An optimized fuzzy logic model for proactive maintenance

Fuzzy logic has been proposed in previous studies for machine diagnosis, to overcome different drawbacks of the traditional diagnostic approaches used. Among these approaches Failure Mode and Effect Critical Analysis method(FMECA) attempts to identify potential modes and treat failures before they occur based on subjective expert judgments. Although several versions of fuzzy logic are used to improve FMECA or to replace it, since it is an extremely cost-intensive approach in terms of failure modes because it evaluates each one of them separately, these propositions have not explicitly focused on the combinatorial complexity nor justified the choice of membership functions in Fuzzy logic modeling. Within this context, we develop an optimization-based approach referred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) that smartly generates fuzzy logic rules using Truth Tables. The ITTFLM was tested on fan data collected in real-time from a plant machine. In the experiment, three types of membership functions (Triangular, Trapezoidal, and Gaussian) were used. The ITTFLM can generate outputs in 5ms, the results demonstrate that this model based on the Trapezoidal membership functions identifies the failure states with high accuracy, and its capability of dealing with large numbers of rules and thus meets the real-time constraints that usually impact user experience.Comment: 16 pages in single column format, 11 figures, 12th International Conference on Artificial Intelligence, Soft Computing and Applications (AIAA 2022) December 22 ~ 24, 2022, Sydney, Australi

Weather related fault prediction in minimally monitored distribution networks

Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales

Microgrid Formation-based Service Restoration Using Deep Reinforcement Learning and Optimal Switch Placement in Distribution Networks

A power distribution network that demonstrates resilience has the ability to minimize the duration and severity of power outages, ensure uninterrupted service delivery, and enhance overall reliability. Resilience in this context refers to the network's capacity to withstand and quickly recover from disruptive events, such as equipment failures, natural disasters, or cyber attacks. By effectively mitigating the effects of such incidents, a resilient power distribution network can contribute to enhanced operational performance, customer satisfaction, and economic productivity. The implementation of microgrids as a response to power outages constitutes a viable approach for enhancing the resilience of the system. In this work, a novel method for service restoration based on dynamic microgrid formation and deep reinforcement learning is proposed. To this end, microgrid formation-based service restoration is formulated as a Markov decision process. Then, by utilizing the node cell and route model concept, every distributed generation unit equipped with the black-start capability traverses the power system, thereby restoring power to the lines and nodes it visits. The deep Q-network is employed as a means to achieve optimal policy control, which guides agents in the selection of node cells that result in maximum load pick-up while adhering to operational constraints. In the next step, a solution has been proposed for the switch placement problem in distribution networks, which results in a substantial improvement in service restoration. Accordingly, an effective algorithm, utilizing binary particle swarm optimization, is employed to optimize the placement of switches in distribution networks. The input data necessary for the proposed algorithm comprises information related to the power system topology and load point data. The fitness of the solution is assessed by minimizing the unsupplied loads and the number of switches placed in distribution networks. The proposed methods are validated using a large-scale unbalanced distribution system consisting of 404 nodes, which is operated by Saskatoon Light and Power, a local utility in Saskatoon, Canada. Additionally, a balanced IEEE 33-node test system is also utilized for validation purposes

Investigating the impact of big data analytics on supply chain operations: case studies from the UK private sector

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityIn the era of increasing competitive pressure and pace of changing demand, volatility and disturbance have become the standard in today’s global markets. The spread of Covid-19 is a prime example of this. Supply chain (SC) managers are urged to rethink their competitive strategies and to identify ways to offer personalised products and services through making use of advanced technologies. With many SC executives recognising the role of data exploitation in improving performance, big data analytics (BDA) has become a salient factor for all kinds of organisations to increase efficiency and gain competitive advantage. Extant research in supply chain management (SCM) has provided limited understanding of strategic SC decision-making concerning BDA. Moreover, inquiry in this area is still poor in relation to providing a conceptual framework that illustrates the potential benefits of BDA utilisation in the SCO context. This study aims to investigate the real impact of BDA implementation in this context. A theoretical framework is developed to explain the motives behind adopting BDA in SCO along with the potential benefits of implementing BDA in SCO. Multiple case studies are the strategy utilised to collect qualitative data in order to gain detailed and in-depth understanding of the BDA as a new phenomenon in the context of SCOs. Semi-structured interviews were conducted in a cross-sectional time horizon across four different industries. Institutional theory and Task-Technology fit theories are utilised to provide better understanding regarding how and why firms adopt BDA as a novel technology, along with the drivers and opportunities of this technology utilisation. The empirical findings reveal that BDA is still in its infant stage, but it is a growing area which has recently been given more attention by scholars and managers. There is a disconnect between the hype and knowledge discussed in the literature and the real practice of BDA. That is, the current state of BDA use is relatively fragmented and rhetoric in discussion among practitioners and researchers. The main contribution of this study is breaking-down the process of BDA utilisation in order to evaluate its implementation in the SCO context by drawing upon a wide range of existing literature regarding BDA and SCO, in addition to present conceptual framework explaining the potential impact of BDA implementation through presenting BDA utilisation drivers, BDA capabilities, and its role in solving different issues