Iotellect - Turbogenerator Unit Predictive Maintenance

Solution Group

Industry

Smart Generation

Energy and Utilities

PoC Duration

Iotellect Match

10-20 days

AI powered Low Code IoT/IIoT Platform

Optimize performance and lifespan of power turbines and other large rotating machines through real-time monitoring and advanced analytics based on machine learning. Our platform helps collect and analyze critical operating data to detect anomalies, predict failures, and recommend maintenance actions. Reduce unplanned downtime, extend equipment life, and maximize energy generation with a comprehensive predictive maintenance strategy driven by your own custom-fit solution.

Iotellect is a low code IoT/IIoT development platform. It does not work as an out-of-the-box solution or product. Instead, it helps you to monetize your IoT know-how by dramatically lowering labor costs and cutting time-to-market for your product, service, or solution. It allows business-oriented IoT professionals to join the development process by converting their IoT market knowledge directly into product features and specific value delivered to your end customers.

Build Your IoT Application

Connect Your

Machines and Equipment

PLCs

IoT edge gateways

Smart turbines and generators

Temperature sensors

Pressure sensors

Acoustic sensors

Oil quality sensors

Shaft speed and position sensors

Current and voltage sensors

Bearing condition sensors

Cooling system sensors

Fuel supply sensors

Exhaust gas sensors

Strain gauges

Thermal cameras

Vibration sensors

Connect Devices

Connectivity & Management

Vibration, temperature, pressure, and acoustic sensors connect with Iotellect's support for over 100 communication protocols, ensuring real-time data acquisition
IoT edge gateways normalize data to facilitate efficient and continuous monitoring of generator winding and shaft speed sensors
Device snapshots ensure uninterrupted monitoring and data synchronization from oil quality and bearing condition sensors even during network disruptions
Implement a networked solution that continually assesses the cooling system for potential failures, optimizing energy generation and reducing maintenance efforts
Leverage acoustic sensors to detect early-stage mechanical issues within turbogenerators, prolonging equipment lifespan through targeted interventions
Utilize data from pressure sensors to create dynamic predictive maintenance models that anticipate system wear before manifesting critical issues
Offer off-the-shelf integration for both legacy and modern industrial systems using Iotellect's flexible driver module for seamless expansion

Integration

Integrate Iotellect with CMMS platforms through low-code connectors, facilitating automated scheduling of turbogenerator maintenance tasks based on sensor alerts
Establish connectivity with BMS platforms via low-code integration pathways, providing a comprehensive energy management interface for plant operators
Low-code connectors enable the seamless exchange of real-time condition monitoring data between shaft speed sensors and fleet management systems
Harmonize vibration sensor analytics with ERP databases for integrated reporting on asset health, performance metrics, and lifecycle costs
Connect exhaust gas measurement systems to environmental compliance modules via low-code connectors for regulatory alignment
Ensure compatible data streaming between predictive maintenance alerts and inventory management software for the timely procurement of replacement parts
Integrate temperature sensor outputs with third-party analytics platforms using flexible IoT protocols to refine predictive maintenance algorithms continuously
Facilitate intelligent routing of predictive alerts from fuel supply sensors into automated response workflows

Analytics

Vibration sensor data is analyzed using machine learning models to predict potential equipment failures
The granulation module allows custom time slices, enabling detailed analysis of temperature fluctuations recorded by winding sensors during peak operational hours
Machine learning algorithms run directly on edge devices assist in fault detection by correlating pressure changes across multiple data streams
Embed cooling system sensors within a feedback loop to autonomously optimizes HVAC usage based on real-time energy demand forecasts
Employ predictive maintenance analytics on shaft speed sensor data to minimize downtime by dynamically scheduling servicing at optimal intervals
Identify operational anomalies in generator behavior using advanced acoustic signal processing to preempt major shutdowns
Custom machine learning workflows use input from oil quality sensors to predict lubrication failures and suggest adjustments in operation parameters
Statistical Process Control tracks historical trends across wear indicators in bearing condition sensors, guiding proactive maintenance scheduling

UI/UX

The visual GUI Builder supports dynamic dashboards showcasing real-time performance metrics from oil quality and vibration sensors on any web-enabled device
Tailor dashboards using interactive Web UI components to visualize temperature fluctuation patterns detected by winding sensors during high-load scenarios
Design intuitive graphical displays that overlay pressure sensor readings onto operational maps, providing comprehensive situational awareness at a glance
Incorporate secure remote access features for operators to monitor signals in real-time from multiple turbogenerators across dispersed locations
Create custom compliance reports leveraging filtered analytics from cooling system readings
Configurable alerts relay immediate status updates on vital shaft speed parameters, empowering rapid assessment and decision-making processes
Use the advanced HMI builder to construct facilities-wide visualizations that highlight potential exhaust gas anomalies

Key Features of Your
Turbogenerator Unit Predictive Maintenance Solution

Smart Energy

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Customers and Partners

System Integrators
Independent Software Vendors
Engineering Companies
Original Equipment Manufacturers
Power Generation Companies

Solution Users and Developers

Dedicated low code developers
Power engineering specialists
Data scientists and analysts
Predictive maintenance experts

Customer success team

Community

Online training

Turbogenerator Unit Predictive Maintenance