Sensor data-based decision making

Increasing globalization and growing industrial system complexity has amplified the interest in the use of information provided by sensors as a means of improving overall manufacturing system performance and maintainability. However, utilization of sensors can only be effective if the real-time data can be integrated into the necessary business processes, such as production planning, scheduling and execution systems. This integration requires the development of intelligent decision making models that can effectively process the sensor data into information and suggest appropriate actions. To be able to improve the performance of a system, the health of the system also needs to be maintained. In many cases a single sensor type cannot provide sufficient information for complex decision making including diagnostics and prognostics of a system. Therefore, a combination of sensors should be used in an integrated manner in order to achieve desired performance levels. Sensor generated data need to be processed into information through the use of appropriate decision making models in order to improve overall performance. In this dissertation, which is presented as a collection of five journal papers, several reactive and proactive decision making models that utilize data from single and multi-sensor environments are developed. The first paper presents a testbed architecture for Auto-ID systems. An adaptive inventory management model which utilizes real-time RFID data is developed in the second paper. In the third paper, a complete hardware and inventory management solution, which involves the integration of RFID sensors into an extremely low temperature industrial freezer, is presented. The last two papers in the dissertation deal with diagnostic and prognostic decision making models in order to assure the healthy operation of a manufacturing system and its components. In the fourth paper a Mahalanobis-Taguchi System (MTS) based prognostics tool is developed and it is used to estimate the remaining useful life of rolling element bearings using data acquired from vibration sensors. In the final paper, an MTS based prognostics tool is developed for a centrifugal water pump, which fuses information from multiple types of sensors in order to take diagnostic and prognostics decisions for the pump and its components --Abstract, page iv

Manufacturing systems interoperability in dynamic change environments

The benefits of rapid i.e. nearly real time, data and information enabled decision making at all levels of a manufacturing enterprise are clearly documented: the ability to plan accurately, react quickly and even pre-empt situations can save industries billions of dollars in waste. As the pace of industry increases with automation and technology, so the need for accurate, data, information and knowledge increases. As the required pace of information collection, processing and exchange change so to do the challenges of achieving and maintaining interoperability as the systems develop: this thesis focuses on the particular challenge of interoperability between systems defined in different time frames, which may have very different terminology. This thesis is directed to improve the ability to assess the requirement for systems to interoperate, and their suitability to do so, as new systems emerge to support this need for change. In this thesis a novel solution concept is proposed that assesses the requirement and suitability of systems for interoperability. The solution concept provides a mechanism for describing systems consistently and unambiguously, even if they are developed in different timeframes. Having resolved the issue of semantic consistency through time the analysis of the systems against logical rules for system interoperability is then possible. The solution concept uses a Core Concept ontology as the foundation for a multi-level heavyweight ontology. The multiple level ontology allows increasing specificity (to ensure accuracy), while the heavyweight (i.e. computer interpretable) nature provides the semantic and logical, rigour required. A detailed investigation has been conducted to test the solution concept using a suitably dynamic environment: Manufacturing Systems, and in particular the emerging field of Manufacturing Intelligence Systems. A definitive definition for the Manufacturing Intelligence domain, constraining interoperability logic, and a multi-level domain ontology have been defined and used to successfully prove the Solution Concept. Using systems from different timeframes, the Solution concept testing successfully identified systems which needed to interoperate, whether they were suitable for interoperation and provided feedback on the reasons for unsuitability which were validated as correct against real world observations

Fractal architecture for 'leagile' networked enterprises.

The manufacturing environment and markets in recent times are becoming increasingly dynamic, diverse and unpredictable, due mainly to fast evolution of products and technology, erratic customer behaviour and high consumerism and an increasingly shorter lead-time. The burden of the impact falls on organisational structures built on centralized, rigid manufacturing architecture, because they cannot cope or adapt to the highly uncertain or unpredictable nature of the market. Enterprises who wish to survive these challenges need to rethink their business and manufacturing models, and most importantly reinvent their tactical, operational and organizational formulas to leverage their strategic long term visions.Newer manufacturing systems to curb the effects of this upheaval have to promote an entirely decentralised, flexible, distributed, configurable and adaptable architecture to ameliorate this condition. Many philosophies are proposed and studied towards planning, monitoring, and controlling the 21st century manufacturing system. These include - Bionic manufacturing system (BMS), Holonic manufacturing system (HMS), Fractal manufacturing system (FrMS), Responsive manufacturing etc.This research program focuses on the FrMS, which has vast conceptual advantageous features among these new philosophies, but its implementation has proved very difficult. FrMS is based on autonomous, cooperating, self-similar agent called fractal that has the capability of perceiving, adapting and evolving with respect to its partners and environment. The fractal manufacturing configuration uses self regulating, organisational work groups, each with identical goals and within its own area of competence to build up an integrated, holistic network system of companies. This network yields constant improvement as well as continuous checks and balances through self-organising control loops. The study investigates and identifies the nature, characteristic features and feasibility of this system in comparison to traditional approaches with a detailed view to maximising the logistical attribute of lean manufacturing system and building a framework for 'leagile' (an integration of lean and agile solutions) networked capabilities. It explores and establishes the structural characteristic potentials of Fractal Manufacturing Partnership (FMP), a hands-on collaboration between enterprises and their key suppliers, where the latter become assemblers of their components while co-owning the enterprise's facility, to create and achieve high level of responsiveness. It is hoped that this architecture will drive and harness the evolution from a vertically integrated company, to a network of integrated, leaner core competencies needed to tackle and weather the storm of the 21st century manufacturing system