Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems

Thesis (M. Tech.) -- Central University of Technology, Free State, 2010Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation

Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method

Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by performing actions that interact with the environment. The relational representation allows more dynamic environment states than an attribute-based representation of reinforcement learning, but this flexibility also creates new problems such as a potentially infinite number of states. This thesis describes an RRL algorithm named Cerrla that creates policies directly from a set of learned relational “condition-action” rules using the Cross-Entropy Method (CEM) to control policy creation. The CEM assigns each rule a sampling probability and gradually modifies these probabilities such that the randomly sampled policies consist of ‘better’ rules, resulting in larger rewards received. Rule creation is guided by an inferred partial model of the environment that defines: the minimal conditions needed to take an action, the possible specialisation conditions per rule, and a set of simplification rules to remove redundant and illegal rule conditions, resulting in compact, efficient, and comprehensible policies. Cerrla is evaluated on four separate environments, where each environment has several different goals. Results show that compared to existing RRL algorithms, Cerrla is able to learn equal or better behaviour in less time on the standard RRL environment. On other larger, more complex environments, it can learn behaviour that is competitive to specialised approaches. The simplified rules and CEM’s bias towards compact policies result in comprehensive and effective relational policies created in a relatively short amount of time

Monitoring Complex Processes to Verify System Conformance: A Declarative Rule-Based Framework

Over the last 60 years, computers and software have favoured incredible advancements in every field. Nowadays, however, these systems are so complicated that it is difficult – if not challenging – to understand whether they meet some requirement or are able to show some desired behaviour or property. This dissertation introduces a Just-In-Time (JIT) a posteriori approach to perform the conformance check to identify any deviation from the desired behaviour as soon as possible, and possibly apply some corrections. The declarative framework that implements our approach – entirely developed on the promising open source forward-chaining Production Rule System (PRS) named Drools – consists of three components: 1. a monitoring module based on a novel, efficient implementation of Event Calculus (EC), 2. a general purpose hybrid reasoning module (the first of its genre) merging temporal, semantic, fuzzy and rule-based reasoning, 3. a logic formalism based on the concept of expectations introducing Event-Condition-Expectation rules (ECE-rules) to assess the global conformance of a system. The framework is also accompanied by an optional module that provides Probabilistic Inductive Logic Programming (PILP). By shifting the conformance check from after execution to just in time, this approach combines the advantages of many a posteriori and a priori methods proposed in literature. Quite remarkably, if the corrective actions are explicitly given, the reactive nature of this methodology allows to reconcile any deviations from the desired behaviour as soon as it is detected. In conclusion, the proposed methodology brings some advancements to solve the problem of the conformance checking, helping to fill the gap between humans and the increasingly complex technology.Negli ultimi 60 anni, i computer e i programmi hanno favorito incredibili avanzamenti in ogni campo. Oggigiorno, purtroppo, questi sistemi sono così complicati che è difficile – se non impossibile – capire se soddisfano qualche requisito o mostrano un comportamento o una proprietà desiderati. Questa tesi introduce un approccio a posteriori Just-In-Time (JIT) per effettuare il controllo di conformità ed identificare appena possibile ogni deviazione dal comportamento desiderato, ed eventualmente applicare qualche correzione. Il framework dichiarativo che implementa il nostro approccio – interamente sviluppato su una promettente piattaforma open source di Production Rule System (PRS) chiamata Drools – si compone di tre elementi: 1. un modulo per il monitoraggio basato su una nuova implementazione efficiente di Event Calculus (EC), 2. un modulo generale per il ragionamento ibrido (il primo del suo genere) che supporta ragionamento temporale, semantico, fuzzy e a regole, 3. un formalismo logico basato sul concetto di aspettativa che introduce le Event-Condition-Expectation rules (ECE-rules) per valutare la conformità globale di un sistema. Il framework è anche accompagnato da un modulo opzionale che fornisce Probabilistic Inductive Logic Programming (PILP). Spostando il controllo di conformità da dopo l’esecuzione ad appena in tempo, questo approccio combina i vantaggi di molti metodi a posteriori e a priori proposti in letteratura. Si noti che, se le azioni correttive sono fornite esplicitamente, la natura reattiva di questo metodo consente di conciliare le deviazioni dal comportamento desiderato non appena questo viene rilevato. In conclusione, la metodologia proposta introduce alcuni avanzamenti per risolvere il problema del controllo di conformità, contribuendo a colmare il divario tra l’uomo e la tecnologia, sempre più complessa

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

An agile and adaptive holonic architecture for manufacturing control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2004. Faculdade de Engenharia. Universidade do Port

Sustainable Spatial Planning based on Ecosystem Services, Green Infrastructure and Nature-Based Solutions

Theoretical and methodological contributions as well as critical discussions on policy implementation characterize this Special Issue, with special reference to the following themes and research questions: 1) Integration of ecosystem services within spatial plans and strategic environmental assessment: “What function do ecosystem services play, or could play, within plan-making processes and strategic environmental assessments?”; “What are the most important challenges in putting integration into practice, and/or the most significant obstacles to achieving integration?”; and “What roles do scientific and technical expertise vs. community values and local knowledge play in integrating ecosystem services within spatial plans and environmental assessments?”; 2) Consideration and use of green infrastructure within spatial plans: “What function do green infrastructure play within plan-making processes?”; “What kinds of spatial plans are most suited for, or most effective in, designing and implementing green infrastructure?”; and “Does scale (local, regional, etc.) make a difference in the way green infrastructure are implemented within spatial plans?”; 3) Relationship between nature-based solutions and spatial plans: “Since nature-based solutions are increasingly promoted at the very strategic level, i.e., that of broad policies, and implemented at the very detailed level, i.e., that of projects, what is the role of nature-based solutions within spatial plans?” and “What tools are at planners’ disposal to effectively integrate nature-based solutions in planning processes and promote their use, especially in urban contexts?”

A framework for knowledge discovery within business intelligence for decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes

An intelligent system for facility management

A software system has been developed that monitors and interprets temporally changing (internal) building environments and generates related knowledge that can assist in facility management (FM) decision making. The use of the multi agent paradigm renders a system that delivers demonstrable rationality and is robust within the dynamic environment that it operates. Agent behaviour directed at working toward goals is rendered intelligent with semantic web technologies. The capture of semantics though formal expression to model the environment, adds a richness that the agents exploit to intelligently determine behaviours to satisfy goals that are flexible and adaptable. The agent goals are to generate knowledge about building space usage as well as environmental conditions by elaborating and combining near real time sensor data and information from conventional building models. Additionally further inferences are facilitated including those about wasted resources such as unnecessary lighting and heating for example. In contrast, current FM tools, lacking automatic synchronisation with the domain and rich semantic modelling, are limited to the simpler querying of manually maintained models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An intelligent system for facility management

A software system has been developed that monitors and interprets temporally changing (internal) building environments and generates related knowledge that can assist in facility management (FM) decision making. The use of the multi agent paradigm renders a system that delivers demonstrable rationality and is robust within the dynamic environment that it operates. Agent behaviour directed at working toward goals is rendered intelligent with semantic web technologies. The capture of semantics though formal expression to model the environment, adds a richness that the agents exploit to intelligently determine behaviours to satisfy goals that are flexible and adaptable. The agent goals are to generate knowledge about building space usage as well as environmental conditions by elaborating and combining near real time sensor data and information from conventional building models. Additionally further inferences are facilitated including those about wasted resources such as unnecessary lighting and heating for example. In contrast, current FM tools, lacking automatic synchronisation with the domain and rich semantic modelling, are limited to the simpler querying of manually maintained models