Industrial maintenance today
In today’s business world, regardless of the sector and its specific problems, competition is increasing and fixed, production/distribution and labour costs are rising. As a result, profit margins are getting smaller and smaller. But we have the opportunity to increase these margins if we can increase asset availability and performance and reduce maintenance costs. The usual maintenance models oriented towards preventive or corrective maintenance are no longer sufficient to get the best possible performance from the equipment without incurring costs due to breakdowns. This is one of the advantages of implementing Predictive Maintenance.
The incorporation of Machine Learning models in Predictive Maintenance helps to guarantee the required availability and efficiency of equipment and installations, ensuring the duration of their useful life and minimising maintenance costs, within the framework of safety and the environment.
The advantages of implementing Predictive Maintenance
The capabilities of Machine Learning applied to equipment, for the analysis of information on the condition and operation of assets, allows the anticipation of possible failures that help to reduce corrective interventions and their associated costs. In addition to optimising the use of equipment and parts to the maximum.
The advantages of implementing Predictive Maintenance are:
- Reduction of failures and breakdowns
- Reduction in the number of interventions
- Prolongation of the useful life of the assets.
- Increased asset availability
- Reduced downtime for repair
- Reduced downtime
- Optimisation of maintenance personnel management
- Option to track the evolution of a defect over time
- Accurate knowledge of the time limit for action
- Reduction of accidents and increased safety
- Verification of repairs and overall reliability
Benefits that incur significant cost savings and increase profit margins:
- Reduced maintenance costs
- Reduced labour costs
- Reduced spare parts costs
- Reduced accident costs
- Reduced industrial insurance costs
The implementation of Predictive Maintenance
In order to implement a Predictive Maintenance system, the first thing we need is data. This data can be collected manually, or through Big Data systems for data capture, processing and storage. Modern information systems related to Industry 4.0 facilitate the provision of data that can be processed analytically to increase reliability and average life, reducing maintenance costs.
There are two stages of implementation. The first stage is analysis and design, consisting of points such as: collecting data, assessing the condition of equipment, looking at possible actions to repair or refurbish machinery and designing inspection routines. The second stage deals with the allocation of resources by the company to the maintenance unit and the execution of predictive techniques.
Among the most relevant techniques are the analysis of lubricants, vibrations, induction electric motors and reciprocating machines, thermography, ultrasonic detection or partial discharges in electrical machines.
According to a study by IBM Analytics, the advantages of Predictive Maintenance are far greater than those of traditional maintenance methods. And they report a return on investment 10 times higher.
In fact, according to the same study, using Predictive Maintenance reduces maintenance costs by 20-25% more than with other types of maintenance. You also reduce breakdowns by 70-75% and downtime by 35-45%.
At decide, we offer our clients all those tools that facilitate more accurate, agile and efficient decision-making, which bring direct value to their businesses.
The solutions we provide give such a high value to our clients that in many occasions they are the core of the competitive advantage that allows them to consolidate in their markets.
Do you want to know more?
Do you want to learn more about decide4AI and keep up to date with future webinars or actions? Follow us on social networks (Linkedin, Twitter, Youtube).
