The self-running industry
Predictive maintenance or “predictive maintenance” plays an essential role in industry. Before the progress of digitalization in production facilities became clear, the maintenance of machines and systems was seen more as a cost factor and an unpleasant necessity. This was due to the fact that either the machines and systems were serviced routinely without any functional defects or impairments, or when a problem had already occurred and operations came to a standstill. This standstill meant:
- Failure of the system availability
- Increased maintenance and service costs
- Decrease in the service life of the system and system safety
- Reduction of spare parts handling
- Downtime-based loss of revenue
In order to be able to eliminate these problems on a large scale, predictive maintenance and repair has established itself as an effective means.
What is predictive maintenance and how does it work?
The function behind predictive maintenance is that predictions of potential events can be made on the basis of historical and real-time data. In this way, users can be made aware of any defects before they arise. Predictive maintenance describes a maintenance procedure that is based on relevant process and machine data. By processing the data, forecasts can be made, which results in needs-based maintenance.
Why predictive maintenance?
Predictive maintenance is enjoying increasing popularity as various potentials have been recognized by active users. The following advantages can be mentioned:
- Improvement in profitability
- Optimizing machine performance
- Best possible maintenance time
Every third industrial company is already actively using predictive maintenance. Three years ago it was only every fourth company. An extremely important factor that contributes to this is the progress of digitization and the associated increase in the amount of data. The analog data acquisition and evaluation via Excel tables, for example, is an outdated system and only illustrates information about past factors. However, they cannot determine when the optimal time for machine / system maintenance is imminent, which in turn leads to enormous costs. In order to be able to generate more efficiency in operation, agile and accurate forecasts about the condition of the machines are necessary, which is why predictive maintenance has proven to be the optimal solution.
Automation of maintenance
Monitoring, analyzing and notifying errors or deviations has shown, according to The Plant Engineer’s Handbook, that the active use of predictive maintenance can reduce downtimes in industries by up to 20%. Impending failures or problems are signaled immediately so that the relevant specialists can implement the necessary maintenance measures. The process of maintenance measures, i.e. the reaction after the signaling, which in many cases is handled by human intervention, can often be automated. As a result, relevant steps are automatically initiated on the basis of the prognostic evaluations in order to relieve specialists and minimize internal downtimes by a considerable percentage. The advantages that result from the automation of the maintenance measures are:
- highly flexible and agile reaction to deviating behavior of machines and framework conditions
- higher scalability to constantly changing conditions in operation
- Optimized maintenance processes and spare parts handling through selective and automatic response
In a nutshell:
Predictive maintenance fulfills the tasks of monitoring and analyzing the system data and notifying you in the event of errors. In order to take advantage of additional savings potential, the measures to correct the deviations can be automated.
Automation through artificial intelligence
One way of being able to bring about automated maintenance, an AI (artificial intelligence) can be used, which creates algorithms on the basis of collected data in order to trigger maintenance commands in a predictive manner. The data can be collected through sensors, switches, and other AI-compatible tools. The advantage is that AI-supported automation can create a network with other machines and information systems. In the event of an imminent failure, a number of reaction measures are triggered, such as automatically contacting a technician or ordering relevant spare parts. Another advantage is that the core function of artificial intelligence, namely machine learning, optimizes the maintenance and repair processes independently and automatically, so that efficiency is increasingly increased independently.
Predictive maintenance is no longer in the context of the technical feasibility, but the equipment of companies and the respective openness to continuous automation. According to experts, predictive maintenance should grow by 20 to 40 percent per year until 2022. It is clear that the potential of predictive maintenance is far from being exhausted. The use of predictive maintenance creates new business models in which companies no longer benefit from the sale of production goods alone, but also from the servicing of their machines. So to speak, they become service sellers who not only focus on optimizing their own products and process chains, but also focus on the end customer through Smart Service 4.0. According to this, a stronger service orientation will be integrated in addition to the actual core segment of the company, from which industries, but also other sectors, will clearly benefit.