Orise Digital AI Features
Built for the Future of Manufacturing: Discover Orise Digital AI’s Features
Orise Digital AI
Anomaly Detection
Orise Digital AI’s ‘Anomaly Detection’ feature predicts issues before they happen by learning the normal behavior of sensors, control loops, equipment, machines, and processes, and detecting deviations.
It includes self-learning functionality to detect unexpected changes in machine settings or setpoints, alerting you to modifications outside of maintenance operations or identifying parameters altered before a problem occurred.
It detects unusual combinations of values, patterns, and sensor signal behaviors, and can also pick up abnormal control software execution. Our proprietary unsupervised machine learning algorithms sense deviations from normal operations and indicate what is abnormal about the machine or process.
This feature informs you about the condition of your assets, speeding up root-cause analysis and fixing technical issues. For frequently occurring assets like motors, drives, pumps, control loops, and valves, Orise Digital AI has dedicated anomaly detectors incorporating expert knowledge about equipment issues and their impact on related sensor signals.
Predictive Models
Predictive modeling with Orise Digital AI allows you to build machine-learning models that predict failures (prognostics) or product properties (virtual sensors) and deploy them in production.
With self-service tools, domain experts can build models to anticipate unwanted events such as technical issues for predictive maintenance or quality problems. Typical applications include virtual sensors predicting lab measurement results of product quality characteristics.
Data pre-processors, predictive models, and graphical user interfaces developed specifically for industrial applications are available at your fingertips.
Micro-Stop Detection
Orise Digital AI’s ‘Micro-Stop Detection’ feature detects and analyzes invisible micro-stops in real-time for faster troubleshooting.
It continuously monitors the production or packaging line’s digital I/O data, reporting deviations from normal operation that cause slowdowns. This feature can be added to any production or packaging line with digital I/O data accessibility. If data collection is not yet in place, we offer dedicated software solutions to collect data locally at high speed.
Unique AI algorithms specifically developed for this purpose are used. Key benefits include increased production throughput, improved overall equipment effectiveness (OEE), enhanced troubleshooting, and effective reporting of micro-stop locations via web browser on tablet or PC.
Soft Sensing Monitor
The ‘Soft Sensing Monitor,’ also known as Virtual Sensors, predicts values of hard-to-measure or expensive-to-measure variables based on other available data and sensors.
This predictive modeling allows for the creation of machine-learning models that forecast failures (prognostics) or product properties, which can then be deployed in production. For example, in the pulp and paper industry, Orise Digital AI has created soft sensors predicting moisture values and paper strength, allowing for better control and cost savings.
Asset Health Performance
Orise Digital AI offers both generic anomaly detectors and dedicated asset-specific health detectors for frequently occurring equipment like motors, VSDs, pumps, control loops, compressors, and heat exchangers.
These specialized detectors combine AI capabilities with built-in human expertise to effectively monitor these assets. The ‘Asset Health Performance’ feature continuously assesses and monitors the condition and performance of critical production equipment, detecting degradation and predicting potential failures to ensure optimal operation and prevent unplanned downtime.
It can be integrated into any production data collection database or IIoT platform. Key benefits include cost savings through predictive maintenance, enhanced production efficiency, and extended equipment lifetime by early detection of asset health issues.
Golden Production Run Advisor
The ‘Golden Production Run Advisor’ provides real-time advice to operators on achieving optimal production runs, often referred to as “golden” runs.
This functionality uses AI-based multivariate centerlining to identify the process variables and settings most important for achieving the best production outcomes. It learns how these variables interact and determines the best settings and ranges for each product and different operating conditions.
By comparing current process values against their optimal target values, it alerts operators to deviations that may impact performance, helping maintain consistent product quality, reduce waste, and improve overall production efficiency.
This feature ensures the process runs with the centerlined settings through visual suggestions, alerts, or even automatic control if desired.
Control Performance Degradation Detection
Orise Digital AI’s Control Performance Degradation Detection (CPDD) monitors and detects degrading performance of control loops and their actuators, such as control valves.
This feature combines human expertise with artificial intelligence to ensure optimal performance and stability in production processes. CPDD calculates control performance metrics like saturation, oscillation, and imbalance from typical control loop-related process data, including setpoint, process variable, output variable (valve position), and controller mode.
It can also incorporate optional context variables indicating operating conditions. The AI component learns the normal performance for each control loop and alerts users if performance worsens. This approach reduces manual interventions, increases equipment lifetime, and improves product quality by minimizing fluctuations in process variables.
CPDD is part of a broader suite of functionalities that include anomaly detection, sensor validation, dynamic centerlining, and predictive models, collectively contributing to data-driven maintenance, improving overall equipment effectiveness (OEE), and reducing costs.