Predictive Maintenance: Beyond All-AI, A Gradual Approach to Industry 4.0

In modern industry, maintenance has become a strategic element ensuring companies’ competitiveness and operational efficiency. Faced with the increasing complexity of production processes and more stringent standards, companies must learn to effectively balance optimizing their operations while controlling their costs.

Predictive maintenance has emerged as a promising solution, enabling the anticipation of breakdowns or defaults and avoiding the unnecessary replacement of equipment parts that have not been used or are still in good working condition. However, this approach requires significant investments in technology and skills.

The central question, therefore, is to determine the best strategy to optimize performance while ensuring the safety and compliance of processes. Companies must assess whether they are ready for predictive maintenance or if more accessible intermediate solutions can already improve their operational efficiency.

Limitations of Traditional Maintenance and the Emergence of Modern Solutions

Traditional maintenance primarily relies on two approaches: reactive maintenance and preventive maintenance. Reactive maintenance involves intervening only when a breakdown occurs, often leading to costly and unexpected production stoppages. This approach, focused on quickly repairing equipment to minimize production downtime, has major shortcomings: the lack of failure anticipation can expose a company to significant risks and can result in substantial financial losses, both in terms of emergency repair costs and lost revenue due to unplanned production interruptions.

In contrast, preventive maintenance involves scheduling interventions at regular intervals, based on equipment wear, usage time, or manufacturer recommendations. This method reduces the risk of major failures but is not always optimal, as it sometimes leads to unnecessary repairs or premature replacement of parts that could have lasted longer.

These traditional methods have several major limitations. Unplanned production stoppages remain possible, even with rigorous planning, and preventive maintenance can generate unnecessary costs. Moreover, these strategies do not optimize human and material resources, as interventions are not always aligned with the actual condition of the equipment. Finally, the lack of failure anticipation can lead to reduced reliability and performance of equipment, with consequences for production and product quality.

Faced with these limitations, a number of companies have begun adopting more modern solutions, such as augmented and predictive maintenance. These solutions, based on advanced technologies, offer promising prospects for cost reduction, improved equipment availability, and enhanced operational safety.

Advantages and Challenges of Modern Maintenance

Modern maintenance approaches, whether augmented or predictive, offer significant benefits. They rely on advanced digital technologies that assist technicians and enhance their on-site efficiency. These approaches help reduce unplanned downtime by anticipating failures and optimally scheduling interventions, thus resulting in better equipment availability and fewer production interruptions.

These technologies also enable optimization of human and material resources: through real-time data collection and analysis, companies can deploy technicians only when an intervention is truly necessary, improving their efficiency while reducing costs associated with unnecessary interventions. Furthermore, these technologies allow for better management of equipment and spare parts consumption while enhancing operational safety by detecting anomalies before they cause severe failures that could endanger personnel or compromise product integrity. By anticipating breakdowns, predictive maintenance also helps prevent quality issues in production by avoiding defects that could impact product compliance. Finally, they assist in anticipating equipment obsolescence by identifying parts or machines that need to be replaced before they become major points of failure.

However, transitioning to modern maintenance solutions presents numerous challenges. First, integrating these technologies requires significant initial investments, both in terms of tools and training for personnel. For example, regular updates to hardware, firmware, and software systems may be necessary, representing a substantial cost for companies. Additionally, these augmented and predictive maintenance technologies rely on sensors and IoT (Internet of Things) devices that require meticulous maintenance, ie. to ensure precise calibration and the reliability of collected data. Such maintenance demands effective change management, as technicians need to acquire new skills and adapt their conduct to transformed workflows. Finally, modern maintenance solutions can also expose companies to additional cybersecurity risks, as connected systems are vulnerable to attacks, requiring increased vigilance in protecting data and infrastructures.

It is essential to carefully evaluate the cost of implementing these solutions against the optimization gains they generate. If the return on investment is not properly assessed, the company could face significant financial risks in the event of incomplete or poorly adapted technology deployment.

Thus, while modern maintenance offers significant advantages, particularly in terms of cost reduction and performance optimization, its implementation requires thorough reflection and substantial investment. Each company must carefully assess its needs and capabilities before taking the step towards a technology shift.

Modern Solutions: Focus on Augmented Maintenance

Augmented maintenance relies on the use of advanced technologies such as smart sensors and real-time data analysis, to provide a better understanding of equipment performance and ensure more targeted interventions. This approach enhances the efficiency of maintenance teams by providing precise information that reduces human error and maximizes resource utilization.

