INTECH excels in utilizing deep learning algorithms to detect multivariate anomalies in industrial assets, preventing costly downtime, enhancing safety, and preemptively identifying patterns to prevent future asset failures.

Key features

• Real-time monitoring
• Multivariate anomaly detection
• Adaptive learning-based algorithm
• Proactive monitoring

Our diagnostic tools allow you to deep dive into the underlying issues that are often overlooked. With the help of our neural networks, we can categorize specific types of faults and take relevant corrective measures to address them.

Key features

• Advance diagnostic analysis
• Flexible
• Integrated sensor analysis
• Fault classification

Be one step ahead through our prognostic tools as they enable you to accurately predict the remaining useful life (RUL) of equipment, identify failure types and recommend maintenance schedules in advance to minimize downtime and prevent excessive expenses.

Soft sensing deals with rudimentary issues pertaining to desired product quality by ensuring operations within the set critical parameters. It generates ML models based on historical data to provide insights which can be utilized to improve processes.

Our algorithm assists in generating “golden profile “of an ideal batch which can be used as a benchmark for other batches to compare. This profile can seamlessly be integrated within the existing system and can identify any deviations of forthcoming batches in real-time.

Boost production yield with recurrent neural networks (RNNs) and deep learning architectures that accurately forecast demand by capturing temporal dependencies and complex patterns. These models stand out for their scalability, flexibility, and ability to handle large datasets.

Our maintenance cost predictor can help curb excessive costs and prioritize activities that are within your budgetary constraints. The model incorporates various factors like equipment age, history, type, and model and utilizes advanced statistical techniques to predict costs for a determinable future horizon.

Our automated solution replaces labor-intensive methods, using standardized inspection with cameras positioned above the production line. High-quality product images are fed into our deep learning models, which employ convolutional neural networks (CNNs) to accurately identify defects. Sensitivity of the system can be adjusted as needed.