Predicting remaining useful life of rotating machinery based artificial neural network

Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure

Bio-inspired relevant interaction modelling in cognitive crowd management

Cognitive algorithms, integrated in intelligent systems, represent an important innovation in designing interactive smart environments. More in details, Cognitive Systems have important applications in anomaly detection and management in advanced video surveillance. These algorithms mainly address the problem of modelling interactions and behaviours among the main entities in a scene. A bio-inspired structure is here proposed, which is able to encode and synthesize signals, not only for the description of single entities behaviours, but also for modelling cause–effect relationships between user actions and changes in environment configurations. Such models are stored within a memory (Autobiographical Memory) during a learning phase. Here the system operates an effective knowledge transfer from a human operator towards an automatic systems called Cognitive Surveillance Node (CSN), which is part of a complex cognitive JDL-based and bio-inspired architecture. After such a knowledge-transfer phase, learned representations can be used, at different levels, either to support human decisions, by detecting anomalous interaction models and thus compensating for human shortcomings, or, in an automatic decision scenario, to identify anomalous patterns and choose the best strategy to preserve stability of the entire system. Results are presented in a video surveillance scenario , where the CSN can observe two interacting entities consisting in a simulated crowd and a human operator. These can interact within a visual 3D simulator, where crowd behaviour is modelled by means of Social Forces. The way anomalies are detected and consequently handled is demonstrated, on synthetic and also on real video sequences, in both the user-support and automatic modes

Data Fusion for Materials Location Estimation in Construction

Effective automated tracking and locating of the thousands of materials on construction sites improves material distribution and project performance and thus has a significant positive impact on construction productivity. Many locating technologies and data sources have therefore been developed, and the deployment of a cost-effective, scalable, and easy-to-implement materials location sensing system at actual construction sites has very recently become both technically and economically feasible. However, considerable opportunity still exists to improve the accuracy, precision, and robustness of such systems. The quest for fundamental methods that can take advantage of the relative strengths of each individual technology and data source motivated this research, which has led to the development of new data fusion methods for improving materials location estimation. In this study a data fusion model is used to generate an integrated solution for the automated identification, location estimation, and relocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is paid to noisy environments where low-cost RFID tags are attached to all materials, which are sometimes moved repeatedly around the site. A portion of the work focuses partly on relocation detection because it is closely coupled with location estimation and because it can be used to detect the multi-handling of materials, which is a key indicator of inefficiency. This research has successfully addressed the challenges of fusing data from multiple sources of information in a very noisy and dynamic environment. The results indicate potential for the proposed model to improve location estimation and movement detection as well as to automate the calculation of the incidence of multi-handling

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

A framework for context-aware sensor fusion

Mención Internacional en el título de doctorSensor fusion is a mature but very active research field, included in the more general discipline of information fusion. It studies how to combine data coming from different sensors, in such way that the resulting information is better in some sense –more complete, accurate or stable– than any of the original sources used individually. Context is defined as everything that constraints or affects the process of solving a problem, without being part of the problem or the solution itself. Over the last years, the scientific community has shown a remarkable interest in the potential of exploiting this context information for building smarter systems that can make a better use of the available information. Traditional sensor fusion systems are based in fixed processing schemes over a predefined set of sensors, where both the employed algorithms and domain are assumed to remain unchanged over time. Nowadays, affordable mobile and embedded systems have a high sensory, computational and communication capabilities, making them a perfect base for building sensor fusion applications. This fact represents an opportunity to explore fusion system that are bigger and more complex, but pose the challenge of offering optimal performance under changing and unexpected circumstances. This thesis proposes a framework supporting the creation of sensor fusion systems with self-adaptive capabilities, where context information plays a crucial role. These two aspects have never been integrated in a common approach for solving the sensor fusion problem before. The proposal includes a preliminary theoretical analysis of both problem aspects, the design of a generic architecture capable for hosting any type of centralized sensor fusion application, and a description of the process to be followed for applying the architecture in order to solve a sensor fusion problem. The experimental section shows how to apply this thesis’ proposal, step by step, for creating a context-aware sensor fusion system with self-adaptive capabilities. This process is illustrated for two different domains: a maritime/coastal surveillance application, and ground vehicle navigation in urban environment. Obtained results demonstrate the viability and validity of the implemented prototypes, as well as the benefit of including context information to enhance sensor fusion processes.La fusión de sensores es un campo de investigación maduro pero no por ello menos activo, que se engloba dentro de la disciplina más amplia de la fusión de información. Su papel consiste en mezclar información de dispositivos sensores para proporcionar un resultado que mejora en algún aspecto –completitud, precisión, estabilidad– al que se puede obtener de las diversas fuentes por separado. Definimos contexto como todo aquello que restringe o afecta el proceso de resolución de un problema, sin ser parte del problema o de su solución. En los últimos años, la comunidad científica ha demostrado un gran interés en el potencial que ofrece el contexto para construir sistemas más inteligentes, capaces de hacer un mejor uso de la información disponible. Por otro lado, el desarrollo de sistemas de fusión de sensores ha respondido tradicionalmente a esquemas de procesado poco flexibles sobre un conjunto prefijado de sensores, donde los algoritmos y el dominio de problema permanecen inalterados con el paso del tiempo. En la actualidad, el abaratamiento de dispositivos móviles y embebidos con gran capacidad sensorial, de comunicación y de procesado plantea nuevas oportunidades. La comunidad científica comienza a explorar la creación de sistemas con mayor grado de complejidad y autonomía, que sean capaces de adaptarse a circunstancias inesperadas y ofrecer un rendimiento óptimo en cada caso. En esta tesis se propone un framework que permite crear sistemas de fusión de sensores con capacidad de auto-adaptación, donde la información contextual juega un papel fundamental. Hasta la fecha, ambos aspectos no han sido integrados en un enfoque conjunto. La propuesta incluye un análisis teórico de ambos aspectos del problema, el diseño de una arquitectura genérica capaz de dar cabida a cualquier aplicación de fusión de sensores centralizada, y la descripción del proceso a seguir para aplicar dicha arquitectura a cualquier problema de fusión de sensores. En la sección experimental se demuestra cómo aplicar nuestra propuesta, paso por paso, para crear un sistema de fusión de sensores adaptable y sensible al contexto. Este proceso de diseño se ilustra sobre dos problemas pertenecientes a dominios tan distintos como la vigilancia costera y la navegación de vehículos en entornos urbanos. El análisis de resultados incluye experimentos concretos que demuestran la validez de los prototipos implementados, así como el beneficio de usar información contextual para mejorar los procesos de fusión de sensores.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Javier Bajo Pérez.- Secretario: Antonio Berlanga de Jesús.- Vocal: Lauro Snidar