Methodological Advancements in Continual Learning and Industry 4.0 Applications

La quarta rivoluzione industriale, nota anche come Industria 4.0, si basa su una varietà di tecnologie, tra cui Artificial Intelligence, Internet of Things, Cloud Computing, Robotics, e Big Data Analytics. L'obiettivo finale è migliorare l'efficienza, la produttività e la flessibilità. Ad esempio, Quality Control permette di scoprire ed eliminare i prodotti difettosi. Con la Predictive Maintenance è invece possibile prevedere il prossimo guasto dei macchinari e programmare in anticipo la manutenzione. L'Industria 4.0 ha molte sfide che saranno discusse e affrontate, come l'Interpretabilità. In particolare, una sfida significativa nell'Industria 4.0 è quando il processo di produzione non è statico ma dinamico. L'attuale impostazione DL classica non può far fronte ad ambienti in cambiamento, a meno che non venga eseguito un riaddestramento con tutti i dati, il quale si traduce in costi e tempi di addestramento significativi per aggiornare il modello. Una soluzione è lo studio ed impiego della disciplina nota come Continual Learning, consente ai modelli ML di essere aggiornati e di accrescere la loro knowledge nel tempo con un consumo ridotto di calcolo computazionale e memoria, riducendo i costi associati alla mantenimento del modello di machine learning. Di conseguenza, ci concentriamo sulle tecniche CL che possono essere applicate nel campo di Industria 4.0. Molti problemi industriali, come Anomaly Detection e Multi-Label Classification, sono al di fuori dei problemi di classificazione convenzionali studiati nel Continual Learning come verrà presentato nel testo seguente, quindi poca ricerca è stata condotta. CL ha il potenziale per migliorare le prestazioni dei modelli di machine learning in ambito industriale, rendendoli più adattabili a nuovi ambienti e aggiornandoli con un minore utilizzo delle risorse rispetto a quanto sarebbe altrimenti richiesto.The Fourth Industrial Revolution, also known as Industry 4.0, is built on a variety of technologies, including Artificial Intelligence, the Internet of Things, Cloud Computing, Robotics, and Big Data Analytics. The ultimate goal is to improve efficiency, productivity, and flexibility. For example, Quality Control allows to discover and eliminate defective products. Instead, with Predictive Maintenance is possible to predict the equipment's next failure and schedule maintenance in advance. Industry 4.0 has many challenges that will be discussed and faced, like Interpretability. In particular, a significant challenge in Industry 4.0 is when the manufacturing process is not static but dynamic. The current classic DL setting cannot cope with changing environments unless it is performed a retraining from scratch with all data, which ultimately results in significant costs and training time to update the model. Continual Learning, on the other hand, allows ML models to be updated and grow their knowledge over time with minimum computation and memory overhead, lowering the costs associated with machine learning model maintenance. As a result, we concentrate on CL techniques that can be applied in an Industry 4.0 field. Many industrial problems, such as Anomaly Detection and Multi-Label Classification, are outside of the conventional classification problems studied in Continual Learning, as presented in the following text, therefore little research has been conducted. Finally, CL has the potential to improve the performance of industrial machine learning models, making them more adaptable to new environments while also updating them with less resource utilization than would otherwise be required

Alarm logs of industrial packaging machines

The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Even though not all pieces of equipment are equipped with sensors yet, usually most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced. This data can be used to address the following tasks: 1. Next alarm forecasting: this problem can be framed as a supervised multi-class classification task, or a binary classification task when a specific alarm code is considered. 2. Predicting alarms occurring in a future time frame: here the goal is to forecast the occurrence of certain alarm types in a future time window. Since many alarms can occur, this is a supervised multi-label classification. 3. Future alarm sequence prediction: here the goal is predicting an ordered sequence of future alarms, in a sequence-to-sequence forecasting scenario. 4. Anomaly Detection: the task is to detect abnormal equipment conditions, based on the pattern of alarms sequence. This task can be either unsupervised, if only the input sequence is considered, or supervised if future alarms are taken into account to assess whether or not there is an anomaly. All of the above tasks can also be studied from a continual learning perspective. Indeed, information about the serial code of the specific piece of equipment can be used to train the model; however, a scalable model should also be easy to apply to new machines, without the need of a new training from scratch

VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing

In this paper, we present the project “VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing” by providing details on its research themes and its scientific and technological output. The project, centered on virtualization and remotization in the industrial sector, was promoted by Regione Veneto in Italy, and it has seen the participation and collaboration of 3 universities, 1 public research entity, and 10 companies composed of end users of digital solutions and high knowledge-intensive service providers. The project aims to develop and use tools for the virtualization of processes, systems, resources, and remoting of operations in order to: (1) maximize the efficiency of manufacturing systems under normal operating conditions; (2) maintain operations in case of emergency situations; (3) facilitate the restart of operations downstream of emergency situations by ensuring flexibility and predictive capability. Each theoretical proposal has been validated in distinct industrial facilities by constructing ten different prototypes

Presente/i e Futuro/i

Recuperare i significati racchiusi nella figura di Atlante, colui che porta su di sé il “peso” del “globo del mondo” (quindi del “tutto”), la responsabilità della sua interpretazione/comprensione, delle possibilità che deriveranno da quest’ultima e dei “sì” e dei “no” che determineranno il corso della storia: questa è la cornice tematica di Presente/i e Futuro/i, terzo volume del Centro “Ricerche di Gnoseologia e Metafisica”, una riflessione corale non solo e non tanto sul “presente” e sul “futuro” in sé e per sé, ma, soprattutto (come già il titolo dichiara con la scelta di entrambe le desinenze, singolare e plurale: [Present]e/i e [Futur]o/i), su una molteplicità di argomenti che costituiscono, ciascuno a suo modo, punti di snodo, nel presente, per diverse situazioni future e in cui si palesa il ruolo del soggetto (come singolo e come collettività, nelle sue varie conformazioni e organizzazioni) come “prisma di rifrazione” (dove la rifrazione sta, metaforicamente, per la capacità di scomporre, reinterpretare e generare). Le questioni trattate, nella loro varietà, mirano intenzionalmente a riflettere il più pienamente possibile la polifonia degli interessi delle/dei partecipanti e vanno da quelle più speculative (teoretiche, morali ed estetiche), a quelle storico-filosofiche, fino a quelle connesse con la presente situazione storica e le sue concrete difficoltà.Rediscover the meanings implied in the figure of Atlas, the one who carries the "weight" of the "globe of the world" (therefore of the "whole"), the responsibility of the interpretation/understanding of the real, of the possibilities that will derive from it, and of the "yes" and "no" that will determine the course of the history: this is the thematic framework of Present/s and Future/s, third volume of the "Researches of Gnoseology and Metaphysics" Centre, a choral reflection not only (and not so much) on the "Present" and the "Future" in themselves, but, above all (as the title already shows with the choice of both endings, singular and plural), on a multiplicity of topics that constitute, each in their different ways, turning points, in the present, for different future situations, where the role of the subject is revealed (as an individual and as a community, in its various conformations and organizations) as a "prism of refraction ”(where refraction stands, metaphorically, for the ability to deconstruct, reinterpret, and generate). The topics, in their variety, intentionally aim to reflect, as fully as possible, the polyphony of the interests of the participants and range from the most speculative ones (theoretical, moral, and aesthetic), to the historical-philosophical ones, up to those connected with the present historical situation and its difficulties