Improving the Diagnosability of Business Process Management Systems Using Test Points

The management and automation of business processes have become an essential task within IT organizations, where the diagnosis is a very important issue, since it enables fault isolation in a business process. The diagnosis process uses a set of test points (observations) and a model in order to explain a wrong behavior. In this work, an algorithm to allocate test points is presented, where the key idea is to improve the diagnosability, getting a better computational complexity for isolating faults in the activities of business processesJunta de Andalucía P08-TIC-04095Ministerio de Ciencia y Tecnología TIN2009-1371

Advanced Diagnostic and Prognostic Testbed (ADAPT) Testability Analysis Report

As system designs become more complex, determining the best locations to add sensors and test points for the purpose of testing and monitoring these designs becomes more difficult. Not only must the designer take into consideration all real and potential faults of the system, he or she must also find efficient ways of detecting and isolating those faults. Because sensors and cabling take up valuable space and weight on a system, and given constraints on bandwidth and power, it is even more difficult to add sensors into these complex designs after the design has been completed. As a result, a number of software tools have been developed to assist the system designer in proper placement of these sensors during the system design phase of a project. One of the key functions provided by many of these software programs is a testability analysis of the system essentially an evaluation of how observable the system behavior is using available tests. During the design phase, testability metrics can help guide the designer in improving the inherent testability of the design. This may include adding, removing, or modifying tests; breaking up feedback loops, or changing the system to reduce fault propagation. Given a set of test requirements, the analysis can also help to verify that the system will meet those requirements. Of course, a testability analysis requires that a software model of the physical system is available. For the analysis to be most effective in guiding system design, this model should ideally be constructed in parallel with these efforts. The purpose of this paper is to present the final testability results of the Advanced Diagnostic and Prognostic Testbed (ADAPT) after the system model was completed. The tool chosen to build the model and to perform the testability analysis with is the Testability Engineering and Maintenance System Designer (TEAMS-Designer). The TEAMS toolset is intended to be a solution to span all phases of the system, from design and development through health management and maintenance. TEAMS-Designer is the model-building and testability analysis software in that suite

Architecting Networked Engineering Systems

The primary goal in this dissertation is to create a new knowledge, make a transformative influence in the design of networked engineering systems adaptable to ambitious market demands, and to accommodate the Industry 4.0 design principles based on the philosophy that design is fundamentally a decision making process. The principal motivation in this dissertation is to establish a computational framework that is suitable for the design of low-cost and high-quality networked engineering systems adaptable to ambitious market demands in the context of Industry 4.0. Dynamic and ambitious global market demands make it necessary for competitive enterprises to have low-cost manufacturing processes and high-quality products. Smart manufacturing is increasingly being adopted by companies to respond to changes in the market. These smart manufacturing systems must be adaptable to dynamic changes and respond to unexpected disturbances, and uncertainty. Accordingly, a decision-based design computational framework, Design for Dynamic Management (DFDM), is proposed as a support to flexible, operable and rapidly configurable manufacturing processes. DFDM has three critical components: adaptable and concurrent design, operability analysis and reconfiguration strategies. Adaptable and concurrent design methods offer flexibility in selection of design parameters and the concurrent design of the mechanical and control systems. Operability analysis is used to determine the functionality of the system undergoing dynamic change. Reconfiguration strategies allow multiple configurations of elements in the system. It is expected that proposed computational framework results in next generation of networked engineering systems, where tools and sensors communicate with each other via the Internet of Things (IoT), sensors data would be used to create enriched digital system models, adaptable to fast-changing market requirements, which can produce higher quality products over a longer lifetime and at a lower cost. The computational framework and models proposed in this dissertation are applicable in system design, and/or product-service system design. This dissertation is a fundamental research and a way forward is DFDM transition to the industry through decision-based design platform. Decision-based design platform is a step toward new frontiers, Cyber-Physical-Social System Design, Manufacturing, and Services, contributing to further digitization

Matrix-structured manufacturing systems : Simulation performance analysis as a successor to dedicated production lines

