Predictive maintenance technology serves as the health checkup system for smart factory equipment. A smart factory is an intelligent manufacturing system based on the Internet of Things (IoT), artificial intelligence (AI), and big data. Since the COVID-19 pandemic, the adoption of smart factories has accelerated, with automation and robotics emerging as major trends driven by rising labor costs. Smart factory systems are designed with optimized layouts and production lines, but a single machine failure can lead to major losses — including production downtime, raw and semi-finished material waste, and additional quality control costs. For this reason, predictive maintenance technology, which diagnoses and monitors equipment, is a crucial strategy for maintaining productivity in smart factories.
[Fig] The Need for Predictive Maintenance Technology
Predictive maintenance is a technology that inspects and monitors equipment to detect and predict abnormalities. It has evolved through four stages of technological development: ① Reactive maintenance – addressing issues only after failure occurs. This approach causes productivity loss since equipment stops running until repairs are completed. ② Planned maintenance – performing maintenance according to pre-set schedules. Technicians carry out maintenance based on each machine’s failure history and inspection cycle. However, this can waste resources because even fully functional machines undergo maintenance. ③ Proactive maintenance – identifying and removing potential defect factors in advance to improve equipment performance. While this minimizes downtime, it still results in resource inefficiency similar to planned maintenance. ④ Predictive maintenance – utilizing AI and big data to detect abnormal data and respond to failures in real time. This approach minimizes resource waste and maintains productivity through timely intervention. In this case, sensors for anomaly detection and data analysis for future anomaly prediction are essential. To achieve accurate diagnostics, research combining AI and big data is actively underway.
[Fig] Stages of Equipment Maintenance Technology
Korean company MovicLab Co., Ltd. (CEO: Wongun Lee) successfully raised 1.5 billion KRW in Pre-A funding with its vibration-based predictive maintenance solution. Its product, Watch Bat, uses an AI edge device that collects acoustic signals from equipment, measuring sound up to the ultrasonic range. Through deep learning–based signal pattern analysis, the system predicts the equipment’s operational status and failure risk. This enables real-time monitoring and early anomaly detection using sound signals. MovicLab’s solution can also be applied to facilities such as valves and pipelines, making it highly versatile. The company plans to accelerate R&D based on this investment, aiming for large-scale supply to major corporations next year, while expanding globally by leveraging its proprietary ultrasonic diagnostic technology.
[Fig] Overview of MovicLab Co., Ltd.’s Predictive Maintenance Solution
Among domestic companies, OnePredict Co., Ltd. (CEO: Byungdong Yoon) is particularly active in developing predictive maintenance technologies. OnePredict provides solutions such as GuardiOne Motor (current data–based motor failure prediction and diagnosis), GuardiOne Substation (big data–based transformer fault prediction), and GuardiOne Turbo (vibration and operational factor–based rotating machinery analysis). Recognized for its technological strength, the company secured 30 billion KRW (Series C) in funding. Recently, OnePredict developed a new technology that predicts future vibration patterns by analyzing historical vibration data and other factors, improving diagnostic accuracy and reducing management costs. Additionally, Korean company PNG Tech has developed a correlation analysis and CNN (convolutional neural network)–based algorithm that enables more accurate prediction of equipment abnormalities. As such, domestic companies are actively developing predictive maintenance technologies in line with the growing adoption of smart factories.
[Fig] Introduction to OnePredict Co., Ltd.’s Predictive Maintenance Solution
Government support for smart factory adoption is robust. In particular, the government is actively assisting small and medium-sized enterprises (SMEs) to strengthen competitiveness through smart manufacturing infrastructure. The Ministry of SMEs and Startups’ Smart Manufacturing Innovation Support Program provides up to 820 million KRW—covering up to 50% of total installation costs—for smart factory implementation. This level of support is expected to expand further in line with policies to boost SME competitiveness. As the smart factory market grows, the demand for predictive maintenance solutions that manage and monitor these systems will increase significantly. Companies that seize this opportunity are expected to lead the next-generation predictive maintenance market.
