IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0
Introduction
With the advancements in the Information and Communication Technology field and the exponentially increasing volumes of data being generated every day, a new set of possibilities has been presented to improve the efficiency and the characteristics of production processes. Adding to this is the transformation from a saturated seller’s market into a customer-driven one, with its growing demand for highly customized products accompanied by decreasing product lifecycles and smaller lot sizes, pushing companies towards a paradigm shift in order to leverage their data to attain a business advantage in such a competitive and dynamic market [1].
As such, the currently ongoing 4th industrial revolution, usually referred to as Industry 4.0 in Europe [[2], [3], [4]] and Industrial Internet in the US [5], aims to introduce and take advantage of the interconnected world along the entire value chain, allowing the sharing and processing of the data available in all of the its actors to generate relevant knowledge and optimize the overall process. The adoption of the Industry 4.0 paradigm encompasses the following three characteristics [6]:
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“Horizontal integration across the entire value network”: By integrating the overall value chain it is possible to optimize it beginning with the suppliers, materials, logistics, etc. In this sense, all of the value chain’s actors must be connected and coordinated among each other based on their individual requirements, creating a very dynamic ecosystem.
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“End-to-end engineering across the entire product life-cycle”: The integration and digitalization across all phases of the product’s life-cycle is crucial to ensure that data can be collected, stored and processed to generate new knowledge from the product’s inception to its end of life. This knowledge can be particularly relevant for the product’s improvement, not only regarding its production, but also the for instance its design or material suppliers.
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“Vertical integration and networked manufacturing systems”: At the shop-floor, the integration among the different components and actors (such as resources, humans and Manufacturing Execution Systems) should be done through a Cyber-Physical System (CPS). This system will allow not only the internal integration and optimization but also a harmonized and smooth integration with the two previously presented functionalities.
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With the vertical integration emerges the concept of Smart Factory (SF). According to [7] these factories must have some characteristics including among others the capacity to deal with mass customization [4], flexibility [8] and new maintenance strategies [9]. However, one of the main characteristics is the ability to generate new and relevant knowledge based on data processing [10]. As previously stated, SFs are implemented and deployed via CPS. The concept of Cyber-Physical Production System (CPPS) [11] merges the functionalities and benefits of CPS applied to the industrial context. The main objective in a CPPS is to create an abstraction layer where each of the shop-floor’s actors is represented by a cyber entity, as shown in Fig. 1.
The communication between the heterogeneous components is now made at the cyber level, allowing a smooth and effective integration of all the components and actors avoiding the usual problems related to vendors’ specifications and standards. In [12] the authors present a comparison between today’s factories and the now emerging Industry 4.0 based SFs, implemented through CPPS. With all the resources integrated, sharing information and their behaviours among each other, the shop-floor can adapt and organize in runtime to optimize at different levels (production, maintenance, energy consumption, etc). Moreover, with the advances in the Industrial Internet of Things (IIoT) and the increasingly large number of sensors and other data sources available on the shop-floor, the amount of extracted data is growing and the traditional algorithms are no longer able to process these volumes of data. Hence, the big data analysis field is becoming more and more important in several areas as a way to tackle this challenge [13].
This is often coupled with the usage of Machine Learning (ML), allowing manufacturers to obtain insights regarding their factory which would have been otherwise missed. ML can be defined as a system’s capacity to improve its performance on a given task or set of tasks over time based on previous results [14]. Therefore, ML algorithms can be used to predict a system’s behaviour and/or improve its overall performance, enabling the development of tools capable of analysing data and perceive what are the underlying trends and correlations. Thus, ML-based approaches can be used to predict abnormal events (failures, degradation, energy consumption, etc), generate warnings and advise the system and/or the operator regarding which course of action to take, assisting in diagnosis and maintenance tasks [15].
In line with this, the work detailed in the coming sections aims to provide an integration of this real-time and historical analysis with self-improvement mechanisms under a single framework for Predictive Manufacturing Systems (PMS), along with an initial implementation of its core functionalities. It is structured as follows: Section 2 presents a summary of related work that can be found in current literature pertaining to CPPS-based PMS. Section 3 introduces the proposed framework and formalizes some core concepts, requirements and functionalities. Section 4 details the initial implementation of the framework and the integration of its modules. Afterwards, Section 5 describes the tests performed to validate the implemented solution, with the results being discussed in Section 6. This is followed by Section 7 in which a brief summary of the developments is presented, along with some conclusions and remarks regarding future work.
Section snippets
Related work
Over the last few years, significant efforts have been put into the research of the various facets of predictive manufacturing in Industry 4.0. In [16] the authors overview the recent advances and trends regarding CPPS and big data analysis, identifying self-predictiveness and self-awareness as key characteristics to gain insight into Industry 4.0 factories. Also, the authors mention that several sources of information in current prognostics methods remain untapped, such as peer-to-peer
The IDARTS framework
In this section, an overview of the proposed Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is provided along with its main design goals, requirements and a thorough description of each of its individual structural elements.
IDARTS targets not only the acquisition of data at different granularity levels, but also the realization of context-aware data analysis and evaluation based on both real-time and historical data. This analysis outputs predictive data, which in this
Implementation
An initial implementation was developed focusing on the integration of the IDARTS’ core elements. The main goal at this stage is to showcase an initial implementation of the data acquisition and pre-processing CPPS, the real-time processing module and its integration with a Knowledge Management tool, as well as all the data flow in between and its required interfaces. Each of these elements is described in the following subsections.
Testing and validation
This section details the steps taken during the testing and validation of the initial implementation described in Section 4. The tests were conducted with the goal of validating the main three non- functional requirements identified in the specification of the framework, namely scalability, flexibility and pluggability.
The testing environment consisted in the four-node cluster shown in Fig. 6, consisting on four machines running Core i7-4770 processors with 12GB of memory each.
One machine was
Discussion of results
The tests described in Section 5 aimed to verify the three main non-functional requirements described in the IDARTS specification, namely scalability, flexibility and pluggability.
Regarding the first set of tests pertaining to the scalability of the solution, the implementation was deployed on a four-node cluster and tested with varying data throughput rates. This rate started at around 750, then 1500, and finally 3000 data points per second, corresponding to 75, 150 and 300 virtual
Conclusion and future work
A generic framework was proposed, focused on the aspects of data analysis and real-time supervision of manufacturing systems. Being aligned with the Industry 4.0 vision, the framework aims to take advantage of the ongoing data explosion, presenting a scalable and flexible solution for predictive manufacturing, being as little invasive as possible. Its efficacy is however dependent on the availability, volume and quality of the data from the underlying production system.
The main contributions of
References (38)
- et al.
Opportunities of sustainable manufacturing in industry 4.0
Procedia CIRP
(2016) SmartFactory—towards a factory-of-things
Annu. Rev. Control
(2010)- et al.
Recent advances and trends in predictive manufacturing systems in big data environment
Manuf. Lett.
(2013) - et al.
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Manuf. Lett.
(2015) - et al.
Big Data and virtualization for manufacturing cyber-physical systems: a survey of the current status and future outlook
Comput. Ind.
(2016) - et al.
An overview of anomaly detection techniques: existing solutions and latest technological trends,”
Comput. Networks
(2007) - et al.
Monitoring of wind farms’ power curves using machine learning techniques
Appl. Energy
(2012) - et al.
Predictive analytics model for power consumption in manufacturing
Procedia CIRP
(2014) Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes
J. Process Control
(2018)A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing
Int. J. Ind. Manuf. Syst. Eng.
(2017)