<font color="e6007e">Predictive <br>maintenance <br>FAQ</font>

Predictive maintenance: definition, benefits, examples ... all with one click!

All questions and answers regarding predictive maintenance with igus® - smart plastics

What does predictive maintenance mean?
What does predictive maintenance have to do with Industry 4.0?
What is the aim of predictive maintenance? What is required for its use?
What are the advantages and added value of predictive maintenance in practice?
What requirements must be met for predictive maintenance?
What application examples are there from igus?
What maintenance strategies are there besides predictive maintenance?
Where can I find further material on the topic?

Questions after questions - we provide the answers!
Here you will find everything you need to know about predictive maintenance summarised, sorted by topic and answered in concrete terms. Immerse yourself in the world of predictive maintenance and discover the benefits of our smart plastics technology for yourself.

General

► What is predictive maintenance?

Predictive maintenance generally refers to the early anticipation and prevention of faults in machine equipment or components thanks to information on their condition. This is achieved using data-based methods that analyse the condition of your machines and help to predict malfunctions, faults and the timing of necessary maintenance work.

► Is there a difference between "predictive maintenance" and "smart maintenance"?

No. Both terms are now used synonymously in the Industry 4.0 terminology. The following terms are also synonymous:

predictive servicing
forecasting maintenance

For the sake of simplicity, the umbrella term "predictive maintenance" will therefore be used in the following.

► What does predictive maintenance have to do with Industry 4.0?

Industry 4.0 promises greater efficiency in production through networked machines as well as insights from data analyses and improved machine availability. In this context, predictive maintenance, i.e. a condition-based maintenance process based on the analysis of process and machine data, is a key component.

► What is the aim of predictive maintenance?

Unplanned downtime of production equipment costs money and cuts your profits significantly in the long run. What is certain is that machine and system failures pose a serious threat to the industrial sector. Predictive maintenance should lead to cost savings compared to preventive maintenance processes based on routine, interval or time, as work is only carried out when necessary.

► What is needed?

First of all, various sensors are needed to record functionally relevant data on the machines, such as speed, temperature, noise, bearing vibrations or power consumption. Next, a combination of real-time analysis technology and a database is required to interpret and meaningfully analyse the sensor data. If all this is successful, it will be possible to rectify the machine problem before it actually arises.

► Is predictive maintenance only for traditional MRO companies?

No. Predictive maintenance is not only interesting for the manufacturing industry or associated MRO areas (Maintenance, Repair & Operations), but also for all mobility services - whether in the air, in vehicles or on rails.
It is usually worthwhile for companies that frequently use the same type of machine or do not want to use predictive maintenance for their own production, but for the machines they sell.

Different i.Sense options at a glance:

General information on predictive maintenance

Advantages of predictive maintenance

► Advantages and added value for companies - a summary

Increased system availability due to less downtime
Maximisation of the service life of systems and machines
Optimised deployment of maintenance technicians
Scheduled maintenance time
Higher machine performance and productivity

► Apart from the obvious advantages, are there also disadvantages?

There are, of course, the one-off investment costs in sensor hardware and software for analysing and evaluating data. In addition, of course, every new method means initial revision and training costs. With the ready-to-install smart plastics solutions from igus i.Cee, however, these can be reduced to a minimum.

► What potential savings are possible?

Between 50 - 70% less unplanned downtime is said to be possible, as well as savings of 20 - 40% in maintenance costs, depending on which study by the two renowned consulting firms McKinsey or Accenture you prefer to trust. Roland Berger also claims to have found that with predictive maintenance, only 15 per cent of time is spent on maintenance. With conventional reactive maintenance, this is 40 per cent of the time.

Does the OEE also improve?

According to the authors of a survey conducted by another technology consultancy, Bearing Point, among experts in maintenance, production, logistics and IT, over 80% of users expect predictive maintenance to increase plant availability, i.e. the OEE (Overall Equipment Effectiveness).

Find out more about the relationship between predictive maintenance and OEE here

Requirements for predictive maintenance

►How can I get started right away?

Collecting and analysing machine data is central to this type of intelligent maintenance. The data can be used to calculate the optimum time for maintenance in order to avoid malfunctions. Machine data is usually recorded using sensors attached to the machine or system component, often aptly called predictive maintenance sensors. In the igus energy chain, this job is performed by the i.Sense condition monitoring sensors from smart plastics. This intelligent sensor technology continuously records the various operating states of the energy supply.

Find out more about i.Sense technology

► What other data is required for the most accurate possible maintenance forecast?

