Making Sense of Manufacturing Data

Jon Kepa Gerrikagoitia, Gorka Unamuno and Anne Sanz

IK4-IDEKO, Arriaga Industrialdea 2, Elgoibar, Spain

Keywords: Industry 4.0, Smart Manufacturing, Digital Factory, Big Data Analytics, Data Science.

Abstract: A fast and successful digitation of the industry is meant to be a key issue for Europe in order to maintain its

leading role. The new industrial revolution will be based on data as raw material, where the digital economy

will merge as a real economy. The challenges for a “hard” sector where traditionally the “soft” has not been

considered as an asset are evident and notorious. In this paper IK4-IDEKO, as part of a machine tool builder

group, DANOBATGROUP, provides a vision of the challenge and the approach for the solution, supported

by results of the current work.

1 INTRODUCTION

The new industrial revolution, nowadays called

Industry 4.0, pursues the adoption of Cyber Physical

Systems (CPS) to enable the creation of a real-time,

precise, reliable, monitoring system able to feed

analytics solutions to support the automation, the

control, and the improvement of the implemented

business processes. With respect to the current

solutions, pervasiveness of the Internet of Things

together with the ability to manage and process

bigger amount of data in real-time, makes the

Industry 4.0 a paradigm that can bring a lot of

advantages in the Factories of the Future: e.g., more

detailed control of processes and ability to quickly

react to internal and external changes.

The Industrial Internet is an internet of things,

machines, computers and people, enabling industrial

operations using advanced data analytics for

transformational business outcomes. There are many

interconnected systems deployed today that combine

hardware, software and networking capabilities to

sense and control the physical world.

With the development of the IIoT and

connectivity and the cloud computing infrastructures

provided as a service, a huge amount of data comes

up in order to boost new business models supported

in the data analytics. Figure 1 shows the three main

pillars of innovation and transformation in

manufacturing based on IT. The connectivity

capabilities provided by the development in IIoT,

supported more and more in standards like

MTConnect or OPC-UA are a straightforward way

to listen to industrial devices, that are becoming

energy efficient and accessible wireless. In a

parallel way, the Platform as a Service and the cloud

computing models are offering ubiquitous storage

and computational resources, federated and hybrid

topologies, for the IIoT data. This data will be

converted in value, if we are able to benefit from Big

Data Analytics, creating business models based in

smart services. The loop with the IIoT is closed in

the extent that smart services can be implemented as

actuators with intelligent CPSs.

Figure 1: IIot, Cloud Computing and Big Data Analytics.

So far, there is actually a real world, represented

by the automation pyramid, where there are different

levels: sensors and embedded systems, cyber-

physical components, machines, production lines

and factories. This real world has a twofold

representation in the data realm, represented as a

virtual world (Figure 2).

590

Gerrikagoitia, J., Unamuno, G. and Sanz, A.

Making Sense of Manufacturing Data.

DOI: 10.5220/0005999005900594

In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) - Volume 2, pages 590-594

ISBN: 978-989-758-198-4

 2016 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

In this sense, the Industrial Internet embodies the

concept of “data factory”, where data is produced,

moved, exchanged, transformed, processed,

elaborated and visualized in the virtual world, where

the digital domain takes its shape to take data into

action and value.

Figure 2: Real and virtual world in manufacturing.

This way, data becomes a real asset even for

industrial organizations and decision makers are

starting to be sensitive to this new paradigm in

which the big data analytics and data science open

new opportunities based on “smartization” and

“servitization”.

2 THE PROBLEM

Manufacturing firms not only seek manufacturing

technique innovations but are also beginning to

focus on added value services and new business

models, creating a fuzzy boundary between

manufacturing industry and service industry.

Servitization was proposed by Vandermerve and

Rada in 1988 (Vandermerwe and Rada, 1989). They

emphasized the concept of customer focus;

combining products, services, support, and

knowledge are the most important elements.

Furthermore, the authors also asserted that not only

service industries, but also manufacturing industries

should focus on innovative value added service

development in order to quickly enhance their core

competencies. Baines defined manufacturing

servitization as innovation of organizational

capabilities and processes, from product sales to

integrated product services (Baines et al, 2009).

Servitization is defined as the strategic innovation of

an organization’s capabilities and processes to shift

from selling products, to selling an integrated

product and service offering that delivers value in

use, i.e. a Product-Service System (Martinez et al.,

2010).

The concept of a Product Service-System (PSS)

is a special case of servitization. Mont defines PSS

as a system of products, services, supporting

networks, and infrastructure that is designed to be

competitive, satisfy customers' needs, and have a

lower environmental impact than traditional business

models (Mont, 2004). In the PSS business model,

industries develop products with value-added

services, instead of a single product itself, and

provide their customers with services that are

needed. In this relationship, the market goal of

manufacturers is not one-time product selling, but

continuous profit from customers by total service

solution, which can satisfy unmet customers’ needs.

For a best viewing experience the used font must be

Times New Roman, on a Macintosh use the font

named times, except on special occasions, such as

program code (Section 2.3.7).

3 PHILOSOPHY

In order to address the explained challenges IK4-

IDEKO has defined a data management framework

shown Figure 3.

Figure 3: Data management architecture.

The three main layers of data acquisition, data

persistence and visualization have to be designed

having in mind that data is a raw material provided

by machines.

Making Sense of Manufacturing Data

591

3.1 Data Acquisition Layer

The data acquisition layer is responsible of the

observation, gathering and delivering of the

variables of the machine to the persistence layer.

