Industrial Machine Learning: Failure Prediction and Energy Optimization
David Walter • 6 novembre 2025
Project Context
A Lyon-based company specializing in energy performance management through cooling sought to strengthen its competitive advantage.
Its dual objective was to:
- Reduce maintenance costs caused by unexpected machine failures
- Optimize energy consumption in its buildings while aiming for ISO certification to access subsidies
Objectives
The main goal was to develop a Machine Learning-based solution to:
- Anticipate and prevent critical equipment failures
- Reduce operational costs and extend equipment lifespan
- Optimize building energy consumption (based on occupancy, weather, and manual settings)
Mission Duration
A multi-month engagement including solution design, algorithm development, and knowledge transfer to internal teams.
Implementation
Agaetis implemented a Data & AI approach focused on business value:
- Diagnosis and scoping: Clarification of opportunities through the use of existing industrial data
- Predictive algorithm development: Design of advanced models to anticipate failures and detect anomalies
- Energy optimization: Creation of tools leveraging occupancy, weather, and manual control data to reduce energy consumption
- Knowledge transfer: Training and support for internal teams to ensure ownership and sustainability of AI models
Results Achieved
- Reduced unexpected failures: 20% decrease in critical incidents
- Energy optimization: Significant reduction in energy costs through smarter resource management
- Extended equipment lifespan: Improved preventive maintenance and machine responsiveness
- Increased Data/AI maturity: Predictive algorithms integrated into operational processes
Key Success Factors
- Agaetis’ expertise in Machine Learning applied to industry
- Combination of data science with the client’s business knowledge
- Implementation of training and skill transfer programs
- Pragmatic and results-oriented approach
And You?
Are you wondering about:
- Using Machine Learning to reduce maintenance costs?
- Optimizing your energy consumption?
- Implementing anomaly detection solutions tailored to your industrial environment?
👉 Contact our experts to turn your data into levers for industrial and energy performance.
Ressources Agaetis
Project Context Michelin aimed to develop a new generation of digital services based on data from connected tires. The goal was to deliver added value to truck fleet managers and maintenance centers worldwide. The challenge lay in the collection, processing, and supervision of massive sensor data, while ensuring robustness, scalability, and relevance of analytics. Objectives The main objective was to establish a technical and architectural framework to: Efficiently collect and process data from connected tires Monitor data flows and measure the impact of business use cases Create a Big Data platform ready to evolve with growing data volumes Mission Duration A long-term engagement , combining initial architectural study, implementation of monitoring systems, and ongoing support. Implementation Agaetis leveraged its Data and Cloud expertise to: Initial architecture study: Define technological and structural choices Platform monitoring: Implement monitoring principles to ensure robustness and availability Load analysis: Evaluate the impacts of various business use cases Big Data framework definition: Integrate best practices to accelerate implementation Data Science work: Perform domain-specific analysis and develop new indicators and services Results Achieved New digital services: Creation of innovative solutions for fleet managers Robust and scalable platform: A Big Data environment ready to handle massive data volumes Operational optimization: Improved traceability and KPI tracking Enhanced innovation: Data transformed into strategic levers for Michelin Key Success Factors Agaetis’ expertise in Big Data and IoT data processing End-to-end methodology: From architecture to Data Science Immersion in the client’s business environment Proactive supervision ensuring robustness and reliability And You? Are you wondering about: Leveraging data from your connected equipment ? Creating new digital services based on Data? Implementing a robust and scalable Big Data architecture ? 👉 Contact our experts to transform your IoT data into innovative, value-generating services.
Project Context France’s first research foundation dedicated to innovation in pain management aimed to launch a market-ready application resulting from its clinical research and development program. The goal was to transform the app into a Digital Therapeutic (DTx) reimbursed by the national health insurance system. Objectives The organization focuses on driving healthcare innovation through extensive collaborations with hospitals, research institutes, universities, and technology companies. The main challenges included: Transforming an application into a Digital Therapeutic (DTx) reimbursed by the national health system. Managing the transition of patients to this new platform. Preparing a new data warehouse to support scientific research. Mission Duration 3 collaborators over 3 years Methodology Agaetis provided its expertise through targeted and structured actions: Audit of existing systems: Evaluation of current infrastructure to identify needs and areas for improvement. Decision support for partner selection: Assistance in choosing competent and reliable technology partners. Technology advisory: Guidance on application architecture, security, and scalability to ensure long-term viability. Results Achieved Development of an application supporting patients in chronic pain management, progressing toward recognition as a reimbursable DTx. Rigorous technical assessment and selection of strategic partners. Adherence to roadmap milestones , ensuring steady progress and alignment with expectations. This project highlights how Agaetis leverages its technological and strategic expertise to transform challenges into innovative, effective solutions — creating tangible value for clients in the healthcare sector.