Augmented reality systems, which allow technicians to visualize technical information overlaid directly on equipment, remote maintenance interfaces that enable off-site interventions, and real-time data collection and analysis platforms that provide a dynamic view of machine status through interactive dashboards, are among key augmented maintenance technologies.

The practical applications of these technologies are particularly impressive. Continuous monitoring of equipment, made possible by sensors measuring parameters such as vibrations, temperature, or energy consumption, allows real-time tracking of “machine health”, with dynamic dashboards that instantly alert technicians to even the slightest anomalies. Predictive analyses based on deep learning can now estimate with remarkable accuracy the remaining lifespan of components, enable the optimal scheduling of maintenance interventions, and significantly reduce the risks of unexpected failures.

Nevertheless, implementing predictive maintenance raises considerable technological and organizational challenges for manufacturers. Data collection and management represent a primary level of complexity, with the quality of collected data becoming a key area of concern. The reliability, completeness, and consistency of collected data must be ensured to guarantee the accuracy of predictive models. With an exponential volume of generated data, manufacturers require powerful and scalable storage and processing infrastructures and must consider the security of sensitive data against cyber threats as a major strategic concern.

The need for advanced analytical and technological skills constitutes another critical challenge faced by manufacturers. Companies must recruit or train hybrid profiles combining expertise in mechanical engineering/automation, data science/data engineering, and artificial intelligence. These experts must be capable of building complex predictive models, interpreting them, and integrating them into operational processes. The scarcity of such talent in the job market creates significant competitive pressure.

Finally, as with augmented maintenance solutions, integration with existing maintenance management systems represents the final strategic challenge. This involves aligning cutting-edge digital technologies with often heterogeneous and inflexible legacy systems, requiring a rigorous approach, significant technological investments, and effective change management within organizations.

Numerous practical applications of these technologies exist. For example, remote assistance allows an expert to guide an on-site technician by transmitting precise visual instructions, thereby reducing travel and intervention times. Likewise, real-time data visualization offers instant diagnostic capabilities, enabling the anticipation of potential failures and more accurate preventive maintenance planning, while smart sensors, coupled with artificial intelligence algorithms, can now reliably predict the optimal times to perform maintenance operations.

However, the implementation of these technologies raises several challenges for manufacturers. Integrating these new digital tools into existing systems represents a major technical challenge, which often involves combining modern technologies with older equipment and necessitates advanced technological adaptations and significant investments in digital infrastructure.

Training personnel becomes a strategic element in ensuring the adoption and optimal use of these new technologies. Technical teams must not only acquire new digital skills but also develop a culture of innovation and continuous adaptation at the heart of their company. Comprehensive training programs, combining theoretical learning and hands-on field practice, are essential for successfully navigating this transition.

Focus on Predictive Maintenance

Predictive maintenance, on the other hand, relies on the analysis of real-time data collected through IoT sensors extensively deployed on equipment, combined with advanced machine learning and deep learning algorithms capable of analyzing millions of data points in just a few seconds. This revolutionary approach aims to anticipate and prevent failures before they occur, thus radically transforming traditional maintenance strategies. The primary objective is to shift from a reactive to a proactive and strategic approach to equipment maintenance.

Numerous practical applications of these technologies exist. For example, remote assistance allows an expert to guide an on-site technician by transmitting precise visual instructions, thereby reducing travel and intervention times. Likewise, real-time data visualization offers instant diagnostic capabilities, enabling the anticipation of potential failures and more accurate preventive maintenance planning, while smart sensors, coupled with artificial intelligence algorithms, can now reliably predict the optimal times to perform maintenance operations.

However, the implementation of these technologies raises several challenges for manufacturers. Integrating these new digital tools into existing systems represents a major technical challenge, which often involves combining modern technologies with older equipment and necessitates advanced technological adaptations and significant investments in digital infrastructure.

Training personnel becomes a strategic element in ensuring the adoption and optimal use of these new technologies. Technical teams must not only acquire new digital skills but also develop a culture of innovation and continuous adaptation at the heart of their company. Comprehensive training programs, combining theoretical learning and hands-on field practice, are essential for successfully navigating this transition.

