Predictive Maintenance

Equipment Checks Have Relied on Eyes and Ears

Equipment and machinery on the shop floor vary widely depending on the products being made, and many work environments are not as clean as a semiconductor fab. As a result, staff and plant managers check for problems themselves by looking, listening, and running test operations. But this approach makes it hard to spot failures in advance and requires constant attention to keep equipment from stopping.

Detect Failures Early Through Vibration

Vibration sensors are attached to equipment, and features for each failure type are extracted from the measured vibration data. AI models quickly analyze and classify vibration and electrical frequency data to monitor for anomalies and predict failures, so teams can respond before equipment stops. Work becomes more efficient in every process, and production becomes more stable.

• One-Class LSTM
• BiLSTM
• Autoencoder
• PCA
• TadGAN

Get Started Quickly with a Site-Specific Setup

Identify the equipment that shows vibration before failure, attach vibration and frequency sensors suited to it, and predictive maintenance can begin right away. Hongik Information Technology packages its accumulated AI models, datasets, and smart factory implementation experience as modules that can be applied on site in a short time, combining web technology and AI to further improve work efficiency.