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PREDICTIVE
MAINTENANCE
Fixing Machinery Before a Breakdown with AI
Automate repetitive &
delete all boring tasks
A typical factory loses between 5 and 20% of its production capacity due to downtime. Traditional preventive maintenance procedures require machines to be repaired at intervals based on time or usage. However, these methods still result in significant downtime, which leads to idle workers, increased scrap rates, lost revenue and angry customers. In addition, preventive maintenance may replace parts that still have significant life, which can be a waste of time and money.
Reduce costs
up to 25 %
Increase the runtimes of your machines
25%
Reduced
downtime
25%
Less Inspection Cost
15%
Reduced ann. Maintenance Cost
Maintenance of difficult to access objects
Maintenance of objects that are difficult to access due to size or location is usually very difficult and costly. Mostly, the objects are maintained after certain cycles - even if it would not be necessary yet. AI based maintenance in combination with drones or cameras is a powerful tool to maintain objects in a safe and time & cost efficient way.
Best Customer Service
Modern customers are used to quick answers and effective solutions. With the help of automation, it is possible to achieve the results desired by customers such as sorting inquiries, giving customers initial answers, classifying inquiries into different categories. The customer's call does not have to be routed from one employee to another.
Abrasion detection of machinery & robotics
Material or object abrasion is a major issue across all industries. Below you will find an overview of the industries in which Predicitive Maintenance can be used. If you have individual requirements, please do not hesitate to contact us.
Wind power
AI can almost completely prevent wind turbine failures these days. This is because sophisticated algorithms and computer vision are able to provide unambiguous predictions about failure probabilities.
Smart Cars
AI is also gaining importance in vehicle maintenance: Sensors located in the engine or chassis, for example, make data analysis possible. This means that a defective part can be replaced during the next visit to the workshop, even before it fails completely. It is thus possible to prevent expensive repairs.
In aviation, turbines or pumps can be replaced even before a final defect. It is particularly important for airlines to detect faults at an early stage. This is because - due to the high fixed costs - there is a major economic loss if an aircraft is not ready for operation.
Aviation
Rail Transport
With the help of AI, it is possible in rail transport to avoid unplanned train failures. For example, a modern, mobile monitoring system can detect track gaps or constantly monitor the condition of switch machines. This means that - if forward planning is used - delays in operations can be avoided if symptoms of deterioration are detected at an early stage. Spare parts can also be ordered before a possible breakdown.
With the help of AI, this industry can detect early warning signs from the supply or demand side of the network. Companies can then fix these before outages occur. The result is the avoidance of costly repairs and the reduction of customer complaints.
Supply Companies
Production
As mentioned above, the use of AI is particularly useful in the production sector. Machines and systems are equipped with sensors that collect big data. This enables algorithms to detect, diagnose and avoid faults at an early stage.
In the oil and gas industry, for example, deep-water locations are becoming a problem: Since it is often impossible to gain insight into the condition of the equipment there, Big Data and predictive maintenance offer a solution. This is because they provide a better overview of the equipment, so you can predict equipment failures.
Oil and gas companies
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