#PredictiveMaintenance #EquipmentDiagnosis #SmartFactory
Predictive maintenance technology serves as the health checkup system for smart factory equipment. A smart factory is an intelligent manufacturing system based on the Internet of Things (IoT), artificial intelligence (AI), and big data. Since the COVID-19 pandemic, the adoption of smart factories has accelerated, with automation and robotics emerging as major trends driven by rising labor costs. Smart factory systems are designed with optimized layouts and production lines, but a single machine failure can lead to major losses — including production downtime, raw and semi-finished material waste, and additional quality control costs. For this reason, predictive maintenance technology, which diagnoses and monitors equipment, is a crucial strategy for maintaining productivity in smart factories.
[Fig] The Need for Predictive Maintenance Technology
Predictive maintenance is a technology that inspects and monitors equipment to detect and predict abnormalities. It has evolved through four stages of technological development: ① Reactive maintenance – addressing issues only after failure occurs. This approach causes productivity loss since equipment stops running until repairs are completed. ② Planned maintenance – performing maintenance according to pre-set schedules. Technicians carry out maintenance based on each machine’s failure history and inspection cycle. However, this can waste resources because even fully functional machines undergo maintenance. ③ Proactive maintenance – identifying and removing potential defect factors in advance to improve equipment performance. While this minimizes downtime, it still results in resource inefficiency similar to planned maintenance. ④ Predictive maintenance – utilizing AI and big data to detect abnormal data and respond to failures in real time. This approach minimizes resource waste and maintains productivity through timely intervention. In this case, sensors for anomaly detection and data analysis for future anomaly prediction are essential. To achieve accurate diagnostics, research combining AI and big data is actively underway.
[Fig] Stages of Equipment Maintenance Technology
Korean company MovicLab Co., Ltd. (CEO: Wongun Lee) successfully raised 1.5 billion KRW in Pre-A funding with its vibration-based predictive maintenance solution. Its product, Watch Bat, uses an AI edge device that collects acoustic signals from equipment, measuring sound up to the ultrasonic range. Through deep learning–based signal pattern analysis, the system predicts the equipment’s operational status and failure risk. This enables real-time monitoring and early anomaly detection using sound signals. MovicLab’s solution can also be applied to facilities such as valves and pipelines, making it highly versatile. The company plans to accelerate R&D based on this investment, aiming for large-scale supply to major corporations next year, while expanding globally by leveraging its proprietary ultrasonic diagnostic technology.
[Fig] Overview of MovicLab Co., Ltd.’s Predictive Maintenance Solution
Among domestic companies, OnePredict Co., Ltd. (CEO: Byungdong Yoon) is particularly active in developing predictive maintenance technologies. OnePredict provides solutions such as GuardiOne Motor (current data–based motor failure prediction and diagnosis), GuardiOne Substation (big data–based transformer fault prediction), and GuardiOne Turbo (vibration and operational factor–based rotating machinery analysis). Recognized for its technological strength, the company secured 30 billion KRW (Series C) in funding. Recently, OnePredict developed a new technology that predicts future vibration patterns by analyzing historical vibration data and other factors, improving diagnostic accuracy and reducing management costs. Additionally, Korean company PNG Tech has developed a correlation analysis and CNN (convolutional neural network)–based algorithm that enables more accurate prediction of equipment abnormalities. As such, domestic companies are actively developing predictive maintenance technologies in line with the growing adoption of smart factories.
[Fig] Introduction to OnePredict Co., Ltd.’s Predictive Maintenance Solution
Government support for smart factory adoption is robust. In particular, the government is actively assisting small and medium-sized enterprises (SMEs) to strengthen competitiveness through smart manufacturing infrastructure. The Ministry of SMEs and Startups’ Smart Manufacturing Innovation Support Program provides up to 820 million KRW—covering up to 50% of total installation costs—for smart factory implementation. This level of support is expected to expand further in line with policies to boost SME competitiveness. As the smart factory market grows, the demand for predictive maintenance solutions that manage and monitor these systems will increase significantly. Companies that seize this opportunity are expected to lead the next-generation predictive maintenance market.
#PredictiveMaintenance #EquipmentDiagnosis #SmartFactory