Practical Know-How Seminar on AI and Machine Learning Applications for Industrial Equipment Held

The IR Engineer Education Institute is hosting an online seminar that explains the principles of neural networks and reinforcement learning, as well as the essentials of time-series data collection, data preparation for learning, and IoT system construction from an implementation perspective.

In the fields of manufacturing sites and commercial building facilities, the use of AI for system control and anomaly detection is expanding. However, on-site implementation often faces "practical challenges" that are difficult to solve with theory alone, such as constraints specific to each piece of equipment, information security, bias in collected data, and ensuring the quantity and quality of data required for learning. Based on these challenges, this seminar provides "field-ready" implementation know-how from both theoretical and practical perspectives.

Seminar Overview - Seminar Name: Practical Know-How for Applying AI and Machine Learning to Industrial Equipment - Format: Online (Zoom LIVE streaming / Archive streaming) - Date and Time: [LIVE Streaming] May 26, 2026 (Tue) 10:00–16:00 [Archive Streaming] May 28 – June 11, 2026 - Capacity: 20 people - Tuition: 49,500 yen per person (tax included) - Instructor: Tadazo Ninagawa (Representative Director, N Research Co., Ltd.)

Course Details In this seminar, after providing an overview of industrial applications of machine learning, participants will learn step-by-step about modeling control targets, predictive equipment maintenance, reinforcement learning for facility management, and tips for learning data collection and system design. We will focus specifically on "data collection" and "preparing data for use in machine learning," which are often bottlenecks in the field, and cover concepts such as ideal collection distribution and how to handle cases with limited data (SMOTE method). Furthermore, we will provide an overview of the internal principles of Transformer, the foundation of generative AI—which is attracting attention as a cutting-edge technology—and explain industrial application examples (research cases) such as time-series trend forecasting and event occurrence prediction.

Seminar Program 1. Overview of Industrial Applications of Machine Learning (Research example videos / On-site application / Practical samples) 2. Modeling of Control Targets (NN basics / Step response / Black-box models for multivariable control) 3. Predictive Equipment Maintenance (LSTM / Sudden event prediction / Accuracy evaluation metrics) 4. Reinforcement Learning for Facility Management (Q-learning / Transfer learning / Optimal economic operation / Shortening learning periods) 5. Practicalities of Learning Data Collection (Quantity and quality / Ideal distribution / SMOTE / Tools vs. custom-built) 6. Tips for Realistic System Design (Initial strategy / Target selection / Team formation / Limitations of tools) 7. Advanced Industrial Applications of Generative AI Technology (Transformer overview / Time-series forecasting / Event prediction) 8. Summary and Q&A

Target Audience - Those who want to learn specific methods and case studies for introducing machine learning to industrial sites - Engineers and team leaders involved in development, design, and production management related to system control - Personnel from equipment/machinery manufacturers, infrastructure/industrial system manufacturers, civil engineering/construction, and related companies - Those who have already begun implementation but are struggling with on-site issues (data, operations, design)

* For more details on this seminar, please visit: https://nihon-ir.jp/seminar/ai_industrial-equipment_know-how/

The IR Engineer Education Institute will continue to provide useful knowledge and know-how for the field through technical education services for the manufacturing industry (seminars, e-learning, training, and publishing).