The general solution provided by IK4-IDEKO is

a device and software built jointly with Cyril data

systems (www.cyril.es) that is able to connect via

Ethernet to different types of machines, read data

and sends them to the cloud. This data gathering

device can connect to diverse numeric controls as

Heidenhain, Fanuc or Siemens, or even a data

gathering sensors.

Once the device is connected to a machine, a

web management tool can be used to remotely

configure the device, define the signals or variables

that will be read from the machine etc (Figure 4). In

this case, the memory address map of the PLC will

be used to match the real world with its

corresponding virtual counterpart. Moreover, the

sampling or data gathering frequency has to be

defined, in that sense, the concept of group of

capture has been defined, in order to read and

transmit together the set of variables that have the

same observation frequency.

Figure 4: Remote configuration of PLC variables.

The commonly available variables of a machine

tool are obtained through the numeric control and

the programmable logic controller (PLC). These

components manage a wide set of variables that can

be categorized in the following groups: state, alarms,

speed, temperature, power, revolutions per minute,

advance,… All these variables are related to the

machine or to specific components as engine, axis,

spindle, tool,…

Other useful variables that can be obtained are

related to the interaction of the worker with the

machine as the different types of overrides,

modifying the programmed operation behaviour,

interruption of the cycle of the machine etc.

Although the numeric control is the main data

acquisition device, there are other variables, for

example the ones related to dynamics, like

vibrations that have to be obtained using specific

sensors. This way, the monitoring of the machine

implies the monitoring of a numeric control, PLC

and sensors, depending on the observational

requirements.

3.2 Data Persistence Layer

The management, transformation and treatment of

the data is the most important stage in a data-driven

approach in order to make sense of a myriad of

variables (temperature, speed, override, power,

revolutions, vibrations,… ) obtained from cnc/plc

and a set of sensors.

The data persistence layer is divided in two parts.

First, a data lake model is the general repository

where the data from the different groups of capture

and sensors is stored and tagged using metadata in

order to provide data lifecycle and management

capabilities. A NoSQL document oriented

MongoDB database is used for this purpose.

The second part of the persistence layer is an

analytical database based on a data mart model. The

initial implementation relies on a PostgreSQL

relational database management system with three

datamarts: production, process and condition.

The whole set variables of a machine can be

grouped conceptually in production, process or

condition data. The production variables focused in

the state of the machine and closely related to the

concept of availability. Process variables provide

information about the machining process through

speed, temperature, power, revolutions,…process

and production can be used to approach the overall

equipment efficiency of the machine in a great

extent. The third group of condition monitoring

variables, besides cnc variables, vibration, noise, and

temperature measurements are often used as key

indicators to provide health information about the

machine and help detect machine faults early, which

prevents unexpected failure and costly repair.

The dimensional model of the data warehousing

conceptual framework has been used because the

observed variables make more sense once they are

organized and combined with dimensions, like

machine, program, tool, engine, spindle…

3.3 Data Visualization Layer

The visualization layer is divided in two parts.

The first one is machine monitoring. The

information is shown in real time, the monitoring

can be about production (state, alarms), process

(current machining process) and condition (health

and symptoms).

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For this purpose, IK4-IDEKO´s strategy is based

in a web interface generator, a toolbox with different

visualization widgets is available and ad-hoc

windows can be designed by the end users. A

process monitoring interface is displayed in Figure

Figure 5: Process monitoring.

For this scenario, the data lake provides the real

time stream data to be presented via web.

The second part of visualization is focused in

visual analytics and insights in order to enhance the

EDA (exploratory data analysis) and communication

tasks of the data scientists. For this purpose third

party tools are used, mainly RStudio, Watson

Analytics and Qliksense (Figure 6).

Figure 6: EDA using third party tools.

The data marts are the data source used in the

case of the exploratory data analysis visualization

and machine learning modelling for production,

process or condition data.

4 CONCLUSIONS

The digitization of the industry in order to boost and

benefit from digital opportunities requires a mid-

term transformational roadmap. Nevertheless, to

move forward in that way, certain steps have to be

done in the right direction. There are key enabling

technologies such as IIoT connectivity, cloud

computing and big data analytics as drivers for

making sense of manufacturing data. This paper

shows the way that IK4-IDEKO is approaching the

challenge of digitizing the manufacturing industry.

If data is the raw material of the new industrial

revolution, the data science in manufacturing is what

definitely will help to make sense of manufacturing

data. A machine-centric data-driven approach that

benefits from IIoT, cloud computing, Big Data and

visualization technologies and empowers the role of

the data scientist in manufacturing.

The consumption, use and creation of innovative

digital services is being leveraged by the this new

role that is gaining prominence in organizations: the

data scientist. Data scientists are the people who

understand how to fish out answers to important

business questions from today’s tsunami of

unstructured information (Davenport and Patil,

2010). As companies rush to capitalize on the

potential of big data, the largest constraint many face

is the scarcity of this special talent. What kind of

person does all this? What abilities make a data

scientist successful? The data scientist can be

understood as a hybrid of data hacker, analyst,

communicator, and trusted adviser. An extremely

powerful combination. As this multidisciplinary role

is difficult to get in a single professional, IK4-

IDEKO has created a Data Science in Manufacturing

Team with the vision and ambition to become a

reference group in this new knowledge area.

Computer scientist, electronics, mechatronics,

experts in industrial automation, dynamics and

control, production systems, machining processes,

… related skills put together to find a story in a data

set and provide a coherent narrative about a key data

insight with the idea to convert data in value and an

eventual business exploitation model.

ACKNOWLEDGEMENTS

ELKARTEK, Basque Government Research

Program

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