The prerequisite for the most accurate possible indication of fault-free running time is that information on usage is as accurate as possible. This includes information on the place of use with weather conditions and temperatures, distances covered, number of cycles, etc. Thanks to the knowledge acquired in the igus test laboratory and the current condition data, igus is able to predict the service life data precisely. A necessary replacement of a component is then indicated in good time and enables planned and resource-conserving maintenance.

Find out more about the igus test laboratory for energy chains

►Do you always need new software or IT tools?

The prerequisite for predictive maintenance is that the machines are networked, i.e. all machines provide their data for storage and analysis in a database. This is ensured with an OPC UA protocol.
With the igus i.Cee solutions for predictive maintenance, however, no additional systems or hardware are required. All the necessary data is provided via the associated i.Sense sensors and the special AI algorithms are implemented in the i.Cee module. And the associated i.Cee dashboard can be accessed with any standard PC.

Find out more about i.Cee from igus

Smart maintenance at a glance:

The diagram schematically shows how i.Sense and i.Cee work and the existing options

Application areas

► Examples of predictive maintenance

In practice, predictive maintenance often makes sense for machines where a breakdown would result in high sales losses or consequential damage. Modern machines are often already equipped with the necessary measurement sensors. Older models can be retrofitted with them. These can be for example:

Vibration measurements on systems, provide information about imbalances, incipient bearing damage, etc.
Temperature measurements, provide information about bearing damage or cooling problems
Measurements of the current consumption of pumps, motors, etc., provide information on sluggish drives, couplings, etc.
Measurements of the transmission properties of data cables provide information about the remaining service life
Force or load cell sensors help to determine the current capacity utilisation

In the field of dynamic energy management applications, igus already has a broad product portfolio available with its smart plastics.

Find out more about igus solutions

► In which industries does predictive maintenance make the most sense?

Predictive maintenance is primarily used in industries in which the failure of systems, machines or components means considerable consequential damage and downtime costs:

Aviation: Airlines use sensors in their aircraft to replace wear-resistant parts such as turbines or pumps in good time and avoid downtime on the ground.
Automotive: Automotive companies operate some of the largest robot parks in the world, produce just in time and have tightly integrated supply chains. Any disruption to production, e.g. unplanned downtime, leads to disruption to the entire supply chain.
Wind turbines: Vibration analyses can now almost completely prevent wind turbine failures.
Ports: Crane breakdowns mean long waiting times for ships and less throughput for ports.

Predictive maintenance solutions from igus smart plastics are already in use in most of these industries.

Find out more about potential areas of application

Case study on i.Cee in CFL train washing station

Predictive operation without surprises: smart energy chains for the most modern train wash in Europe

Case study
In this case study, we talk about how CFL, Luxembourg's national railway company, uses several energy chains from igus® together with intelligent sensor technology from smart plastics to overcome numerous challenges:

How exactly are failures prevented?
Which components were to blame for the failure?
How was the entire project approached and with which tools?
And how is it currently going?

To PDF download

The white paper on predictive maintenance

Increase system availability and ensure process conformity

In the white paper, you can find out about the following:

What is the difference between the two concepts?
What technologies are behind the respective systems?
What do both systems offer for energy supply in industry?

Download white paper here!

Other maintenance strategies

► What other maintenance strategies are there?

In its DIN EN 13306 - Terms and definitions of maintenance, the German Institute for Standardisation defines a maintenance strategy as a procedure for achieving maintenance objectives, such as maintaining a specified condition or extending the life expectancy of a machine. A distinction is usually made between the following three different approaches:

Reactive maintenance strategy / Damage-dependent maintenance strategy
Preventive maintenance strategy
Predictive maintenance strategy

► What characterises reactive maintenance?

With the reactive form of maintenance, damage to machines is repaired and production parts are replaced only when there is an acute problem. This means that companies have to react quickly in the event of damage in order to be able to continue production as quickly as possible and avoid high downtimes and costs. Most often, this strategy is used with machines that don't need to be repaired often or that can be repaired or replaced cheaply.

► What is the difference between preventive and predictive maintenance?

Preventive maintenance is a maintenance strategy in which measures are carried out at certain intervals. These can be based on time or quantity. In other words, you do not wait until systems or machine parts break down, but systematically carry out preventive inspections and maintenance measures. The actual degree of wear and tear on the machines and systems is irrelevant. In predictive maintenance, on the other hand, process and machine data is recorded, stored and subsequently analysed during operation using sensors and interfaces.