Examples of Modern Maintenance Implementation in Industry

In the pharmaceutical sector, intelligent maintenance of equipment and production environments plays an important role in improving productivity, reducing costs, and ensuring compliance with regulatory standards. For instance, in the specific context of air quality management in production areas, it is essential to maintain positive pressures to prevent contamination by external agents and negative pressures to stop potentially hazardous substances from escaping. An Environmental Monitoring System (EMS) collects real-time data via various sensors, enabling continuous monitoring through dashboards. This proactive approach optimizes production conditions, thereby reducing the risks of non-compliance and the costs associated with corrective interventions.

Similarly, this type of approach, when applied to Air Handling Units (AHUs) in Controlled Atmosphere Zones (CAZ), is of critical importance. Through vibration analysis, it becomes possible to detect early signs of failures, allowing intervention before a breakdown occurs. This approach reduces the high risks associated with AHU stoppages and ensures the continuity of operations in sensitive environments.

Another example concerns Overall Equipment Effectiveness (OEE), a key performance indicator in the industry. By analyzing data from automation systems, companies can identify downtime linked to various events, such as material loading, maintenance or calibration sessions, or shift changes. By adjusting scheduling based on this information, it is possible to optimize production lines, improve productivity, and ensure that manufacturing targets are met efficiently.

These examples illustrate how the integration of advanced technologies into maintenance and industrial processes can lead to significant gains in productivity, cost reduction, regulatory compliance, and contribute to more sustainable and resource-efficient practices. By transforming data into actionable insights, companies can anticipate problems before they occur, thereby optimizing their operations and strengthening their competitiveness in the market.

 
Toward a Comprehensive or Intermediate Modern Maintenance Solution?

The transition to augmented and predictive maintenance holds strategic importance for industrial companies seeking to remain competitive in a constantly evolving environment. However, this transition should not be seen as requiring sudden or immediate changes. A gradual approach is not only possible but also recommended for companies that wish to integrate these solutions while minimizing associated risks and costs. Contrary to the idea that complex systems must be adopted immediately, companies can opt for intermediate solutions that allow them to build expertise and test the benefits of modern technologies before making significant investments.

Among these intermediate solutions, a first accessible step is the implementation of simple dashboards to visualize real-time data. Installing sensors on strategic equipment can also help collect essential information about the condition of machines without requiring a massive technological deployment and investment. Additionally, training teams on new digital tools is a key step to ensuring their progressive adoption of technologies while fostering a culture of innovation within the company.

The choice between a comprehensive or intermediate solution will depend on the specific needs of each company, its level of technological maturity, and its investment capacity. Some companies, facing critical reliability or competitiveness challenges, may opt for a rapid and complete transformation. Others, however, may prefer a gradual adoption, starting with targeted experiments before deploying solutions on a larger scale.

In terms of timeline, it is essential for companies to incorporate their technological transformation into a medium- and long-term strategy. The goal is to rethink maintenance processes as part of a larger holistic transition strategy and not merely to align technology investments with Industry 4.0 requirements. This gradual approach will not only maximize return on investment but also ensure a smooth and controlled transition to augmented and predictive maintenance solutions.

Conclusion

Transitioning to modern maintenance approaches, whether augmented or predictive, presents a major opportunity for companies to optimize costs and improve performance. However, this transformation can occur progressively, tailored to each company’s needs. The key lies in transforming collected data into actionable strategic insights. By taking the first step and adopting a continuous improvement mindset, companies can turn maintenance into a true competitive advantage.

 
Need Help?

Our multidisciplinary teams can assist you with the following:

• Integration and analysis of multi-source data
  • Collection and processing of IoT sensor data
  • Analysis of machine parameters (vibrations, temperatures, energy consumption, etc.)
  • Application of machine learning and deep learning techniques for anomaly detection
• Development of customized predictive models
  • Creation, validation, and optimization of models tailored to the specificities of industrial equipment
  • Estimation of the remaining useful life of components
  • Optimized planning of maintenance interventions
• Methodological and technical support
  • Assistance in integrating real-time monitoring systems
  • Support for implementing supervision dashboards
  • Guidance in the digital transformation of maintenance processes
• Strategic support and decision-making assistance
  • Definition of analytical pipelines adapted to specific needs
  • Development of roadmaps for transitioning to predictive maintenance
  • Fast and precise support through a dedicated hotline

Contact us at onedt@efor-group.com to receive personalized support tailored to your specific needs.