Reacting only when the problem is already visible costs money and nerves. Even if it will never be possible to foresee and proactively avert all eventualities, action is usually better than reaction. Predictive maintenance enables production companies to use smart data to schedule maintenance work in such a way that maintenance is carried out as efficiently as possible and the probability of failure is reduced.
Predictive maintenance offers productions the possibility of aligning maintenance intervals according to the condition of the machine and the operational requirements, instead of following a fixed cycle or even only reacting when defects occur. The more networked the entire manufacturing process is, the higher the level of possibility for predictive maintenance.
Nothing works without data
In order to be able to make a reliable statement about the condition of a machine, as much information as possible must be collected – depending on the machine, this includes not only data from the machine and its components, but possibly also information from the environment, such as temperature or humidity.
These measurement and production data collected over a longer period of time must then be evaluated based on a machine learning algorithm. Based on the historical data on failures and defects, a forecast can be made as to how likely a new failure is. If there is also a link between shop floor management and maintenance, the best possible time for maintenance can also be identified. Other criteria such as the availability of skilled workers and individual parts can also be taken into account during planning. In addition, the collected data can be used to create a Digital Twin of a machine or plant and use it to analyse how performance could be optimised and higher productivity achieved.
Not a question of technology
From a technological point of view, predictive maintenance only requires two things: firstly, equipping the machines with sensors, if these are not already available, and secondly, an intelligent software solution. The technical hurdles of the machine connection itself against the background of predictive maintenance are therefore no longer in the foreground.
Two aspects are much more decisive: Building up know-how and a long-term digitalisation strategy. Digitalisation approaches that are forward-looking focus on one aspect, but keep the big picture in mind. For example, intelligent machine connection and maintenance management 4.0 not only offer the potential to optimise maintenance. It also offers the opportunity for cross-departmental cooperation and communication, for example. If properly thought out and cleverly implemented, not only can the machines be maintained with foresight, but also work assignments and production processes can be optimised.
The data that is needed is already available in every production facility. It is merely a decision to make it visible and use it intelligently. Do you want to continue to wait or do you really want to use the potential of your production? Read here how you can use the full driving power of data with an integrated platform solution!