Predictive Maintenance (with R)

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Over the course of the next 15 to 20 years the global economy will continue to progress further, heading towards a promising future full of opportunities especially within the sector of manufacturing and industrial production. However, industrial production in the 21st century will also continue to encounter numerous and diverse challenges as for instance the increasing international competition, shorter product life cycles and faster technological leaps forward – just to mention a few. Also, there will be an additional structural shift of machinery demand as well as of production from developed countries to emerging countries such as China.

Companies operating in the industrial production branch have to anticipate the future developments and start to prepare by putting things on the right track. In order to survive in an environment like this, resource-efficient and secure planning of production processes are of main importance to guarantee a consistent and high quality output.

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Figure 1: Four Phases of Predictive Maintenance

In industrial production, unforeseeable machine failures as well as performance drops or deterioration in quality because of defective system components can lead to severe shortness’ of supplies which will eventually weaken the market position of the entire organization. In order to avoid this, organizations are increasingly focusing on the improvement, maintenance, repair and operations of their machinery.

Types of Maintenance Strategies

Generally, one can distinguish between three broad types of maintenance:

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 Preventive or Periodic Maintenance. This type consists of regular inspections of the equipment and machinery with the objective to prevent sudden failures and problems. Through regular maintenance cycles the risk of total failure is reduced substantially compared to breakdown maintenance.

 Condition-based Maintenance is based on the monitoring of the machinery and the performance. The monitoring is conducted by continuous assessment of device and performance parameters and a comparison thereof with predetermined values. Maintenance is only carried out after certain indicator show an increase in failure probability.

In the previous years, the industry shifted their focus away from only reactive repair mechanisms towards the predictive coordination of machine maintenance. In the category of the future of maintenance developments,

 Predictive Maintenance as an extension of condition-based maintenance represents the informatization of production processes. With intelligent IT-based production systems Predictive Maintenance represents one important step on the path towards the development of a Smart Factory in industrial production.

This method employs inspections by measuring and analyzing data about deterioration of the machinery and its performance, and relies on a surveillance system in order to monitor conditions and to manage trend values. In the context of analyzing data, the implementation of statistical methods respectively Predictive Analytics and Data Mining are important tools. Predictive Analytics help organizations to anticipate changes. By analyzing existing patterns and past events, future events can be predicted and the planning and execution of maintenance strategies can be adapted accordingly.

Advantages of Predictive Maintenance

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eoda Page 5 life of the machinery or individual components; decreases equipment downtime; decreases costs and leads to better product quality.

Open Source R

In order to successfully conduct Predictive Maintenance procedures, one needs a sound basis in the form of a compelling software solution that can be tailored to the specific requirements and tasks at hand. A powerful tool is the programming language R, which is one of the best alternatives for the analysis and visualization of data that provides many advantages for Data Mining and Predictive Analytics. Consequently statistical methods such as clustering, classification, regression analysis or event history analysis lay the foundation to successfully apply Predictive Maintenance strategies in industrial production.

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Advantages of R

Originally developed in 1993, R has since then benefited from its Open Source character which supported the development of a programming language for statistical data analysis to a cross-platform operating lingua franca for data analysts. For Predictive Maintenance, R provides numerous advantages for a flexible and efficient application.

Worth mentioning here are especially:

 Features: The features that R offers (without additional investment) are incomparable. Norman Nie, one of the founders of SPSS says: "R is the most powerful and flexible statistical programming language in the world"

 R in the analytic stack: R can be integrated with all the layers of an analysis or reporting architecture like data management, data analysis, or the presentation layer. Almost all software vendors offer interfaces possibilities to integrate R.

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Investment protection:

The involvement of the scientific community and large companies support the development and acceptance of R. The R community grows steadily.

 Quality: The basic development of R takes place in the field of science. Two-thirds of the core developers are professors, engaged in the data analysis at universities or companies. R offers high reliability and uses the latest statistical methods.

 Data Visualization: R is one of the most powerful alternatives for creating high quality charts. From data processing to analyze and visualization - R implements the entire workflow.

In addition, R is open source and there are no license costs and it is platform independent and can be integrated into the existing software architecture.

Way of use

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eoda Page 7 With the programming language R the entire Predictive Maintenance process can be managed successfully: starting with the process of data collection, the analysis of data to the preparation of a maintenance prognosis through to a clear visualization of the results. The prediction of machinery failures in the semiconductor industry is one specific example of how the powerful analysis software R uses regression trees and the random forest method to put the innovative concept of Predictive Maintenance into practice in order to significantly improve the process of machine maintenance.

Figure 3: Regression Tree Depicting the Different Weighting of the Variables

However, this is only one field of application, the combination of R and Predictive Analytics provides a wide range of applications. Additional examples of application can be found in the Whitepaper “Predictive Maintenance (with R) – Potential and Possibilities of the Free Statistics Language R for New Business Models in the Era of Industry 4.0”

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Thereby, an organization cannot only reduce repair and downtime costs, but also substantially improve the quality of its products which will have a positive impact on the overall market position of the company. Predictive Maintenance is the maintenance strategy of the future, and the programming language R with its endless possibilities is a key tool for its successful realization in the times of smart factories.

About eoda

We at eoda have a passion for data and analysis. We are data scientists, software developers, management consultants and personal trainers – all combined in one. We generate strategic advantages from your data on the basis of extensive experience in Data Mining and Predictive Analytics.

Our team will derive acting recommendations and solutions that will help you to adjust to upcoming trends or future market changes. We do not shrink away from challenges and individual requests. We are always ready for new tasks that we will manage with our hands-on-mentality, proven methods and technologies – feel free to contact us!

Ludwig-Erhard-Str. 8 34131 Kassel

Tel. +49(0) 561 202724 40

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Table of Figures:

Figure 1: Four Phases of Predictive Maintenance 3

Figure 2: Possible Application of R for Predictive Maintenance 5

Figure 3: Regression Tree Depicting the Different Weighting of the Variables 7