, the heterogeneous and massive data bring significant challenges to real-Time monitoring and decision making, particularly in time-sensitiv...

We explored methods of detecting occupancy in single-person offices using data already collected by the occupant’s PC, or data from relatively cheap sensors added to the PC. We collected data at 15-second intervals for up to 31 days in each of 28 offices. A combination of low/no cost sensors (webcam-based motion detection, and keyboard and mouse ac...

This paper studies the uncertain and random factors in real-life spare parts supply networks which are abstract and complex dynamical systems, then quantifies these factors in a mathematical model. To seek a dynamic spare parts ordering and pricing policy from a distributor’s viewpoint, stochastic programming with multi-choice parameters is applied...

Customized/personalized products are gaining more shares in today’s product market. Such products need collective efforts from consumers, manufacturers and third parties. On the other side, the Internet of Things (IoT) with pervasive sensing/actuating/networking ability greatly facilitates remote operation of manufacturing activities and efficient...

Agricultural supply chain is in general open and dynamic attributed and negotiation is a key strategy to realize collaboration among different entities involved. However, traditional static and offline negotiation strategy may not function well, an agricultural supply chain centered on third party logistics will provide a new collaborative relation...

Cloud manufacturing (CMfg) adopts and extends the concept of cloud computing to make mass Manufacturing Resources and Capabilities (MR/Cs) more widely integrated and accessible to users through the Internet. However, a single manufacturing cloud (MC) has limited MR/Cs, due to both economic and technical constraints, and can only provide limited man...

In 1999, Kevin Ashton envisioned a novel paradigm named Internet of Things (IoT), in which all things could see, hear, and smell the world for themselves, and interact with each other and cooperate with their neighbors to reach some common desired goals. In the following years, the IoT ideas started to spread rapidly due to the technology advanceme...

The Internet of Things (IoT) envisions the seamless interconnection of the physical world and the cyber space. This provides a promising opportunity to build powerful services and applications for manufacturing. This paper provides an overview of key research issues to be addressed and the latest advances in the area of IoT-enabled manufacturing. W...

Cloud Manufacturing can provide mass manufacturing resources and capabilities as services via the Internet. Undoubtedly, multiple manufacturing clouds (MCs) will have extremely abundant services in terms of function, price, etc. The ability to leverage ample services hosted in MCs has direct relation to the success or failure of a manufacturer. Mea...

Maintenance service outsourcing is a strategic driver for asset intensive industries pursuing the enhancement of supply chain performance. Maintenance service supplier selection plays a relevant role in this premise since its significant impact on equipment availability, and hence, on business success. To periodically review suppliers' performances...

Modeling information diffusion over social networks has attracted a lot of attention from both academia and industry. Based on universal generating function method and discrete stress-strength interference theory, a novel method is proposed to model the users’ random forwarding actions, and the most susceptible users are extracted. The effect of a...

Cloud manufacturing can manage mass manufacturing resources and capabilities, and provide them as services via the Internet. Undoubtedly, multiple manufacturing clouds (MCs) will have extremely abundant services in terms of function, price, reliability, location, etc. Selecting and using services from multiple MCs is a natural evolution in the best...