Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoT-Architecture and Machine Learning

Straus, Patrick; Schmitz, Markus; Wöstmann, René; Deuse, Jochen

doi:10.1109/bigdata.2018.8622076

Cited by 59 publications

(55 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For Instance, Cattaneo and Macchi [16] have retrofitted an old drilling machine realizing a DT for estimate Remaining Useful Life; Herwan et al [17] and Hesser et al [18] used artificial neural networks (ANN) for detecting the tool wear in a CNC machine after retrofitting; the latter show how the ANNs give better results than support vector machine (SVM) and k-nearest neighbors (KNN) models in tools wear prediction. Strauß et al [19] have retrofitted a heavy lift Electric Monorail System (EMS) at the BMW Group with a low-cost sensor and have used machine learning algorithms for predictive maintenance; this work shows how supervised models, such as the ANNs, are the best choice when labeled fault data are available. Supervised ANNs cannot be used without data, and they also have the disadvantage of overfitting, which tends to make the model adapt to a specific and not general behavior of the system.…”

Section: Literature Reviewmentioning

confidence: 99%

Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance

2021

View full text Add to dashboard Cite

The transformation from traditional industry to Industry 4.0 can bring many benefits in various spheres, from efficiency to safety. However, this transition involves adopting technologically advanced machinery with a high level of digitization and communication. The costs and time to replace obsolete machines could be unsustainable for many companies while retrofitting the old machinery. To make them ready to the Industry 4.0 context, they may represent an alternative to the replacement. Even if there are many studies related to retrofitting applied to machinery, there are very few studies related to the literature process industry sector. In this work, we propose a case study of a two-phase mixing plant that needed to be enhanced in the safety and maintainability conditions with reasonable times and costs. In this regard, the Digital Twin techniques and Deep Learning algorithms will be tested to predict and detect future faults, not only already visible and existing malfunctions. This approach strength is that, with limited investments and reasonable times, it allows the transformation of an old plant into a smart plant capable of communicating quickly with operators to increase its safety and maintainability.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance

2021

View full text Add to dashboard Cite

show abstract

“…After the data has been collected, the next step involves data cleansing in order to remove, for instance, sensor errors that may affect the data analysis. Often data are subjected to a pre-processing step where the volume of data is reduced (i.e., aggregation) to pass only the selected and extracted indicators (i.e., feature extraction) [31] to the forecasting and/or decision-making algorithms. The techniques adopted to process and analyze data mainly depend on both the types of data collected and the algorithms used to reveal the condition of the machine.…”

Section: Data Processingmentioning

confidence: 99%

“…Hashemian and Bean [ 28 ] identify three major categories for predictive maintenance depending on both the kind of information acquired and the source adopted. Most of the time, old equipment requires the additions of new sensors [ 31 ] and the need for new tools for monitoring has made the adoption of PdM quite expensive in the past [ 6 , 27 ]. Nowadays, new technologies in the field of data acquisitions, like the IoT and sensor technology, make the adoption of PdM solutions more accessible [ 28 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the time, old equipment requires the additions of new sensors [ 31 ] and the need for new tools for monitoring has made the adoption of PdM quite expensive in the past [ 6 , 27 ]. Nowadays, new technologies in the field of data acquisitions, like the IoT and sensor technology, make the adoption of PdM solutions more accessible [ 28 , 31 , 32 ]. Anyway, often the brownfield already contains smart equipment providing more information than just basic data over intelligent communication protocols, but systems from different vendors may run in different parts of the production process and a way to allow them to understand the information exchanged must be provided (e.g., gateways).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Model for Predictive Maintenance Based on Asset Administration Shell

Cavalieri

Salafia

2020

Sensors

View full text Add to dashboard Cite

Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance.

show abstract

“…In industrial production, ML methods can be used to overcome existing problems and boundaries in a number of application scenarios along the production process chain. Successful applications can be found for various tasks in production planning and optimization [9], quality improvement and prediction [10,11], predictive maintenance [12] and energy efficiency optimization [13]. The data used ranges from master data, over transaction, log and sensor data to text, voice, video and audio data [14].…”

Section: Machine Learning In Productionmentioning

confidence: 99%

Industrial Data Science - Interdisciplinary Competence for Machine Learning in Industrial Production

Schulte

Schmitt

Stankiewicz

et al. 2020

Annals of Scientific Society for Assembly, Handling and Industrial Robotics

Self Cite

View full text Add to dashboard Cite

Within the increasing digitalization, the widespread application of modern information and communication technologies and the technological ability to systematically and comprehensively capture and store data allow to build data storages of unprecedented size and quality. The evaluation and efficient use of the implicit knowledge in the data to support decision-making processes is becoming increasingly significant in manufacturing companies. Thus, new requirements arise for the qualification and competence development to efficiently solve engineering applications and issues in manufacturing and assembly with advanced data-driven methods. This paper presents the contribution of a qualification concept for Machine Learning in industrial production that has been realised within a recent research project funded by the Federal Ministry of Education and Research. This concept has been designed and validated within the university curriculum for graduate students in mechanical and industrial engineering, computer science and statistics. Taking into account the current challenges in manufacturing and assembly, the contribution of this enhanced interdisciplinary competence development can be considered quite significant. The results, findings, and future enhancements are presented within this paper.

show abstract

Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoT-Architecture and Machine Learning

Cited by 59 publications

References 37 publications

Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance

Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance

A Model for Predictive Maintenance Based on Asset Administration Shell

Industrial Data Science - Interdisciplinary Competence for Machine Learning in Industrial Production

Contact Info

Product

Resources

About