Monitoring the condition of your company’s industrial equipment can significantly reduce breakdowns and lower the costs related to unplanned downtimes. Predictive maintenance is a machine learning-based technique that makes this possible. Learn how it can reduce the risk of downtime and increase the efficiency of your business.

Are you wondering how to improve the processes’ efficiency in your company? You know well that machine failures are costly. But what if you could reduce such breakdowns by up to 70%? According to Deloitte, this is possible. Predictive maintenance increases productivity by 25% in organizations that decide to implement it and, at the same time, lowers the costs of equipment maintenance by 25%. This solution can also increase machine uptime even by up to 20%!

As companies observe the rise of AI to global dominance, more and more of them are deciding to leverage this technology in their internal processes – not only to create new opportunities but also to minimize potential losses. Let us explain what predictive maintenance is and how it can improve your organization’s operations by preventing unnecessary breakdowns.

What is predictive maintenance?

What is predictive maintenance?

There are 3 maintenance management methods:

  • Run-to-failure (R2F) – in this strategy, the assets are used until they break down and then repaired or replaced with new ones.
  • Preventive maintenance (PvM) – businesses that choose this approach to prevent failures by carrying out replacements and restorations on a regular basis at fixed intervals (sometimes “just in case” when the piece of equipment is not broken).
  • Predictive Maintenance (PdM) – using artificial intelligence and machine learning for predictive maintenance seems the most cost-effective solution (if you have access to a big collection of appropriate data). That’s because the machines are monitored continuously, and necessary repairs can be performed when they are actually needed. Using enormous amounts of data, algorithms can estimate when the defect may occur, as they “know” when the performance goes down, or other symptoms of previous defects are spotted.

If you are entering the world of artificial intelligence and you don’t have access to the big collection of appropriate data, implementing predictive maintenance may be a substantial investment. Leveraging this maintenance method is cost-effective in the long run, though it requires a lot of work and expense.

Machine learning has huge potential, and it can be used to increase the efficiency of various processes in companies – also for monitoring the work of the equipment during production. With advanced analytics, the condition and effectiveness of machines can be assessed automatically, so the existing defects can be fixed before they jeopardize the continuity of production. Companies can use machine learning algorithms for predictive maintenance, which allows them to take advantage of real-time data to predict the possibility of defects in equipment and processes to fix them before they happen.

Where does the data come from? Predictive maintenance involves using sensors to monitor machinery continuously to avoid breakdowns. Working devices send information back to the systems, which can determine when maintenance will be required. This is much more cost-effective than relying on regularly scheduled maintenance routines. Machine learning in predictive maintenance also uses historical data to learn from. Then it analyzes live data and compares it to failure patterns – allowing for resources to be utilized in the most optimum way.

How does predictive maintenance work?

Machine learning algorithms for predictive maintenance are applied on historical data to learn from it. When ML models are trained properly, they can be used in real-life cases in production. Sensors from the equipment send data collected in real-time to ML-based systems, so it can be compared with failure patterns. The condition of each machine is assessed, and the algorithms notify the staff when repairs are necessary, or there is a need to replace some equipment components.

There are three main predictive maintenance areas:

  • Real-time monitoring – the performance of machines and data generated in real-time are automatically analyzed.
  • Benchmarking – every detail is important when it comes to optimizing production. ML-based solutions in predictive maintenance perform an in-depth analysis of work orders to eliminate risks.
  • Analyzing work order data – work orders allow companies to track the completion of particular work and learn about the usage of resources. With machine learning, an organization can identify work orders that are likely to cause breakdowns.

With access to such a vast collection of data, predictive maintenance solutions are capable of assessing the condition of equipment with high accuracy. When it comes to ML, there are two types of learning: supervised and unsupervised, and the main distinction between them is the use of labeled datasets:

  • Supervised learning – it uses labeled input data, in the case of predictive maintenance systems, it will need failure prediction-related information in a database. During the analysis, the incoming data will be matched against labeled data in order to get reliable results.
  • Unsupervised learning – there is no need for labeled data. ML algorithms simply analyze and compare data to discover hidden patterns. The system uses clustering methods such as grouping and correlation to define them. 

The choice of the right class of predictive maintenance should depend on the maintenance policy of a specific company.

The implementation of ML in predictive maintenance

How to implement ML in PdM

The implementation of ML in predictive maintenance is quite a complex process. It consists of the following steps:

  • Data collection – AI systems need data to come up with feedback about your equipment. The gathered information has to also be properly handled during the process of ensuring data quality.
  • Data analysis and data modeling – the proper algorithms and models are leveraged to first establish patterns (learn how to identify incoming defects). Data scientists choose those that suit the company’s needs the most.
  • Data prediction – new data collected in real-time is being fed to trained algorithms in order to produce predictions.

Predictive maintenance & machine learning advantages for your business

The IoT Analytics report from April 2021 estimates that the predictive maintenance market, which is currently worth about $6.9 billion, will reach $28.2 billion by 2026. This only proves that ML and AI are becoming more and more popular. There are various benefits of AI in business. For example, natural language processing allows you to offer more efficient customer service to your clients, and data analysis gives you a chance to better understand users and the market. With predictive maintenance, you can significantly reduce your maintenance expenses and improve operational efficiency. Machine learning models process data with high efficiency. As a result, companies are capable of:

  • reducing maintenance frequency,
  • preventing unplanned reactive maintenance,
  • performing cost-effective maintenance instead of serious and costly repairs,
  • leveraging machines and devices in the company in an optimal way,
  • eliminating the negative effects of failures in the form of downtime.

According to Bain & Company, the integration of predictive maintenance solutions powered by machine learning can enable organizations to reduce the frequency of breakdowns by 70-75%. At the same time, it is possible to cut downtime by 35-45%, which has a great impact on the production efficiency and the success of the company. This way, businesses are capable of lowering maintenance costs by 25-30%.

Why does your business need predictive maintenance?

The cost of predictive maintenance equipment is often high, but earlier mentioned run-to-failure and preventive maintenance strategies are not always as cost-effective in the long run. Predictive maintenance is an investment that pays off by saving companies a lot of troubles down the road. Leveraging this solution not only enables companies to reduce expenses but also allows for revenue to be increased with predictive analytics by extending the uptime of the equipment. Integrating predictive maintenance solutions may be crucial to staying competitive in the very near future. Make your company’s production run like clockwork – ensure the highest efficiency of processes with machine learning. Leveraging predictive maintenance will help you to outrun your competition. Here you can learn more about AI development services.

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