ABSTRACT: Growing demand for product variations has led to mass personalized production in the manufacturing industry. Many manufacturers still use traditional product line configurations based on a dedicated manufacturing system (DMS). This system is not considered compatible with ongoing manufacturing trend. The challenge is finding a manufacturing system that would combine high productivity with the flexibility to produce multiple types of products. To this end, a matrix-structured manufacturing system (MMS) was developed. In addition, a reconfigurable manufacturing system (RMS) has been researched as a replacement for the DMS. The problem is that these two systems are not compared performance-wise. Moreover, it has not been investigated in which cases MMS or RMS would provide better compatibility to replace the product line. This Master’s thesis aims to answer how MMS and RMS perform compared to DMS regarding productivity and flexibility. Furthermore, it is evaluated in which manufacturing scenarios MMS provide better performance than RMS and DMS. Finally, thesis seeks to answer what are the benefits and disadvantages of MMS compared to RMS and DMS. To fill this research gap in knowledge, thesis presents a discrete-event simulation experiment. Thesis follows principles of experimental research with deductive approach and collects quantitative data from manufacturing simulation. Theoretical review is conducted focusing on characteristics and background of manufacturing systems. This information is utilized when designing and constructing simulation experiment. Simulation results are evaluated from the following performance perspectives: efficiency, effectiveness, delivery, and operational flexibility. These are based on com mon manufacturing competitive priorities. It was discovered that MMS provides highest workstation utilization and overall production effectiveness. RMS performed best in efficiency, delivery, and flexibility perspective. Flexibility was measured with production scenarios which involved simulated production disturbances and changes in number of products in system. It was found that production flow in MMS is declined significantly when number of products in system increases to high level. DMS resulted lowest in every scenario and performance view. The problem is the inflexibility to alternative production routes making it sensitive to production disruptions. Both MMS and RMS provided notably better productivity and flexibility performance than DMS. Based on the results, MMS is recommended for production scenarios where product variety extends over product family with importance in production customization and high station utilization. RMS is suitable for scenarios where delivery performance with flexibility is crucial.TIIVISTELMÄ: Kasvava määrä tuotevarianttien kysynnälle johtanut massapersonointiin massatuotannossa. Monet teollisuuden yritykset käyttävät yhä perinteisiä tuotelinjakonfiguraatioita, jotka kuuluvat kiinteisiin työasemiin perustuviin tuotantojärjestelmiin (DMS). Kuitenkaan näiden ei nähdä soveltuvan käynnissä olevaan tuotantotrendiin. Haasteena on löytää valmistusjärjestelmä, jossa korkea tuottavuus yhdistyisi joustavuuteen tuottaa monenlaisia tuotteita. Tätä varten kehitettiin matriisirakenteinen valmistusjärjestelmä (MMS). Lisäksi uudelleen konfiguroitavaa valmistusjärjestelmää (RMS) tutkitaan korvaajaksi perinteiselle tuotantolinjalle. Ongelmana on, että näitä kahta järjestelmää ei ole vertailtu suorituskyvyllisesti. Lisäksi ei ole tutkittu, missä tapauksissa MMS tai RMS olisi parempi vaihtoehto perinteisen tuotelinjan korvaajaksi. Tämän Pro gradu -tutkielman tavoitteena on vastata kysymykseen, miten MMS ja RMS suoriutuvat verrattuna perinteiseen tuotelinjaan tarkasteltaessa järjestelmän tuottavuutta ja joustavuutta. Lisäksi arvioidaan, missä valmistusskenaarioissa MMS tarjoaa parempaa suorituskykyä kuin RMS ja DMS. Viimeisenä tutkielmassa pyritään vastaamaan mitkä ovat MMS:n edut ja haitat verrattuna RMS:ään ja DMS:ään. Tutkimusaukon täyttämiseksi opinnäytetyö esittää diskreetti tapahtumapohjaisen simulaation. Tutkielma noudattaa kokeellisen tutkimuksen periaatteita deduktiivisella lähestymistavalla, jossa kerätään kvantitatiivista dataa tuotantosimulaatiosta. Teoreettinen katsaus keskittyy valmistusjärjestelmien ominaispiirteisiin sekä taustaan. Tätä tietoa hyödynnetään suunniteltaessa ja rakentaessa simulaatiokoetta. Simuloinnin tuloksia arvioidaan seuraavista suorituskyvyn näkökulmista: hyötysuhde, tehokkuus, toimituskyky ja järjestelmän joustavuus. Nämä perustuvat yleisiin tuotannon kilpailuprioriteetteihin. Tutkielmassa havaittiin, että MMS tarjoaa korkeimman työaseman käyttöasteen ja tuotantojärjestelmän tehokkuuden. RMS suoriutui parhaiten hyötysuhteen, toimituskyvyn ja joustavuuden näkökulmista. Joustavuutta mitattiin tuotantoskenaarioilla, joissa simuloitiin tuotantohäiriöitä ja muutoksia yhtäaikaisesti valmistettavien tuotteiden määrässä tuotannossa. Tutkielmassa todettiin, että MMS:n tuotantovirta hidastuu merkittävästi, kun valmistettavien tuotteiden määrä järjestelmässä kasvaa korkealle tasolle. DMS suoriutui huonoiten kaikissa skenaarioissa ja suorituskykynäkökulmista. Tämä johtuu joustamattomuudesta vaihtoehtoisille tuotereiteille tekien siitä herkän häiriöille. Molemmat sekä MMS että RMS tarjosivat huomattavasti paremman tuottavuus- ja joustavuuskyvyn kuin DMS. Tutkimuksen tuloksiin perustuen MMS:ää suositellaan tuotantoskenaarioihin, joissa tuotevalikoima ulottuu myös yli tuoteperheen, ja jossa tuotannon kustomointikyky ja korkea työasemien käyttöaste on tärkeää. RMS sopii skenaarioihin, joissa tuotannon toimituskyky ja joustavuus on ratkaisevan tärkeää

