"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.
"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.
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.
In simple terms, predictive maintenance is essentially based on three pillars:
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.
Both manufacturers and operators of machines and systems achieve numerous advantages through the correct use of predictive maintenance:
Whether predictive maintenance is the most suitable maintenance method for a company depends on several factors, including:
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.
With a digital spare parts and 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 service information system supports you in this.
