A constraint and position identification (CPI) approach for the synthesis of decoupled spatial translational compliant parallel manipulators

This paper introduces a screw theory based method termed constraint and position identification (CPI) approach to synthesize decoupled spatial translational compliant parallel manipulators (XYZ CPMs) with consideration of actuation isolation. The proposed approach is based on a systematic arrangement of rigid stages and compliant modules in a three-legged XYZ CPM system using the constraint spaces and the position spaces of the compliant modules. The constraint spaces and the position spaces are firstly derived based on the screw theory instead of using the rigid-body mechanism design experience. Additionally, the constraint spaces are classified into different constraint combinations, with typical position spaces depicted via geometric entities. Furthermore, the systematic synthesis process based on the constraint combinations and the geometric entities is demonstrated via several examples. Finally, several novel decoupled XYZ CPMs with monolithic configurations are created and verified by finite elements analysis. The present CPI approach enables experts and beginners to synthesize a variety of decoupled XYZ CPMs with consideration of actuation isolation by selecting an appropriate constraint and an optimal position for each of the compliant modules according to a specific application

Conceptual designs of multi-degree of freedom compliant parallel manipulators composed of wire-beam based compliant mechanisms

This paper proposes conceptual designs of multi-degree(s) of freedom (DOF) compliant parallel manipulators (CPMs) including 3-DOF translational CPMs and 6-DOF CPMs using a building block based pseudo-rigid-body-model (PRBM) approach. The proposed multi-DOF CPMs are composed of wire-beam based compliant mechanisms (WBBCMs) as distributed-compliance compliant building blocks (CBBs). Firstly, a comprehensive literature review for the design approaches of compliant mechanisms is conducted, and a building block based PRBM is then presented, which replaces the traditional kinematic sub-chain with an appropriate multi-DOF CBB. In order to obtain the decoupled 3-DOF translational CPMs (XYZ CPMs), two classes of kinematically decoupled 3-PPPR (P: prismatic joint, R: revolute joint) translational parallel mechanisms (TPMs) and 3-PPPRR TPMs are identified based on the type synthesis of rigid-body parallel mechanisms, and WBBCMs as the associated CBBs are further designed. Via replacing the traditional actuated P joint and the traditional passive PPR/PPRR sub-chain in each leg of the 3-DOF TPM with the counterpart CBBs (i.e. WBBCMs), a number of decoupled XYZ CPMs are obtained by appropriate arrangements. In order to obtain the decoupled 6-DOF CPMs, an orthogonally-arranged decoupled 6-PSS (S: spherical joint) parallel mechanism is first identified, and then two example 6-DOF CPMs are proposed by the building block based PRBM method. It is shown that, among these designs, two types of monolithic XYZ CPM designs with extended life have been presented

Design of 3-legged XYZ compliant parallel manipulators with minimised parasitic rotations

This paper deals with the design of 3-legged distributed-compliance XYZ compliant parallel manipulators (CPMs) with minimised parasitic rotations, based on the kinematically decoupled 3-PPPRR (P: prismatic joint, and R: revolute joint) and 3-PPPR translational parallel mechanisms (TPMs). The designs are firstly proposed using the kinematic substitution approach, with the help of the stiffness center (SC) overlapping based approach. This is done by an appropriate embedded arrangement so that all of the SCs associated with the passive compliant modules overlap at the point where all of the input forces applied at the input stages intersect. Kinematostatic modelling and characteristic analysis are then carried out for the proposed large-range 3-PPPRR XYZ CPM with overlapping SCs. The results from finite element analysis (FEA) are compared to the characteristics found for the developed analytical models, as are experimental testing results (primary motion) from the prototyped 3-PPPRR XYZ CPM with overlapping SCs. Finally, issues on large-range motion and dynamics of such designs are discussed, as are possible improvements of the actuated compliant P joint. It is shown that the potential merits of the designs presented here include a) minimised parasitic rotations by only using three identical compliant legs; b) compact configurations and small size due to the use of embedded designs; c) approximately kinematostatically decoupled designs capable of easy controls; and d) monolithic fabrication for each leg using existing planar manufacturing technologies such as electric discharge machining (EDM)

