Sooner or later, components in constant use start to wear out. You need to repair or replace them before they negatively impact the equipment or systems they are in. Even a short period of downtime for a faulty – or even failed – machine is expensive. It may even have negative consequences for the entire supply chain. This is where predictive maintenance comes in. Read on to discover how and where predictive maintenance is used to pre-empt and prevent problems before they get expensive.
From product development to production to logistics, in today’s digitalised world, a product‘s value creation stages are increasingly connected to each other. And this is true for the stages in the entire product life cycle. In this age of Industry 4.0, production processes are increasingly being controlled and monitored by IT systems. This creates complex digital ecosystems. These enable predictive maintenance measures, which make sure your equipment runs smoothly and prevent machines or even whole assembly lines going down.
What is predictive maintenance?
Predictive maintenance is proactive maintenance. Using technology like the internet of things (IOT), machine learning, cloud computing and big data analytics, it predicts when a component is likely to fail, and alerts you to replace it before it causes problems.
For a company with large, high-capacity production lines, a single failed component could put the whole company in jeopardy. Did you know that a car manufacturer loses an average of €18,000 to €42,000 per minute of unplanned downtime? Predictive maintenance can help minimise these drastic losses and even prevent them altogether. Predictive maintenance lets companies get more out of their machines and production lines. It reduces both planned and unplanned downtime with more efficient maintenance and upgrades. To put that in numbers, studies have shown that predictive maintenance can reduce maintenance costs by up to 30 % and cut unplanned downtime by up to 70 %.
How does predictive maintenance work?
How does predictive maintenance actually work? Various devices and sensors capture data in real time. Depending on the specific needs, this data may be on a machine, a process, or even external factors such as weather, traffic or location. The information is analysed using big data technologies, and compared to information on earlier breakdowns. Artificial intelligence (AI) is then able to draw on pattern recognition and algorithms to identify information, such as the time at which a component may fail. The system is constantly learning and its predictions become very accurate. This lets you act to prevent blips or downtime before they occur.
Traditional versus data-driven maintenance
Predictive maintenance is not new. By 2017, 81% of German machine and plant manufacturers were already grappling with this subject. In an article from 15th March 2021, the online magazine Instandhaltung wrote that one third of companies were continuing with their predictive maintenance plans beyond the pilot project. There are other ways to approach maintenance. Figure 1 compares more classic ways with a data-driven approach.
Let’s take a closer look at the differences between a data-driven maintenance approach and more classic models. This information is based on a May 2019 report by Thomas Fritz from DLG-Expertenwissen.
Traditional approaches to maintenance
Today, most companies are still employing the classic, more traditional maintenance models. These approaches involve
- reacting only when malfunctions or breakdown occur, or
- performing preventive maintenance activities at fixed intervals based on previous experience or legal requirements (such as stipulated intervals for inspecting and maintaining elevators or fire extinguishers )
These traditional approaches may be suitable for simple, standalone equipment. Carrying out maintenance at predefined intervals often leads to high maintenance and material costs, not to mention a long downtime period. However, if a standalone machine fails, this generally doesn’t have a huge impact on the process as a whole.
However, today’s production lines are more complex. They contain a huge amount of interconnected, closely coordinated components. This means that a traditional approach to maintenance is no longer appropriate. If you wait for a component to fail before replacing it (known as run-to-failure, this may well lead to the entire production line going down. And that downtime is terrifically expensive.
A data-driven approach to maintenance
Data-driven maintenance takes a different approach. It targets and continually checks equipment, components and environmental factors. You can split data-driven maintenance into two categories: condition monitoring and predictive maintenance.
Condition monitoring determines when to replace a component by looking at its level of wear and tear. Once this has exceeded a pre-defined level, the system issues a warning. The component affected is, however, still intact and could be reused.
The highest level of maintenance is predictive maintenance. This employs analytics and machine learning on machine and environmental data. This makes it possible to recognise when a component is likely to fail, and to react accordingly. In fact, systematically employing AI in your maintenance strategy can help you do the following:
- Reduce downtime
- Lower costs and follow-up costs
- Minimise unplanned downtime
- Increase the lifespans of machines and equipment
- Optimise the performance of your machines
- Use previous data to detect abnormalities in how your equipment is running
- Recognise the best time to perform maintenance
- Prevent unnecessary scheduled maintenance
- Home in on error sources
- Recommend appropriate remedies
- Schedule your service and maintenance team better
- Reduce the workload of your field engineers
- Manage your spare parts more effectively
Where is predictive maintenance used?
There is potential to use predictive maintenance in almost every industry. However, needs and complexity vary from sector to sector, which naturally has an impact on how to conduct predictive maintenance. Let’s take a quick look at how predictive maintenance looks in certain industries:
In mechanical engineering, predictive maintenance helps reduce time and cost-intensive inspections and the associated downtime. Issues are detected early on, and its’ also easy to identify ways to make improvements in the plant. In the long term, predictive maintenance extends a machine’s lifespan. It also enables manufacturers and operators to offer their customers better service and new business models.
