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Predictive diagnostics through machine learning on the injection group of a diecasting machine.
No full textL'analisi dei big data ha sempre più preso un ruolo rilevante nello scenario industriale degli ultimi decenni. Il "Data Lake" rappresenta una nuova frontiera in data science. Non si vuole solo immagazzinare dati, ma analizzarli con il fine di applicare procedure correttive in tempo per poter evitare stop di produzione e far crescere la produttività di un'azienda.
Ogni campo della catena produttiva è importante per poter crescere la produttività. In particolare, la manutenzione è uno dei tasks più importanti di cui tenere in considerazione per queste analisi. Infatti, i costi dovuti alla manutenzione sono la maggior parte dei costi totali di un impianto di produzione. Questi costi devono essere ridotti mediante diverse strategie.
La nuova frontiera nella strategia di manutenzione è rappresentata dalla Manutenzione Predittiva (PM) o anche definita, Predictive Health Management (PHM). La PHM è una strategia di manutenzione dove vengono applicati algoritmi statistici o algoritmi di machine learning per ottenere la "Remaining Useful Life" (RUL) di un componente. Questo progetto è focalizzato nell'applicazione della PHM nel gruppo iniezione di una macchina di pressocolata. Per sua definizione, il processo di pressocolata ad alta pressione (HPDC) presenta aspetti differenti che possono andare ad inficiare sull'analisi dei dati. Per esempio, il malfunzionamento di un componente è un evento raro e l'analisi non può essere eseguita andando ad investigare ampi dataset, oppure investigando i dati di fault basandoci sui registri di manutenzione di un'azienda. Questo rende difficoltoso indentificare condizioni di fault dei componenti utilizzando i tradizionali algoritmi di machine learning. Un ulteriore problema legato con il processo di HPDC sta nel frequente cambio di produzione, che porta a cambiamenti radicali nei parametri di processo. Inoltre, può capitare che le aziende di piccole dimensioni non facciano il corretto update dei dati di produzione (cambio di stampo, cambio ricetta nell'iniezione).
Per risolvere queste problematiche, viene proposto un nuovo metodo per determinare le condizioni di fault di componenti in una macchina di pressocolata. Il metodo proposto è in grado di determinare automaticamente un cambio di produzione e resettare il dataset utilizzato per il training. Il metodo si basa sulla peculiarità del processo di pressocolata di avere differenti fasi che risultano essere eguali per ogni tipo di macchina e produzione. Queste fasi sono, l'avanzamento lento del pistone per l'iniezione in modo da evitare bolle d'aria nella camera di iniezione, l'avanzamento veloce del pistone con il completo riempimento dello stampo, e la fase di moltiplica dando una maggiore pressione al processo di pressofusione. Questo aumento di pressione serve per compensare il ritiro del materiale dovuto al raffreddamento dopo l'iniezione.
Ogni fase viene interpolata in modo da estrarre parametri significativi per la futura predizione del fault. Per ogni parametro, viene calcolato uno stimatore dell'incertezza, che viene combinato con l'incertezza della strumentazione per ottenere un'incertezza estesa che tenga in considerazione dei due contributi. Il core di questo metodo si concretizza proprio nel calcolo della metrica finale per poter monitorare lo stato di salute di un componente. Infatti, il metodo si basa sulla combinazione della classica analisi statistica con delle matrici peso date dagli esperti del settore della manutenzione di questa tipologia di macchina. Il risultato finale è l'Health Index (HI) che rappresenta la probabilità di avere una condizione di fault per la macchina di pressocolata. Ogni matrice peso che viene combinata con i parametri estratti si traduce in un HI per quel componente. In questo modo, è possibile creare tanti Heath Index possibili utilizzando una specifica matrice peso, che è possibile costruire mediante le interviste degli esperti.In the last decades, data analysis becomes relevant in the industrial scenario. The data lake represents the new frontier in the data science. The new concept is not only the data storage anymore, but the possibility to analyse the historical data in order to optimize the production by finding bottle necks in the production chain and solving the problem by applying corrective procedures to increase the productivity of a company.
Every field in the production chain is important to increase the productivity. Maintenance is one of the most important tasks to take into in account. Indeed, maintenance costs are a major part of the total operating costs of all manufacturing or production plants. These costs must be reduced by applying different strategies.
The new frontier in the maintenance strategy is represented by the Predictive Maintenance (PM) or Predictive Health Management (PHM). PHM is a maintenance strategy in which different statistical algorithms or machine learning algorithms can be applied to obtain the Remaining Useful Life (RUL) of a component.
This project is focused on the application of the PHM on an injection group of a die casting machine. By this own definition, the High Pressure Die Casting (HPDC) process presents different aspects that can affect the analysis. For instance, the fault of components is a rare event, and the analysis cannot be performed by investigating large datasets or fault data based on maintenance records. This makes very difficult to detect the fault of components with traditional machine learning algorithms. A further problem, however, linked with HPDC process is in the frequent change in production, which leads to changes in the process parameters. Moreover, sometimes small companies do not correctly update the production identifiers. To solve these problems, a new method is proposed to detect the fault of components in a diecasting machine. The proposed method automatically detects a production change and resets each time the dataset used for training.
