What is Nexen Predict?
Nexen Predict is an advanced AI-powered solution designed for predictive maintenance and energy forecasting. It uses machine learning models to detect early signs of equipment failure based on data gathered by sensors—including triaxial vibrations, temperature, and other operational parameters—helping organizations avoid costly downtime and extend machinery lifespans.
The system also includes an AI model that forecasts energy price trends, indicating whether prices are likely to rise or fall. This allows companies to optimize production scheduling, particularly energy-intensive processes, by aligning operations with the most cost-effective periods.
Key features of Nexen Predict
Comprehensive sensor integration
Collects data from vibration, thermal, acoustic, and electrical sensors connected through industrial protocols such as Modbus, OPC UA, MQTT, and Ethernet/IP.
Anomaly and trend detection
Identifies anomalies and performance trends through real-time analysis of key operational parameters—voltage, frequency, temperature, humidity, CO₂ levels, and air quality.
AI-driven analytics and predictive maintenance
Uses machine learning models and pattern recognition to detect early signs of equipment failure and classify failure types, preventing unplanned downtime and extending asset lifespan.
Energy price forecasting
Forecasts dynamic price trends for electricity, gas, and other utilities to support smarter purchasing decisions and production planning.
Health index and diagnostics dashboard
Displays a dynamic “health score” for each machine, with trend graphs, alerts, and plant-wide status visualization to simplify diagnostics and maintenance prioritization.
Smart operations and resource planning
Optimizes scheduling of production and facility operations based on utility costs, tariffs, and predicted demand—particularly valuable for energy-intensive processes.
Integration with maintenance and enterprise systems
Automatically generates work orders and maintenance schedules in ERP or CMMS systems, with native integration to SCADA, MES, and other enterprise infrastructures.
AI-powered conversational agents
Provides access to advanced analytics and expert knowledge through conversational AI agents, enabling intuitive data exploration and decision support.
Flexible deployment
Available as a cloud-based (Azure) or on-premises solution, easily integrating with existing IT and OT ecosystems.
Business benefits of Nexen Predict
Prevent costly equipment failures
AI models detect early signs of equipment degradation, allowing organizations to prevent failures and avoid unplanned downtime.
Reduce maintenance costs
Predictive insights enable planned maintenance, reducing emergency repairs and optimizing service operations.
Extend asset lifespan
Continuous monitoring helps detect mechanical stress and anomalies early, extending the lifespan of critical equipment.
Optimize production with energy forecasts
Energy price forecasting helps schedule energy-intensive operations during the most cost-effective periods.
Improve operational visibility
Real-time analytics and health dashboards provide clear insight into equipment condition and operational performance.
Strengthen resilience to energy price volatility
Predictive insights into energy market trends help organizations plan procurement and operations more strategically.
Nexen Predict use cases
Energy price forecasting and predictive models
- Unpredictable fluctuations in electricity and gas prices on the energy market.
- Dependence on external conditions such as weather (temperature, sunlight, wind) and renewable energy supply.
- Lack of forecasting tools to anticipate price changes.
- Difficulty in aligning production schedules with favorable energy cost periods.
- Limited ability to plan budgets and energy procurement strategies.
The solution: Nexen Predict
Nexen Predict leverages advanced AI models to forecast dynamic energy prices and support smarter production and purchasing decisions. The goal is to deliver accurate short-term price forecasts, capturing both overall trends and daily fluctuations—so that companies can better plan operations and take advantage of favorable cost periods.
How it works:
- Machine learning–based forecasting uses dozens of parameters, combining market data such as energy prices, power demand, renewable energy generation, and system status with weather information (temperature, precipitation, cloud cover, and wind speed) to predict energy trends up to seven days in advance.
- Market trend analysis identifies expected rises and falls in energy prices, helping to plan production during the most cost-effective time windows.
- Smart production scheduling aligns energy-intensive processes with periods of lower energy costs.
- Strategic procurement supports purchasing decisions and contract negotiations based on reliable, data-driven forecasts.
Result:
By combining Nexen EMS with Nexen Predict, companies gain predictive insight into market dynamics, enabling proactive production planning, reduced energy spending, and greater operational resilience in a volatile energy market.
Predictive maintenance
- Unplanned downtime disrupting production and delivery schedules.
- High maintenance costs due to reactive servicing and part replacements.
- Lack of real-time visibility into equipment health and operating conditions.
- Inability to detect anomalies early enough to prevent failures.
- Shortened asset lifespan caused by undetected mechanical stress or imbalance.
The Solution: Nexen Predict
Nexen Predict uses AI-powered predictive maintenance models to continuously monitor equipment condition and predict potential failures before they occur. By analyzing data from vibration, temperature, acoustic, and electrical sensors, the system identifies subtle deviations that indicate wear, imbalance, or malfunction.
How it works:
- Comprehensive sensor integration: Collects data from triaxial vibration, temperature, and other sensors via standard industrial protocols (Modbus, OPC UA, MQTT, Ethernet/IP).
- Anomaly and trend detection: Analyzes operational parameters in real time to detect abnormal behavior or performance degradation.
- AI-driven analytics: Uses machine learning and pattern recognition to classify failure types and estimate Remaining Useful Life (RUL) for each component.
- Health index dashboard: Provides an intuitive overview of each asset’s condition, displaying dynamic “health scores,” alerts, and performance trends.
- Maintenance integration: Automatically generates maintenance tasks and work orders in ERP or CMMS systems, enabling proactive scheduling.
Result:
With Nexen Predict, manufacturers move from reactive to predictive maintenance—reducing downtime, extending asset life, and optimizing maintenance costs.
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