In an industrial context where every minute of downtime represents a significant cost, anticipating breakdowns becomes a major challenge. Our client, operating a production line composed of several machines (press, conveyor, assembly unit, etc.), suffered from frequent unexpected interruptions. These not only caused delays in deliveries but also high costs related to emergency interventions. The maintenance teams always intervened reactively: a malfunction occurred, then an intervention was launched, often with heavy consequences for overall production.

Project Objectives

To reduce these incidents and improve operations planning, we proposed testing an artificial intelligence solution on a pilot machine in the chain. Specifically, this involved:

• collecting essential data from this machine (electrical measurements, speed, temperature),
• integrating a predictive model capable of learning its normal operation and automatically generating alerts when behavior deviated from usual values. The idea was to start with a limited scope to validate the approach, then, in case of success, extend the solution to all equipment.

1. Implementing the Pilot Phase

Simplified Data Collection

Rather than adding new sensors, we exploited measurements already available on the pilot machine:

• electrical voltage and current,
• rotation speed,
• temperature of critical components. This information was continuously transmitted to local infrastructure dedicated to preprocessing (data storage and preparation for analysis).

AI Model Integration

Our model was deployed directly on the pilot machine. Its role was to continuously learn the equipment's normal behavior, based on historical and real-time data. With each measurement received, the model evaluated whether parameters remained within the expected range:

• If everything proceeded normally, no signal was generated.
• As soon as a significant deviation was detected (for example, excessive vibration or abnormal temperature rise), an alert was triggered.

Alert Management and Monitoring

Each alert sent to the maintenance team now included, in addition to a clear message free of jargon, the probable cause of the malfunction (for example: abnormal vibrations related to excessive rotor rotation):
"This machine presents a risk of breakdown in the next 48 hours due to excessive vibrations of element X. Please check this element and schedule an intervention."
Thanks to these details, technicians could:

• quickly diagnose the problem's origin,
• schedule a targeted intervention without stopping the entire line,
• or, if necessary, postpone the intervention based on production requirements.

2. Deployment and Adjustments

Pilot Phase (3 months)

1. Critical Machine Selection
   We chose the machine most exposed to unexpected downtime to maximize the impact of initial results.
2. Historical Data Preparation
   One month of readings was used to train the model to recognize normal operation under various conditions (light loads, heavy loads, ambient temperature variations).
3. Simulations and Adjustments
   Several scenarios were simulated to validate alert relevance:

• Gradual threshold adjustment to limit false alarms.
• Regular meetings with maintenance teams to collect feedback and refine model sensitivity.

Real Implementation

Once the pilot phase was validated:

• The model ran continuously, generating each morning a simple report on the machine's condition.
• Technicians followed brief training to read these reports and interpret alerts without getting lost in technical details.

3. Results Achieved

Unplanned Downtime Reduction

Within the first two months of real operation, the number of unexpected stops for the pilot machine decreased by approximately 30%. Teams were able to shift from purely reactive maintenance to a proactive approach, thus reducing immobilization times.

Financial Savings

By avoiding serious breakdowns, the company achieved savings on emergency spare parts and interventions outside normal hours. These cost reductions translated into concrete return on investment from the first quarter.

Organizational Improvement

Maintenance interventions became more predictable: rather than chasing breakdowns on weekends, technicians could schedule their checks at the beginning of the week, integrating scheduling constraints.

Simplified Traceability

Each alert and corrective action were recorded in a document accessible to all. This system now provides clear history, facilitating analysis of past incidents and future planning.

4. Benefits and Perspectives

Benefits for the Company

• Reduced production losses: fewer unexpected stops, therefore fewer interruptions in the chain.
• Reduced emergency costs: fewer urgent part purchases and less overtime.
• Better production time control: simplified intervention planning.
• Extension simplicity: the method can be progressively deployed on all machines without unnecessarily complicating the system.

Next Steps

1. Deployment on Other Equipment
   After success on the pilot machine, the objective is to cover the press, conveyor, then assembly unit.
2. Unified Dashboard
   Implement a global dashboard to centralize all machine conditions, offering real-time vision across entire production.
3. Additional Variables Integration
   Consider integrating new data (finer vibrations, compressed air consumption, etc.) to strengthen predictions while preserving ease of use.

Conclusion

This case study illustrates that an artificial intelligence-based project doesn't need to be complex to generate rapid value. By targeting a critical machine and favoring simplified data collection, we were able to:

• Shift from reactive to predictive maintenance,
• Significantly reduce unplanned downtime,
• Achieve substantial savings. The progressive approach, focused on the clarity of information provided to technical teams, not only reinforced operators' confidence in adopting this solution but also laid the foundation for smooth extension to the entire production chain. Today, the company benefits from a simple and reliable tool to anticipate breakdowns, plan interventions, and optimize their machine fleet.