If wind turbines break down unexpectedly, this results in huge financial losses. As a rule, you need cranes and other specialist equipment to make repairs, and it takes technicians time to get hold of these. Then, there are offshore wind farms. Tide, weather conditions and other environmental factors mean that these are not readily accessible 24/7. There are many reasons why a wind turbine may fail. Therefore, it is crucial to closely monitor and proactively maintain the equipment to keep costs and stress as low as possible.
Predictive maintenance is a crucial success factor in maintaining customer satisfaction for today’s railway operators. Diagnostic data helps them plan maintenance work in advance and reduce breakdowns, line closure, or trains standing idle, as well as maintenance costs as a whole. Railways have many ways in which they employ and benefit from predictive maintenance:
- Points: The Deutsche Bahn use a system they refer to as a ‘points ECG’ to keep their trains running on time. Sensors on 28,000 sets of points constantly report back whether everything is working as it should, or whether there are technical issues. This makes it easy to detect and correct errors – before they happen. This system has enabled the Deutsche Bahn to prevent several thousand service disruptions a year, proactively correcting 36,000 emerging faults in 2019 alone. Ultimately, the customer benefits as train travel becomes more reliable.
- Tracks: Predictive Maintenance is used for rail networks around the globe. The system automatically detects and records any hollow space under the tracks and bed. This is because if the tracks just slightly start to subside, the tractive force from the locomotive is no longer evenly spread, leading to friction. Stronger subsidence can easily become a safety risk.
- Trains: These days, train manufacturers collect data from trains while they are running. Their predictive maintenance analytics system automatically recognises patterns and any deviations from expected values. This lets the manufacturers know when certain maintenance and repairs need to be carried out. Furthermore, the engineers know exactly which components they need to look at and potentially replace to prevent greater damage.
In the vehicle world, predictive maintenance is making it easier to forecast when engine components may fail. To this end, the following are constantly being monitored:
- Engine temperature
- Fluid levels
- Level of vibration
This data, alongside master data from the vehicle is drawn upon to forecast when engine components could fail. This lets you get them repaired before they become a problem.
If an aircraft is grounded, this is extremely expensive for the airline. Predictive maintenance helps prevent grounded planes, and, of course, crashes. In 2017, Airbus launched their sophisticated data platform, Skywise. This lets aircraft engineers intervene early on, as soon as an issue is detected, and replace a faulty component before it fails. Skywise collects data from over 7,000 aircraft at more than 90 airlines. This creates a huge pool of data, as an Airbus produces an average terabyte of data per flight hour12. And that helps create pretty good predictive models. Airbus is intending to use its Skywise data alongside predictive maintenance to develop better planes.
The process manufacturing industry is one of the largest users of predictive maintenance. To begin with, manufacturing involves joining several components together. At the same time, a company can ill afford to slow down or even stop their production lines. These are both excellent reasons to employ predictive maintenance. Predictive maintenance can be found in the following areas:
- chemical processing plants
- petrochemical plants
- oil and gas industry
- cement works
- pulp and paper mills
- drinks industry
- pharmaceutical industry
The examples below show that predictive maintenance works regardless of time and place. Instead, it takes full advantage of the networked sensor technologies available in industry 4.0 and the internet of things (IoT). You can read more about how this works in the second part of this blog series, here.
 cf. Wartung 4.0 senkt Kosten [Maintenance 4.0 Reduces Costs] (MaschinenMarkt.vogel.de, from 21st Jun 2017) (accessed 15th Nov.2021).
 cf. Predictive Maintenance. Service der Zukunft – und wo er wirklich steht. [Predictive Maintenance – Service of the Future and Where We Are Now] Roland Berger für VDMA, April 2017 (accessed 15th November 2021).
 cf. Predictive Maintenance setzt sich durch – langsam – (instandhaltung.de from 15th Mar 2021, accessed 15th Nov 2021).
 Based on: Predictive Maintenance – dlg.org (accessed 15th November 2021).
 Vgl. Predictive Maintenance – Definition, Voraussetzungen und Vorteile (quanos-service-solutions.com) (abgerufen am 15.11.2021).
Vgl. Predictive Maintenance – dlg.org (abgerufen am 15.11.2021).
 Cf. DB sorgt mit „Weichen-EKG“ für verlässlichere Züge: Bereits 3.600 Störungen frühzeitig erkannt und verhindert | Deutsche Bahn AG [Deutsche Bahn runs a ‛points ECG’ for more reliable trains: 3,600 faults lready detected and resolved](accessed 15th Nov. 2021).
 Cf. Predictive Maintenance – Was ist prädiktive Wartung? (service-lobby.com) [What Is Predictive Maintenance?](accessed 15th Nov. 2021).
 Cf. Was ist Predictive Maintenance? Definition, Anwendung und Beispiele (industry-of-things.de) [What Is Predictive Maintenance? Definition, Use and Examples] (accessed 15th Nov. 2021).
 Cf. Predictive Maintenance soll Flugzeuge zuverlässiger machen – airliners.de [Predictive Maintenance Could Make Planes More Reliable](accessed 15th Nov. 2021).
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