Key Performance Monitoring and Diagnosis in Industrial Automation Processes

With ever increasing global competition, monitoring and diagnosis methods based on key performance indicator (KPI) are increasingly receiving attention in the process industry. Primarily due to the scale and complexity of modern automation processes, application of signal processing and model-based monitoring methods is too costly and time-consuming. On the other hand, due to the availability of cheap measurement and storage systems, a large amount of process and KPI data is obtained. As a result, developing data-driven KPI monitoring methods has become an area of great interest in both academics and industry. Therefore, this thesis is focused on the data-driven design of systematic KPI monitoring and diagnosis systems for industrial automation processes. Depending on the relationship between the low-level process variables and the high-level KPIs, industrial processes can be classified into three groups: 1. Static processes (SPs) are those described by algebraic equations; 2. Lumped-parameter processes (LPPs) are those described by ordinary differential equations; and 3. Distributed-parameter processes (DPPs) are those described by partial differential equations. For each of these groups of processes, analytical redundancy plays a very important role when developing efficient process monitoring tools. For SPs, multivariate-statistics-based methods have been used. However, their applicability is restricted by high mathematical complexity, high design costs and low diagnostic performance. For this reason, an alternative improved method has been proposed in this thesis. For LPPs, complex model-based methods have been implemented. Therefore, to reduce the design costs required for monitoring LPPs, efficient Subspace identification based approaches are presented. Finally, since there are very few available model-based methods for DPPs, this thesis presents novel approaches for KPI monitoring in DPPs. For all these methods, the design procedures are based on the process I/O data and do not require advanced mathematical knowledge. After performance degradation has been detected, it is important to identify the root causes to prevent further losses. In industrial processes, performance degradation is more often caused by multiplicative faults. In this work, a new data-driven multiplicative fault diagnosis approach is proposed. This approach aims at assisting the maintenance personnel by narrowing down the investigation scope. As a result, overall equipment effectiveness (OEE) can be significantly improved. To show the effectiveness of the proposed approaches, case studies on the Tennessee Eastman benchmark process, the continuous stirred tank heater benchmark and the simulated drying section of a paper machine have been performed. The proposed methods worked successfully with these processes.Key Performance Überwachung und Diagnose in industriellen Automatisierungsprozessen Im Rahmen einer stetigen Zunahme des globalen Wettbewerbs erhalten Key Performance Indikator (KPI) basierte Überwachungs- und Diagnosetechniken zunehmend Aufmerksamkeit in der Prozessindustrie. Vor allem vor dem Hintergrund von Umfang und Komplexität moderner Automatisierungsprozesse ist die Anwendung von Signalverarbeitung und modellbasierten Überwachungstechniken zu teuer und zu zeitaufwendig. Andererseits ist häufig auf Grund der Verfügbarkeit von günstigen Mess- und Speichersystemen, eine große Menge von Prozess- und KPI-Daten vorhanden. Daher ist die Entwicklung von datenbasierten Verfahren ein Forschungsfeld, welches sowohl im akademischen als auch im industriellen Bereich mit großem Interesse verfolgt wird. Dementsprechend liegt der Fokus der vorliegenden Arbeit auf einem systematischen und datenbasierten Entwurf von KPI-Überwachungs- und -Diagnosesystemen für industrielle Automatisierungsprozesse. Anhand der Beziehung zwischen den low-level Prozessgrößen und den high-level KPIs können industrielle Prozesse in drei Gruppen eingeteilt werden: 1. Statische Prozesse (SP) sind Prozesse, die sich durch algebraische Gleichungen beschrieben lassen; 2. Konzentrierte-Parameter Prozesse (KPP) sind Prozesse, welche durch gewöhnliche Differentialgleichungen beschrieben werden; und 3. Verteilte-Parameter Prozesse (VPP) sind Prozesse, welche durch partielle Differentialgleichungen beschrieben werden. Für jede dieser Gruppen spielt das Konzept der analytischen Redundanz eine sehr wichtige Rolle bei der Entwicklung von effizienten Prozessüberwachungs-Tools. Für SP, sind multivariate statistische Verfahren verwendet worden. Allerdings ist deren Anwendbarkeit durch hohe mathematische Komplexität, einen hohen Entwurfsaufwand und eine geringen Diagnoseleistung beschränkt. Aus diesem Grund wird ein alternatives, verbessertes Verfahren in dieser Arbeit vorgeschlagen. Für KPP, sind komplexe modellbasierte Methoden implementiert worden. Um die Entwicklungskosten für die Überwachung der KPP zu reduzieren, wird eine effiziente Methode, basierend auf Subspace-Identifikation, vorgestellt. Da es nur sehr wenige modellbasierte Methoden für VPP gibt, präsentiert diese Arbeit schließlich neue Verfahren für die KPI- Überwachung in VPP. Alle vorgestellten Verfahren basieren auf den Prozess E/A Daten und erfordern daher keine tiefergehenden mathematischen Kenntnisse über den Prozess. Nach erfolgreicher Erkennung des Leistungsabfalls eines KPI, ist es in einem nächsten Schritt erforderlich die Ursache zu identifizieren, um weitere ökonomische Verluste zu verhindern. In industriellen Prozessen wird ein Leistungsabfall häufig durch multiplikative Fehler verursacht. In dieser Arbeit wird ein neues datenbasiertes, multiplikatives Fehlerdiagnoseverfahren vorgeschlagen. Dieses Verfahren soll der Unterstützung des Wartungspersonals dienen, indem eine Eingrenzung der Problemursache vorgenommen wird. Als Ergebnis kann somit die OEE (Overall Equipment Effectiveness) deutlich verbessert werden. Um die Wirksamkeit der vorgeschlagenen Verfahren zu demonstrieren, wurden verschiedene Fallstudien an Hand des „Tennessee Eastman“ Benchmark, des „continuous stirred tank heater“ Benchmark und einer simulierten Trockenpartie einer Papiermaschine durchgeführt. Die Effektivität der vorgeschlagenen Methoden konnte an Hand der aufgeführten Benchmark Prozesse erfolgreich gezeigt werden