The method is based on the peculiarity of the die casting process that presents different phases equal to each machine and production considered. These phases are the slow motion of the piston to avoid air bubbles inside the injection chamber, the stroke with the filling of the die, and the multiplication phase to compensate the shrinkage of the material due to the cooling by giving more pressure in the process. Each phase is interpolated to extract sensitive parameters to perform the prediction of fault. For each parameter, an uncertainty estimator is recorded and combined with the uncertainty of the instrumentation to obtain an uncertainty that considers the two contributions.
The core of this method is in the combination of the classical prediction analysis with a weighing matrix given by the experts. The weights are determined in a series of formal interviews for each phase and quantity recorded. The result is the Health Index (HI) representing the probability of different types of faults in the diecasting machine. Each weighing matrix combined with the parameters extracted is a HI for that component and it is possible to create how many HIs as possible by using a proper weighing matrix that can be constructed through the interview of the experts
A flexible method to detect the fault of components in an injection group of a diecasting machine
No full textThis work presents a simple method to detect the fault of components in an injection group of a diecasting machine. A usual problem in predictive diagnostics in industrial applications is the lack of replicable failure and fatal data: the parameters of the diecasting process are often changed to adapt the machine to a new production cycle and this makes difficult to identify faults using automated data analysis. The proposed method is based on an algorithm able to re-train itself when a change in production is detected. The final prediction of each fault condition is performed by combining classical machine learning analysis and experts' knowledge of the field by identifying discriminant weights to insert in the machine learning analysis. These weights are quantities that represent the experts' knowledge and the algorithm take into account
Effects of full-stops on shoe-braked railway wheel wear damage
No full textThe purpose of this work is to gain a better understanding of the complex damage phenomena taking place at the railway wheel/brake block interface due to thermo-mechanical loading. Initially, full-stop braking was studied using Finite Element (FE) simulations to estimate the temperature reached in the wheel rim. Experiments to reproduce wheel damage were conducted with a two-disc machine using test conditions that were based on the results of the FE simulations. Three different wheel steels were tested against the same cast iron shoe material. The evolution of the wheel disc damage was studied at various numbers of cycles under fixed contact pressure and sliding speed. The friction coefficient and the temperature on the wheel disc surface were measured during the tests. At the end of the experiments, the wheel disc was examined and characterized. Cross-sections were observed with an optical microscope and the hardness was measured as a function of the depth to investigate the damage mechanisms that occurred at surface and subsurface. Material transfer from the shoe specimen to the wheel specimen results in the formation of a discontinuous “third body” layer, and that layer plays a key role in the evolution of the wheel disc damage. When the transferred layer of brake material is worn away, detachment of steel from the wheel disc surface occurs, probably promoting the crack nucleation. In addition, wear debris from both disc materials promotes three-body abrasive wear of the wheel disc surface
Study of the damage induced by thermomechanical load in ER7 tread braked railway wheels
No full textThis work aims to better understand the complex damage phenomena taking place at the wheel/brake block interface due to the thermomechanical load. An experimental procedure, articulated in three series of tests carried out with a bi-disc machine, was designed in order to experimentally simulate in controlled laboratory conditions the thermomechanical history of the real wheel during stop braking. The first series of tests was performed on ER7 wheel steel discs paired with cast iron shoe material discs, setting the sliding speed and the contact load in such a way to generate the heat flux needed to reproduce the typical tread temperature of a real wheel in stop braking. The second series was carried out by repeating the tests in the conditions of the first series and subsequently subjecting the tested wheel specimens to rolling/sliding contact with discs of 350HT rail steel. The third series was carried out by repeating the two phases of the second series and subsequently adding water to the contact interface of the wheel-rail specimens. Measurements of friction coefficient, surface temperature and weight changes were carried out during the tests. At the end, cross-sections of the specimens were observed with an optical microscope. The hardness along the depth was measured. It was observed that during the braking phase parts of the wheel specimen surface are coated by a discontinuous layer of cast iron that is transferred from the brake block specimens. During the braking phase and the subsequent phase of dry contact with the rail specimen, the transferred material is removed, promoting the nucleation of surface cracks; in addition, surface cracks are generated also by ratcheting due to high friction. During the subsequent wet contact phase, these cracks propagate in the wheel disc due to the pressurization of the fluid entrapped inside the cracks. The propagation of surface cracks in wet contact was assessed by a fracture mechanics approach, including the Finite Element simulation of a surface crack with entrapped fluid. The stress intensity factor range during a load pass was calculated and compared with the propagation threshold of the ER7 steel, determining this way the critical depth of surface cracks. This study is a step towards a damage tolerant approach for the designing and maintaining tread-braked wheels
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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