NOVELTY DETECTION FOR PREDICTIVE MAINTENANCE

Since the advent of Industry 4. 0 significant research has been conducted to apply machine learning to the vast array of Internet of Things (IoT) data produced by Industrial Machines. One such topic is to Predictive Maintenance. Unlike some other machine learning domains such as NLP and computer vision, Predictive Maintenance is a relatively new area of focus. Most of the published work demonstrates the effectiveness of supervised classification for predictive maintenance. Some of the challenges highlighted in the literature are the cost and difficulty of obtaining labelled samples for training. Novelty detection is a branch of machine learning that after being trained on normal operations detects if new data comes from the same process or is different, eliminating the requirement to label data points. This thesis applies novelty detection to both a public data set and one that was specifically collected to demonstrate a its application to predictive maintenance. The Local Optimization Factor showed better performance than a One-Class SVM on the public data. It was then applied to data from a 3-D printer and was able to detect faults it had not been trained on showing a slight lift from a random classifier

Forecasting of the Urban Area State Using Convolutional Neural Networks

Active development of modern cities requires not only efficient monitoring systems but furthermore forecasting systems that can predict future state of the urban area with high accuracy. In this work we present a method for urban area prediction based on geospatial activity of users in social network. One of the most popular social networks, Instagram, was taken as a source for spatial data and two large cities with different peculiarities of online activity – New York City, USA, and Saint Petersburg, Russia – were taken as target cities. We propose three different deep learning architectures that are able to solve a target problem and show that convolutional neural network based on three-dimensional convolution layers provides the best results with accuracy of 99%

Automating Water Capital Activities Using Naïve Bayes Classifier with Supervised Learning Algorithm

Municipal governments have the responsibility to provide safe drinking water to residents. Maintaining water infrastructure systems to keep a certain level of service is a vital service. It is possible by assessing all assets and planning capital work activities to renew and renovate the existing assets. The municipalities prioritize the capital activities of their infrastructure and are required to optimize their available resources. Past studies confirmed due to several complexities and imperfections of the available water network data, there is a need for a comprehensive multicriteria database to prioritize pipe capital plan decisions based on engineering expert judgment. This database must include information about water pipe physical condition and performance up to an acceptable level of service and criticality based on the water pipe location. In addition, the lack of standard regulatory requirements due to incomplete condition, criticality and performance assessment of the entire Municipal Water Network (MWN) leads to bias and undefendable engineering judgment. Although several pipe prioritization models have been developed and published in the literature, no comprehensive multi-decision criterion model is available to date, including the pipe segment condition, performance, and criticality. In this research, a novel Priority Action Number (PAN) is developed and parameterized based on pipe segment condition, performance and criticality. An automated Naïve Bayes Classifier (NBC) with a supervised machine learning model is proposed for consistent, defensible and personnel independence ranking of existing water pipe condition, performance, and criticality of all water pipes through MWN. This methodology automates the capital activities decision-making process. The research presents and develops a prioritizing approach for the MWN capital activities and aids in selecting assistive technology for rehabilitation and renewal capital activities. The developed model is applied to the City of London MWN database in a Geographical Information System (ArcGIS) database to validate and verify the model. The multi-level classifier model classified and assigned a capital work activity to all pipes in the City of London MWN. The presented multi-level NBC with a supervised learning algorithm replicates the expert's opinion and engineering judgement. Through NBC supervised machine learning algorithm, the capital project decision-making process is automated. This methodology will add consistency and defensibility to capital programs. Using this algorithm can help utility save money by automating industry best practices and optimizing long-term decisions about the order in which pipes need to be staged into capital works programs

Proactively managing drinking water distribution networks: A data- driven, statistical modelling approach to predict the risk of pipe failure.

Water distribution networks are critical infrastructures, providing clean water to millions of people. 3 billion litres of water are lost through pipe failure every day in the UK, impacting serviceability. Statistical pipe failure models can reduce pipe failures by providing valuable insights to enhance decision-making and promote proactive management. This research aims to understand the complexity of pipe failures in water distribution networks and develop a methodology for a reliable pipe failure model that identifies the risk of failure. Through an embedded case study and data-driven approach, several Objectives have been undertaken that comprise the body of research delivered through several research papers. This study offers several contributions to the immediate field of pipe failure research. Firstly, the findings investigate new factors that form the various modes and mechanisms of pipe failure, using alternative methods not commonly used in pipe failure research are used, including Generalized Additive Model and Dijkstra’s algorithm, and using data from a large UK water distribution network. Secondly, the research develops a suitable methodology for predicting annual pipe failures using an advanced machine learning method; a methodology that is easily transferrable. Thirdly, the research provides a useful means of predicting the risk of failure and visualising the results. Fourthly, the research investigates the challenges of pipe failure models using a semi-structured interview approach to review current practice. Finally, the research contributes by exploring several different data-driven methods and an embedded case study design to contribute to the broader context of pipe failure modelling. The approach presented in this research provides a methodological framework to enhance decision-making for asset management of pipes in clean water networks. Furthermore, it highlights the main limitations, particularly data quality and quantity, data-pre-processing, and model development, highlighting areas for future progress.Jude, Simon (Associate)PhD in Environment and Agrifoo

Chancen und Risiken der Digitalisierung kritischer kommunaler Infrastrukturen an den Beispielen der Wasser- und Abfallwirtschaft. Endbericht zum TA-Projekt

Im Mittelpunkt des TAB-Arbeitsberichts Nr. 205 steht der Einsatz digitaler Lösungen für zentrale Aufgaben der kommunalen Abfall- und Wasserwirtschaft. Der Bericht informiert für beide Bereiche über den aktuellen Stand der Technik und die Perspektiven der Digitalisierung. Für die Wasserwirtschaft wird der mögliche Nutzen digitaler Lösungen zur Bewältigung von Ausnahmesituationen untersucht. Darüber hinaus werden die Anfälligkeiten der Versorgungsinfrastrukturen der Wasserwirtschaft gegenüber Cyberangriffen und anderen IT-bedingten Störungen diskutiert sowie der aktuelle Stand der Informationssicherheit und der diesbezügliche Handlungsbedarf identifiziert. Abschließend werden Gestaltungsoptionen skizziert und ein möglicher Orientierungsrahmen speziell für politisches Handeln aufgezeigt, um den digitalen Fortschritt nachhaltig zu gestalten. Dem Bericht ist eine 20-seitige Zusammenfassung vorangestellt. Zentrale Ergebnisse sind auf vier Seiten im zugehörigen TAB-Fokus (s.u. Relation in KITopen) zusammengestellt