1,721,083 research outputs found
Prospective ISO 22400 for the challenges of human-centered manufacturing
No full textThe standard definition of the key performance indicators for the manufacturing operations management level could not resist the incoming challenges and models of the 4th industrial revolution, which currently relies on the paradigms abiding by the cyber-physical system of systems vision. By means of a brief survey of the current trends, a set of recommendations are provided in order to open discussions for the foreseen evolutions of the ISO 22400 standard. A focus is made on the role of the humans in their increasingly closer collaboration with the pervasive autonomous intelligent entities in the context of a human-centered manufacturing. It is contended that there is still some room for a review of the definitions of the performance indicators and their structure that should aim to reflect human and intelligent automation aspects in an integrated framework
Fault Diagnosis in a belt-drive system under non-stationary conditions. An industrial case study
No full textFault Diagnosis (FD) is a fundamental step to estimate the Remaining Useful Life (RUL) of a component and then achieve a reliable Predictive Maintenance (PdM) system. The signal-based diagnosis methods received wide research attention since their implementation relies basically on the processing of signals. This is an interesting feature but at the same time it has some limitations. Indeed, the signal-based methodology are usually developed "ad hoc"for the particular application and do not show the same validity under different assumptions. In particular most of the applications deal with systems working in steady-state. Differently, in this paper, is proposed a solution to overcome limitations related to non-stationary conditions. The proposed approach is theoretically evaluated and then tested in an industrial packaging machinery. The results evidence the validity of the strategy to deal with signals acquired in systems which run continuously in non-stationary conditions
ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots
No full textFeatured Application: This work aimed to enhance the current state of the art regarding the Condition Monitoring of industrial collaborative manipulators providing a general architecture which can be used in many different industrial scenarios. This solution is helpful in overcoming current limits regarding the definition of algorithms for the automatic detection of failures in collaborative robots working on flexible manufacturing. The Condition Monitoring (CM) of industrial collaborative robots (cobots) has the potential to decrease downtimes in highly automated production systems. However, in such complex systems, defining a strategy for effective CM and automatically detecting failures is not straightforward. In this paper, common issues related to the application of CM to collaborative manipulators are first introduced, discussed, and then, a solution based on the Robot Operating System (ROS) is proposed. The content of this document is highly oriented towards applied research and the novelty of this work mainly lies in the proposed CM architecture, while the methodology chosen to assess the manipulator’s health is based on previous research content. The CM architecture developed and the relative strategy used to process data are useful for the definition of algorithms for the automatic detection of failures. The approach is based on data labeling and indexing and aims to extract comparable data units to easily detect possible failure. The end of this paper is provided with a proof of concept (PoC) applied to an industrial collaborative manipulator where the proposed CM strategy has been implemented and tested in a real application scenario. Finally, it is shown how the proposed methodology enables the possibility of defining standard Health Indicators (HIs) to detect joint anomalies using torque information even under a highly dynamic and non-stationary environmental conditions
Towards a formal model of computation for RMAS
Full text linkRMAS is a multi-agent system architecture and associated model of computation that has been recently proposed as promising framework for autonomic computation and as viable implementation means for industrial agents. RMAS is proposed as an effective tool in the realization of the full-fledged concept of cyber-physical systems of systems, in agreement with new visions of the digital future and prominent standardization efforts like the IEC 61499. Currently, there is the need to establish a solid formalism for the model of computation of RMAS in order to harness and fully express its possibilities. In particular, this model should bridge the disciplines of control and systems engineering and computer science, whilst concerning artificial intelligence. This work proposes a first operational step in this direction. A formalism of the RMAS model of computation is established for the first time, and a complete definition of hierarchical nested levels of RMAS units is proposed. This work constitutes the ground for industry-ready technological instances of the RMAS architecture
MNR structural investigation of the biotin-avidin complex: a 13C NMR paramagnetic relaxation stud
No full textAnomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot
Full text linkIndustrial collaborative robots (cobots) are known for their ability to operate in dynamic environments to perform many different tasks (since they can be easily reprogrammed). Due to their features, they are largely used in flexible manufacturing processes. Since fault diagnosis methods are generally applied to systems where the working conditions are bounded, problems arise when defining condition monitoring architecture, in terms of setting absolute criteria for fault analysis and interpreting the meanings of detected values since working conditions may vary. The same cobot can be easily programmed to accomplish more than three or four tasks in a single working day. The extreme versatility of their use complicates the definition of strategies for detecting abnormal behavior. This is because any variation in working conditions can result in a different distribution of the acquired data stream. This phenomenon can be viewed as concept drift (CD). CD is defined as the change in data distribution that occurs in dynamically changing and nonstationary systems. Therefore, in this work, we propose an unsupervised anomaly detection (UAD) method that is capable of operating under CD. This solution aims to identify data changes coming from different working conditions (the concept drift) or a system degradation (failure) and, at the same time, can distinguish between the two cases. Additionally, once a concept drift is detected, the model can be adapted to the new conditions, thereby avoiding misinterpretation of the data. This paper concludes with a proof of concept (POC) that tests the proposed method on an industrial collaborative robot
Responses of rat mesencephalic dopaminergic neurons to a prolonged period of oxygen deprivation.
No full textGoing 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
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