Prediction of Machines Health with Application of an Intelligent Approach – a Mining Machinery Case Study

2005 ◽  
Vol 293-294 ◽  
pp. 661-668 ◽  
Author(s):  
Mariusz Gibiec
Keyword(s):  
Past History ◽  
Predictive Maintenance ◽  
Intelligent Maintenance ◽  
Machine Performance ◽  
Future Performance ◽  
Machine Productivity ◽  
Mining Machinery ◽  
The Moment ◽  
Intelligent Approach

Machine field services depend on sensor-driven management systems that provide alerts, alarms and indicators. At the moment the alarm is sounded, it’s sometimes too late to prevent the failure. There is no alert provided that looks at degradation over time. If we could monitor degradation, then we would forecast upcoming situations, and perform maintenance tasks when necessary. In our research we chose to focus on intelligent maintenance system, which is defined as the prediction and forecast of equipment performance. Predictive maintenance, on the other hand, focuses on machine performance features. Data come from two sources: sensors mounted on the machine to gather the machine feature information, and information from the entire manufacturing system, including machine productivity, past history and trending. By correlating data from these sources — current and historical — predictions can be made about future performance. In this article case study of coal mining machinery health prediction is presented. Health of water pumping unit was considered. Such units placed in old mine shafts are crucial to avoid flooding working ones. As an effect of predictive maintenance it can be possible to improve safety and reduce costs incurred from accidents.

scholarly journals An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402091920 ◽  
Author(s):  
Ebru Turanoglu Bekar ◽  
Per Nyqvist ◽  
Anders Skoogh
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Industrial Case Study ◽  
Intelligent Approach

Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance.

A Machine for Feeling: Ping's Posthuman Affect

2017 ◽  
Vol 26 (1) ◽  
pp. 87-102
Author(s):  
Alys Moody
Keyword(s):  
Twentieth Century ◽  
Scholarly Work ◽  
Active Involvement ◽  
Affective Quality ◽  
Contemporary Perspective ◽  
The Moment ◽  
Natural Emotion ◽  
Archival Quality ◽  
Historical Moment

Beckett's famous claim that his writing seeks to ‘work on the nerves of the audience, not the intellect’ points to the centrality of affect in his work. But while his writing's affective quality is widely acknowledged by readers of his work, its refusal of intellect has made it difficult to take fully into account in scholarly work on Beckett. Taking Beckett's 1967 short prose text Ping as a case study, this essay is an attempt to take the affective qualities of Beckett's writing seriously and to consider the implications of his affectively dense writing for his texts’ relationship to history. I argue that Ping's affect emerges from the rhythms of its prose, producing a highly ‘speakable’ text in which affect precedes interpretation. In Ping, however, this affective rhythmic patterning is portrayed as mechanical, the product of the machinic ‘ping’ that punctuates the text and the text's own mechanical rhythms, demanding the active involvement of the reader. The essay concludes by arguing that Ping's mechanised affect is a specifically historical feeling. Arising from a specifically twentieth-century anxiety about technology's tendency to evacuate ‘natural’ emotion in favour of inhuman affect, it participates in a tradition of affectively resonant but curiously blank or indifferent performances of cyborg embodiment. Read in this historical light, Ping's implication of the reader in the production of its mechanised affect grants it, from our contemporary perspective, an archival quality. At the same time, it asks us to broaden the way in which we understand the Beckettian text's relationship to history, pointing to the existence of a more complex and recursive relationship between literature, its historical moment, and our contemporary moment of reading. Such a post-archival historicism sees texts as generated by but not bound to their historical moments of composition, and understands the moment of reception as an integral, if shifting, part of the text's history.

scholarly journals Intelligent Approach for Improvement of BIPV Systems Performance: Case Study

2019 ◽  
Vol 471 ◽  
pp. 112015
Author(s):  
Dan Craciunescu ◽  
Laurentiu Fara ◽  
Ana-Maria Dabija ◽  
Paul Sterian ◽  
Silvian Fara
Keyword(s):  
Intelligent Approach

scholarly journals APPLICATION OF DOMAIN INTEGRATED DESIGN METHODOLOGY FOR BIO-INSPIRED DESIGN- A CASE STUDY OF SUTURE PIN DESIGN

2021 ◽  
Vol 1 ◽  
pp. 487-496
Author(s):  
Pavan Tejaswi Velivela ◽  
Nikita Letov ◽  
Yuan Liu ◽  
Yaoyao Fiona Zhao
Keyword(s):  
Cross Sections ◽  
Design Methodology ◽  
Reaction Force ◽  
Integrated Design ◽  
Total Deformation ◽  
Insertion Force ◽  
Force Reaction ◽  
The Moment ◽  
Work Done

AbstractThis paper investigates the design and development of bio-inspired suture pins that would reduce the insertion force and thereby reducing the pain in the patients. Inspired by kingfisher's beak and porcupine quills, the conceptual design of the suture pin is developed by using a unique ideation methodology that is proposed in this research. The methodology is named as Domain Integrated Design, which involves in classifying bio-inspired structures into various domains. There is little work done on such bio-inspired multifunctional aspect. In this research we have categorized the vast biological functionalities into domains namely, cellular structures, shapes, cross-sections, and surfaces. Multi-functional bio-inspired structures are designed by combining different domains. In this research, the hypothesis is verified by simulating the total deformation of tissue and the needle at the moment of puncture. The results show that the bio-inspired suture pin has a low deformation on the tissue at higher velocities at the puncture point and low deformation in its own structure when an axial force (reaction force) is applied to its tip. This makes the design stiff and thus require less force of insertion.

scholarly journals The Role of Industry 4.0 and BPMN in the Arise of Condition-Based and Predictive Maintenance: A Case Study in the Automotive Industry

2021 ◽  
Vol 11 (8) ◽  
pp. 3438
Author(s):  
Jorge Fernandes ◽  
João Reis ◽  
Nuno Melão ◽  
Leonor Teixeira ◽  
Marlene Amorim
Keyword(s):  
Business Process ◽  
Automotive Industry ◽  
Industry 4.0 ◽  
Process Model ◽  
Business Process Management ◽  
Process Management ◽  
Predictive Maintenance ◽  
The Evolution Of Industry

This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.

scholarly journals Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1044
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Predictive Maintenance ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Building Maintenance ◽  
Collection Data ◽  
Iot Devices

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

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