Vision AI Assistant

What is Vision AI Assistant?

Integrated with AVEVA’s HMI and SCADA software and AVEVA™ Insight, Vision AI Assistant employs deep learning to train machine learning models. Generating alerts when an image deviates from its expected state and alert operators to take immediate action, Vision AI Assistant can increase situational awareness, lengthen equipment uptime, and prevent machine failure.

Anomaly detection

This method utilizes an unsupervised machine algorithm to determine if specific images represent anomalies.  Users train the algorithm with images that represent the expected outcome or “good” condition. The solution then determines anomalies—including novel anomalies—if real-time images do not match training images.

Discrete state detection

This method utilizes a supervised deep learning algorithm to distinguish between two known states. After users train the algorithm with categorized examples, the algorithm builds a general model of categories. It then attempts to assign unlabeled items to pre-learned categories.

User Defined Pipeline (UDP)

This is the most sophisticated skill type for advanced image processing prior to model detection. Users self-specify a series of automated steps in a chain for pre-processing and transformation of desired images. The input image travels through a series of defined processing blocks, transformations, or actions (“the pipeline”) to achieve the desired prediction output.

Context-aware alerts for AVEVA™ System Platform

Vision AI Assistant OMI App is an easy addition for an AVEVA System Platform application that taps into the context-aware environment of AVEVA™ Operations Management Interface. Automatically link alerts with specific areas of your process and interact with Vision AI Assistant.

Increased efficiency through identifying anomalies

Increase your efficiency and improve operator awareness by using image streams alongside HMI/SCADA data to identify visual anomalies.

Operations

Employ infrared or thermal cameras to identify visual anomalies. For example: Locate compressed air and vacuum leaks to reduce energy waste. Analyze electrical partial discharge patterns to minimize failures and downtime.

Quality

Use cameras to ensure quality control. For example: Determine fluid quality in water treatment, gauge if a hopper is full, or use in scenarios where quality control is subjective, such as identifying an item’s color.

Maintenance

Monitor machinery to provide early warning notification and diagnosis of equipment issues days, weeks, or months before failure. For example: Rely on cameras for overhead transportation such as chain or cart breakage.