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

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PLCs
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IoT edge gateways
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Smart turbines and generators
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Temperature sensors
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Pressure sensors
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Acoustic sensors
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Oil quality sensors
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Shaft speed and position sensors
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Current and voltage sensors
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Bearing condition sensors
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Cooling system sensors
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Fuel supply sensors
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Exhaust gas sensors
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Strain gauges
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Thermal cameras
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Vibration sensors
  • 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

Connectivity and Management

  • 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

Integration

  • 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

Analytics

  • 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

UI/UX

Key Features
of Your Turbogenerator Unit Predictive Maintenance Solution

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
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Proved Worldwide
Across Industries
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15 000 000 000 000+

data samples collected and processed

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25 000 000+

devices under monitoring and supervision

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40 000+

types of devices and data sources connected via more than 50 supported communication protocols

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300+

SaaS and on-premise partner products created

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80+

countries leverage Iotellect technology

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