Predictive Maintenance – Definition, Requirements and Benefits

Published: 2020-11-11 Updated: 2023-11-16
Subject: Digital Information Twin Networking service information Increase efficiency in service Documentation

"Predictive Maintenance" is a frequently used buzzword in the field of Industry 4.0 and is considered a desirable goal by many maintenance managers and those responsible for servicing machines and systems. But what exactly does predictive maintenance mean, how does it differ from other maintenance strategies and what benefits does it bring? What requirements must be met in order to actually implement predictive maintenance in a company? This article of our series "Buzzwords Explained" offers an overview, suggestions and ideas on the topic of predictive maintenance.

Definition of „Predictive Maintenance“

Predictive maintenance is a proactive maintenance process based on permanent monitoring and evaluation of machine and process data. The aim is to predict future maintenance requirements, thereby avoiding malfunctions and making maintenance processes more efficient. Real-time analysis in combination with Big Data is used to determine the condition of machines and systems that are in operation. In combination with other information, the aim is to predict the best time to perform maintenance. Ideally, a maintenance technician will service a machine before a malfunction occurs - but only if it is actually necessary. Compared to preventive maintenance - where maintenance is routinely performed at regular intervals - cost savings can be achieved.

How does predictive maintenance work?

In simple terms, predictive maintenance is essentially based on three pillars:

  • Permanent or periodic collection and storage of machine and process data
  • (Automatic) analysis and evaluation of data
  • Calculation of probabilities of occurrence of certain events

To evaluate the actual state of a device, various inspection methods are used, for example, by means of infrared, vibration analysis, acoustic or sound level measurements. For example, temperatures, rotational speeds, noises or running times are determined. Predictive maintenance therefore refers to the actual condition of the machines, not - as with preventive maintenance - to the average or expected service life. This measured data is then linked to other information, such as machine failures, malfunctions or repairs. This allows conclusions to be drawn about expected future maintenance requirements.

A huge amount of data is needed to make the calculations as accurate as possible. Predictive maintenance procedures are therefore particularly worthwhile for companies that use many machines of the same type or for manufacturers of these machines who want to use predictive maintenance not only for their own machines but also for those they sell.


Advantages of Predictive Maintenance at a glance:

Both manufacturers and operators of machines and systems achieve numerous advantages through the correct use of predictive maintenance:

  • Reduction of downtimes and unplanned outages
  • Increased service life of machines and systems
  • Calculation of the optimal maintenance time
  • Avoidance of unnecessary routine maintenance
  • Improved scheduling of maintenance and service technicians
  • Efficient spare parts management
  • Improved machine productivity and performance

What Requirements must be met in order to efficiently implement Predictive Maintenance?

Whether predictive maintenance is the most suitable maintenance method for a company depends on several factors, including:

  • Resources: number and diversity of machines in use
  • Costs: previous number and severity of incidents
  • Time: planned duration of use of the machines

Follow these steps to successful implement a Predictive Maintenance Project:

  1. Prioritize the use cases, if necessary: Particularly relevant are machines that are very expensive, frequently malfunction or whose failure causes high costs.
  2. Equip the devices with sensors: The earlier the machines and devices are equipped with sensors and networked with each other, the more data - including historical data - is available for further evaluation.
  3. Periodically or continuously collect and store data: This includes status data (such as temperature) or static properties (e.g. date of manufacture) as well as event-related data, such as repairs or other service data. To store this data, a central system is necessary, which gives you a 360° view of your machines and facilities.
  4. Prepare the collected data: The data records must be cleaned up; wrong values must be deleted, and missing values must be added.
  5. (Automatically) analyze and interpret the collected data, e.g. using machine learning algorithms to derive correlations: Among others, the following questions shall be addressed: Which measured variables are relevant for the machine? Which status data provides information about which component might fail in the foreseeable future? Which threshold values are relevant for which data types?
  6. Predict the expected maintenance requirements and plan maintenance operations well in advance: Now wear and spare parts as well as service and maintenance operations can be planned efficiently.

The longer a predictive maintenance algorithm is in use, the more it learns and the more valid statements it can make. Predictive maintenance should therefore be understood as a long-term maintenance strategy.

Create the foundations for Predictive Maintenance now!

With a digital service information system, you create the basis for the implementation of predictive maintenance. By using a central portal for all maintenance and service information, you develop a comprehensive, digital understanding of your machines and equipment. You build a "Digital Information Twin" - an intelligent data model - of your machines and systems. By linking IoT data with stock levels and spare parts, you optimize your processes, reduce costs and optimally prepare your company for the future.

Our aftersales platform Quanos InfoTwin supports you in this.