Maximize uptime,
minimize downtime
Predictive maintenance:
Optimizing maintenance with real-time monitoring and anomaly detection
AI-powered predictive maintenance provides real-time monitoring and proactive anomaly detection, enabling optimal maintenance strategies. By continuously monitoring the health of process-critical facilities and equipment, it predicts failures, maximizing equipment uptime and minimizing unplanned downtime. This approach reduces costs and enhances operational efficiency.
MakinaRocks excels in dynamic industrial environments such as semiconductor component production equipment, large-scale chemical plant processes, and advanced manufacturing with robotic arms. We ensure reliable data collection and rapid deployment using proven AI models. Beyond merely detecting abnormal patterns, we delve into root cause analysis and provide adaptive solutions that empower field workers to swiftly resolve issues and flexibly respond to changes in the production environment.
MakinaRocks excels in dynamic industrial environments such as semiconductor component production equipment, large-scale chemical plant processes, and advanced manufacturing with robotic arms. We ensure reliable data collection and rapid deployment using proven AI models. Beyond merely detecting abnormal patterns, we delve into root cause analysis and provide adaptive solutions that empower field workers to swiftly resolve issues and flexibly respond to changes in the production environment.
Approach
01
Proprietary technology
Apply our proprietary RaPP (Reconstruction along Projection Pathway) algorithm to boost the accuracy of autoencoder-based predictive maintenance AI models
Determine the best retraining timing, data selection, and methods using our advanced technology, keeping AI models accurate and reliable
Combine diverse data sources into a single model to accurately predict equipment and facility failure
02
Deep learning-based AI models
Detect abnormal patterns with deep learning-based predictive maintenance models, surpassing traditional rule-based systems
Ensure reliable predictions by training models on normal data distributions
Identify changes in data distribution with advanced pre- and post-processing methods that integrate data analysis and domain expertise
03
Organized operating environment
Monitor equipment status and health in real-time with an intuitive dashboard
Receive proactive alarms and cause analysis for quick maintenance
Leverage an AI platform to automate dataset selection and retraining, ensuring a stable and efficient operating environment
Capabilities
Comprehensive equipment monitoring with 1PageProfile
Efficiently monitor equipment status at a glance with 1PageProfile. Access sensor data, alerts, inspection records, installation photos, and product images on a single page. Quickly locate equipment on interactive factory layouts for swift issue resolution.
Predictive anomaly detection with SmartAlert
Stay ahead with AI-powered SmartAlerts, developed by ML experts. Instantly view sensor data charts at the first sign of anomalies and quickly create alert reviews. Easily assign tasks, generate reports, and access inspection histories for immediate response.
Sensor data analysis with versatile viewing modes
Gain unmatched flexibility in sensor data analysis with diverse viewing modes. Compare single or multiple datasets, including an overlap mode for detailed comparisons.