Explainability in Graph Neural Networks: A Taxonomic Survey

Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain the predictions, giving rise to the area of explainability. Recently, explainability of deep models on images and texts has achieved significant progress. In the area of graph data, graph neural networks (GNNs) and their explainability are experiencing rapid developments. However, there is neither a unified treatment of GNN explainability methods, nor a standard benchmark and testbed for evaluations. In this survey, we provide a unified and taxonomic view of current GNN explainability methods. Our unified and taxonomic treatments of this subject shed lights on the commonalities and differences of existing methods and set the stage for further methodological developments. To facilitate evaluations, we generate a set of benchmark graph datasets specifically for GNN explainability. We summarize current datasets and metrics for evaluating GNN explainability. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations

Blockchain technology research and application: a systematic literature review and future trends

Blockchain, as the basis for cryptocurrencies, has received extensive attentions recently. Blockchain serves as an immutable distributed ledger technology which allows transactions to be carried out credibly in a decentralized environment. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability, security and other issues waiting to be overcome. This article provides a comprehensive overview of blockchain technology and its applications. We begin with a summary of the development of blockchain, and then give an overview of the blockchain architecture and a systematic review of the research and application of blockchain technology in different fields from the perspective of academic research and industry technology. Furthermore, technical challenges and recent developments are also briefly listed. We also looked at the possible future trends of blockchain

Understanding coupled factors that affect the modelling accuracy of typical planar compliant mechanisms

In order to accurately model compliant mechanism utilizing plate flexures, qualitative planar stress (Young’s modulus) and planar strain (plate modulus) assumptions are not feasible. This paper investigates a quantitative equivalent modulus using nonlinear finite element analysis (FEA) to reflect coupled factors in affecting the modelling accuracy of two typical distributed- compliance mechanisms. It has been shown that all parameters have influences on the equivalent modulus with different degrees; that the presence of large load-stiffening effect makes the equivalent modulus significantly deviate from the planar assumptions in two ideal scenarios; and that a plate modulus assumption is more reasonable for a very large out-of-plane thickness if the beam length is large

Guiding On-Chip Optical Beams without Diffraction in a Rod- Type Silicon Photonic Crystal

Guiding on-chip optical beams without diffraction is very important in the future’s all-photonic circuits. Herein, both theoretically and experimentally, we study an all-angle quasi-self-collimation phenomenon occurring in photonic crystals composed of silicon nanorods. When the all-angle quasi-self-collimation phenomenon occurs, the optical beams can be incident onto such photonic crystals from directions covering a wide range (extremely close to all-angle) of incident angles direction and become highly localized along even a single array of rods, which finally achieve results in the narrow-beam propagation without divergence. The propagation length is expected to be 1000 times larger than the wavelength of light. Theoretically, it is shown that such all-angle quasi-self-collimation phenomenon is owing to the symmetry change of the lattice of photonic crystals. By changing the symmetry of a photonic crystal to straighten the isofrequency contours, the photonic crystal shows the all-angle quasi-self-collimation effect. Experimentally, we show the observation of all-angle quasi-self-collimation phenomenon occurring in a rod-type silicon photonic crystal fabricated on by patterning a silicon-on-insulator (SOI) wafer. The experimentally observed propagation length is more than 0.4 mm over the telecom wavelength range, even though at large angle of incidence, which is a relatively large length scale for on-chip optical interconnection