Maintenance Requirements in Aerospace Systems

AbstractThe paper discusses the importance of granularity in maintenance requirements. This becomes significantly important when investigating false alarms that cannot be verified, nor duplicated under typical inspections. Continuing advances in electromechanical systems, such as an aircraft's fuel system, can frequently face a high number of No Fault Found (NFF) events due to design limitations associated with fault diagnosability. This work discusses such maintenance requirements whilst covering the human aspects of the design – involving stakeholders identification and presenting meaningful data identified from the requirements. Ideas to optimise system diagnostics (by using extra sensors) to recognise and reduce failure ambiguity groups are also discussed. This can help indicate how the most appropriate data can be selected to represent the aforementioned maintenance requirements, facilitate in trade-off analysis and making design decisions

Multilevel distributed diagnosis and the design of a distributed network fault detection system based on the SNMP protocol.

In this thesis, we propose a new distributed diagnosis algorithm using the multilevel paradigm. This algorithm is a generalization of both the ADSD and Hi-ADSD algorithms. We present all details of the design and implementation of this multilevel adaptive distributed diagnosis algorithm called the ML-ADSD algorithm. We also present extensive simulation results comparing the performance of these three algorithms.In 1967, Preparata, Metze and Chien proposed a model and a framework for diagnosing faulty processors in a multiprocessor system. To exploit the inherent parallelism available in a multiprocessor system and thereby improving fault tolerance, Kuhl and Reddy, in 1980, pioneered a new area of research known as distributed system level diagnosis. Following this pioneering work, in 1991, Bianchini and Buskens proposed an adaptive distributed algorithm to diagnose fully connected networks. This algorithm called the ADSD algorithm has a diagnosis latency of O(N) testing rounds for a network with N nodes. With a view to improving the diagnosis latency of the ADSD algorithm, in 1998 Duarte and Nanya proposed a hierarchical distributed diagnosis algorithm for fully connected networks. This algorithm called the Hi-ADSD algorithm has a diagnosis latency of O(log2N) testing rounds. The Hi-ADSD algorithm can be viewed as a generalization of the ADSD algorithm.In all cases, the time required by the ML-ADSD algorithm is better than or the same as for the Hi-ADSD algorithm. The performance of the ML-ADSD algorithm can be improved by an appropriate choice of the number of clusters and the number of levels. Also, the ML-ADSD algorithm is scalable in the sense that only some minor modifications will be required to adapt the algorithm to networks of varying sizes. This property is not shared by the Hi-ADSD algorithm. The primary application of our research is to develop and implement a prototype network fault detection/monitoring system by integrating the ML-ADSD algorithm into a SNMP-based (Simple Network Management Protocol) fault management system. We report the details of the design and implementation of such a distributed network fault detection system

Smart Manufacturing

This book is a collection of 11 articles that are published in the corresponding Machines Special Issue “Smart Manufacturing”. It represents the quality, breadth and depth of the most updated study in smart manufacturing (SM); in particular, digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the Internet to expand system capabilities, (3) supporting data-driven decision-making activities at various domains and levels of businesses, or (4) reconfiguring systems to adapt to changes and uncertainties. System smartness can be evaluated by one or a combination of performance metrics such as degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This book features, firstly, the concepts digital triad (DT-II) and Internet of digital triad things (IoDTT), proposed to deal with the complexity, dynamics, and scalability of complex systems simultaneously. This book also features a comprehensive survey of the applications of digital technologies in space instruments; a systematic literature search method is used to investigate the impact of product design and innovation on the development of space instruments. In addition, the survey provides important information and critical considerations for using cutting edge digital technologies in designing and manufacturing space instruments