Interactive visualization for NILM in large buildings using non-negative matrix factorization

Artículo publicado en abierto mediante APC Elsevier Open AccessNon-intrusive load monitoring (NILM) techniques have recently attracted much interest, since they allow to obtain latent patterns from power demand data in buildings, revealing useful information to the expert user. Unsupervised methods are specially attractive, since they do not require labeled datasets. Particularly, non-negative matrix factorization (NMF) methods decompose a single power demand measurement over a certain time period into a set of components or “parts” that are sparse, non-negative and sum up the original measured quantity. Such components reveal hidden temporal patterns which may be difficult to interpret in complex systems such as large buildings. We suggest to integrate the knowledge of the user into the analysis in order to recognize the real events inside the electric network behind the learnt patterns. In this paper, we integrate the available domain knowledge of the user by means of a visual analytics web application in which an expert user can interact in a fluid way with the NMF outcome through visual approaches such as barcharts, heatmaps or calendars. Our approach is tested with real electric power demand data from a hospital complex, showing how the interpretation of the decomposition is improved by means of interactive data cube visualizations, in which the user can insightfully relate the NMF components to characteristic demand patterns of the hospital such as those derived from human activity, as well as to inefficient behaviors of the largest systems in the hospita

Interactive data visualization of chatter conditions in a cold rolling mill

Artículo publicado en abierto mediante APC Elsevier Open AccessRolling of flat steel products is an industrial process in the field of metalworking where two or more pairs of rolls reduce the thickness of a steel strip to produce a uniform thickness material. Despite it has been studied for many years, there are still unpredictable problems that can affect the final quality of the product. One of them is the so-called chatter, that is a powerful self-excited vibration that appears suddenly and limits the productivity of the process. In this paper, a visual analytics approach is considered for exploratory analysis in order to discover and understand the factors and conditions under which chatter appears. An interactive web-based interface is presented here which allows the user to explore a map of dynamical conditions and visualize relevant details of each chatter onset. A validation case is performed using real data where normal/fault conditions have been identified automatically. By means of interactive exploration, the tool allows to refine an automatic chatter detection method. Moreover, it is shown to reveal correlations between variables, providing in some expected cases data-based confirmation, but also revealing less obvious relationships. Finally, it provides context, allowing to carry out comparative analysis, both qualitative and quantitative, for different subsets of coils (e.g. different years) as well as for different working condition

Multiple factor analysis for time-varying two-mode networks

Most social networks present complex structures. They can be both multi-modal and multi-relational. In addition, each relationship can be observed across time occasions. Relational data observed in such conditions can be organized into multidimensional arrays and statistical methods from the theory of multiway data analysis may be exploited to reveal the underlying data structure. In this paper, we adopt an exploratory data analysis point of view, and we present a procedure based on multiple factor analysis and multiple correspondence analysis to deal with time-varying two-mode networks. This procedure allows us to create static displays in order to explore network evolutions and to visually analyze the degree of similarity of actor/event network profiles over time while preserving the different statuses of the two modes

Aluminium Process Fault Detection and Diagnosis

The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments

LASAGNA PLOTS: A SAUCY ALTERNATIVE TO SPAGHETTI PLOTS

Longitudinal repeated measures data has often been visualized with spaghetti plots for continuous out- comes. For large datasets, this often leads to over-plotting and consequential obscuring of trends in the data. This is primarily due to overlapping of trajectories. Here, we suggest a framework called lasagna plot ting that constrains the subject-specific trajectories to prevent overlapping and utilizes gradients of color to depict the outcome. Dynamic sorting and visualization is demonstrated as an exploratory data analysis tool. Supplemental material in the form of sample R code additional illustrated examples are available online

Symmetrical and Non-Symmetrical Variants of Three-Way Correspondence Analysis for Ordered Variables

