AI : a valuable aid in predictive maintenance
Introduction
In today’s industrial landscape, predictive maintenance is becoming a crucial factor in industrial performance. Relying on data analysis and artificial intelligence, this approach makes it possible to anticipate equipment failures and proactively plan maintenance interventions, thus optimizing machine availability and reducing operating costs.
I. Different types of maintenance
To better understand predictive maintenance, let’s start by listing the other types of maintenance:
1/ Curative maintenance : repairing a fault after it has occurred. You have to be reactive!
2/ Corrective : Anticipating an imminent breakdown based on warning signs.
3/ Systematic Preventive : Perform maintenance tasks at regular, defined intervals. Can be costly and unnecessary!
4/ Conditional Prevention : Monitor the condition of equipment and carry out maintenance only when necessary.
5/ Predictive maintenance : Use analysis and algorithms to predict breakdowns before they occur. Maximizes reliability and profitability, the most advanced!
II. AI in all this?
AI is not just used for predictive maintenance, although it does play a crucial role.
For curative and corrective maintenance: AI can help diagnose faults quickly and identify faulty parts, thus reducing downtime.
In systematic preventive maintenance: AI can optimize maintenance plans by analyzing historical data and identifying equipment requiring special attention.
For condition-based preventive maintenance: AI can improve condition monitoring by analyzing real-time data from sensors and monitoring systems.
III. FOCUS: AI at the heart of predictive maintenance
AI plays a central role in predictive maintenance by analyzing large volumes of data from sensors installed on machines. This data, which reflects equipment operating parameters, enables AI to identify anomalies and patterns indicative of potential failures.
AI then uses this information to build predictive models that anticipate the probability and timeframe of a breakdown. These models take into account various factors, such as maintenance history, operating conditions and environmental data, to provide an accurate and reliable analysis.
But first, what is predictive maintenance ?
- Definition of predictive maintenance :
Otherwise known as “predictive maintenance”, it goes beyond the limits of corrective and preventive maintenance. It maximizes the uptime of parts while anticipating future failures thanks to data collected from IoT equipment.
In this way, predictive maintenance limits the number of breakdowns that will impact the production chain. This agile method enables maintenance operations to be triggered at just the right moment.
- Why implement predictive maintenance ?
The adoption of AI-based predictive maintenance in industries offers many considerable benefits, including the following :
- Reduce unplanned downtime and breakdowns :
By anticipating potential failures, companies can proactively plan maintenance, minimizing costly and unplanned production interruptions. This optimizes equipment availability and boosts overall productivity.
b. Improving equipment service life :
By constantly monitoring the condition of machines and identifying early warning signs of failure, predictive maintenance enables corrective action to be taken before serious damage occurs. This extends equipment life and reduces replacement costs.
c. Optimizing maintenance resources :
By proactively planning maintenance, companies can allocate their maintenance resources more efficiently, ensuring that qualified technicians are available at the right time to deal with problems. This reduces maintenance costs and improves overall efficiency.
d. Better decision-making :
AI provides actionable analytics and insights from collected equipment data, enabling companies to make informed maintenance decisions. This can include identifying failure-prone components, prioritizing maintenance tasks and optimizing preventive maintenance strategies.
e. Improving safety :
Predictive maintenance can help improve safety by reducing the likelihood of sudden equipment failures that could lead to accidents or injuries. This creates a safer working environment and reduces liability risks for companies.
f. Increase customer satisfaction :
By reducing unplanned downtime and improving equipment reliability, predictive maintenance can help increase customer satisfaction by ensuring timely delivery of products and services of consistent quality.
g. Competitive advantage :
Advantages of AI-based predictive maintenance can give companies a competitive edge by enabling them to optimize operations, reduce costs and improve customer satisfaction.
In short, implementing AI-based predictive maintenance offers a multitude of benefits that can transform industrial operations, leading to improved efficiency, reduced costs, enhanced safety and greater customer satisfaction.
IV. La démarche à suivre pour réussir son projet de maintenance prédictive
- La maintenance prédictive : un investissement rentable ?
Many manufacturers have already taken the plunge in terms of equipment, particularly in the aeronautics and rail transport sectors. The rise of Industry 4.0, linked to technological change (notably the integration of IoT equipment and associated data into the overall strategy), is gradually overturning the entire industrial sector, from SMEs to major corporations.
To stay on the cutting edge and avoid missing out on a crucial turning point in the life of your industry, particularly where maintenance is concerned, it’s essential to consider the challenge of predictive maintenance.
In 2023, a OnePoll study was carried out for Reichelt Elektronik. It showed that 29% of companies surveyed were reluctant to adopt predictive maintenance because of the cost involved.
2. Is there a real need?
If you don’t have many machines in your plant, you may be able to make do with preventive maintenance. However, as we have already seen, you need to be aware of the limitations of this method.
On the other hand, if your industrial park is made up of a hundred or so machines and produces several thousand part numbers, predictive maintenance is of direct concern to you. Especially if equipment breakdowns have a major impact on production, it may be wise to invest in IoT equipment that will subsequently enable analysis of the data collected. This is the case for industries specializing in the automotive or metallurgy sectors, for example.
In concrete terms, the costs of a predictive maintenance project can be high, and require a strong internal change process. However, this investment quickly pays for itself, as the returns are considerable.
3. L’apport de l’intelligence artificielle dans l’industrie 4.0 :
- Challenge : Data flooding and cognitive bias
The era of Big Data poses a major challenge: analyzing a multitude of data to identify those relevant to maintenance. In-house teams, prone to cognitive bias, can get lost in the shuffle.
- Solution : Outside view and business expertise
Artificial intelligence brings a fresh perspective: data scientists analyze the database without bias. In addition, the integration of business experts is crucial. Their knowledge of the field makes it possible to define a “criticality index” for each component, like the quality of the soil for an agricultural machine.
- Human-machine collaboration : modeling and learning
Collaboration between business experts and AI is essential. Together, they model machine and plant-specific incident and failure patterns. Machine Learning algorithms are then implemented to predict failures. Trained on data sets, these algorithms improve over time and adapt to non-programmed situations, illustrating the power of AI.
- Production start-up and continuous improvement
The predictive models, connected to the existing CMMS, enable proactive maintenance. AI continues to learn and refine its predictions over time, guaranteeing increased machine reliability and optimized operations.
Conclusion
AI is revolutionizing maintenance, making it more proactive, efficient and cost-effective. By leveraging the analytical and predictive capabilities of AI, companies can optimize operations, reduce costs and improve the safety of their facilities. Adopting AI-based predictive maintenance is a wise investment for companies wishing to increase their competitiveness and ensure the longevity of their assets.
Don’t forget that AI can also play a part in curative and corrective maintenance: AI can help diagnose faults quickly and identify faulty parts, thereby reducing downtime.
And, in systematic preventive maintenance : AI can optimize maintenance plans by analyzing historical data and identifying equipment requiring special attention.
But also, in condition-based preventive maintenance: AI can improve condition monitoring by analyzing real-time data from sensors and monitoring systems.