In the framework of multi-way data analysis, this paper presents symmetrical and non-symmetrical variants of three-way correspondence analysis that are suitable when a three-way contingency table is constructed from ordinal variables. In particular, such variables may be modelled using general recurrence formulae to generate orthogonal polynomial vectors instead of singular vectors coming from one of the possible three-way extensions of the singular value decomposition. As we shall see, these polynomials, that until now have been used to decompose two-way contingency tables with ordered variables, also constitute an alternative orthogonal basis for modelling symmetrical, non-symmetrical associations and predictabilities in three-way contingency tables. Consequences with respect to modelling and graphing will be highlighted

Process Monitoring and Data Mining with Chemical Process Historical Databases

Modern chemical plants have distributed control systems (DCS) that handle normal operations and quality control. However, the DCS cannot compensate for fault events such as fouling or equipment failures. When faults occur, human operators must rapidly assess the situation, determine causes, and take corrective action, a challenging task further complicated by the sheer number of sensors. This information overload as well as measurement noise can hide information critical to diagnosing and fixing faults. Process monitoring algorithms can highlight key trends in data and detect faults faster, reducing or even preventing the damage that faults can cause. This research improves tools for process monitoring on different chemical processes. Previously successful monitoring methods based on statistics can fail on non-linear processes and processes with multiple operating states. To address these challenges, we develop a process monitoring technique based on multiple self-organizing maps (MSOM) and apply it in industrial case studies including a simulated plant and a batch reactor. We also use standard SOM to detect a novel event in a separation tower and produce contribution plots which help isolate the causes of the event. Another key challenge to any engineer designing a process monitoring system is that implementing most algorithms requires data organized into “normal” and “faulty”; however, data from faulty operations can be difficult to locate in databases storing months or years of operations. To assist in identifying faulty data, we apply data mining algorithms from computer science and compare how they cluster chemical process data from normal and faulty conditions. We identify several techniques which successfully duplicated normal and faulty labels from expert knowledge and introduce a process data mining software tool to make analysis simpler for practitioners. The research in this dissertation enhances chemical process monitoring tasks. MSOM-based process monitoring improves upon standard process monitoring algorithms in fault identification and diagnosis tasks. The data mining research reduces a crucial barrier to the implementation of monitoring algorithms. The enhanced monitoring introduced can help engineers develop effective and scalable process monitoring systems to improve plant safety and reduce losses from fault events

My multiway analysis: From Jan de Leeuw to TWPack and back

Development Psychopathology in context: famil

Monitoring the waste to energy plant using the latest AI methods and tools

Solid wastes for instance, municipal and industrial wastes present great environmental concerns and challenges all over the world. This has led to development of innovative waste-to-energy process technologies capable of handling different waste materials in a more sustainable and energy efficient manner. However, like in many other complex industrial process operations, waste-to-energy plants would require sophisticated process monitoring systems in order to realize very high overall plant efficiencies. Conventional data-driven statistical methods which include principal component analysis, partial least squares, multivariable linear regression and so forth, are normally applied in process monitoring. But recently, latest artificial intelligence (AI) methods in particular deep learning algorithms have demostrated remarkable performances in several important areas such as machine vision, natural language processing and pattern recognition. The new AI algorithms have gained increasing attention from the process industrial applications for instance in areas such as predictive product quality control and machine health monitoring. Moreover, the availability of big-data processing tools and cloud computing technologies further support the use of deep learning based algorithms for process monitoring. In this work, a process monitoring scheme based on the state-of-the-art artificial intelligence methods and cloud computing platforms is proposed for a waste-to-energy industrial use case. The monitoring scheme supports use of latest AI methods, laveraging big-data processing tools and taking advantage of available cloud computing platforms. Deep learning algorithms are able to describe non-linear, dynamic and high demensionality systems better than most conventional data-based process monitoring methods. Moreover, deep learning based methods are best suited for big-data analytics unlike traditional statistical machine learning methods which are less efficient. Furthermore, the proposed monitoring scheme emphasizes real-time process monitoring in addition to offline data analysis. To achieve this the monitoring scheme proposes use of big-data analytics software frameworks and tools such as Microsoft Azure stream analytics, Apache storm, Apache Spark, Hadoop and many others. The availability of open source in addition to proprietary cloud computing platforms, AI and big-data software tools, all support the realization of the proposed monitoring scheme