Predictive Maintenance

Solution Group

Industry

Infrastructure Management
Any

PoC Duration

Iotellect Match

1-5 days
AI powered Low Code IoT/IIoT Platform

Bring a univocal business value materialized by higher equipment uptime, lower OPEX due to reduced repair costs, and stricter Service Level Agreements (SLAs) through a predictive maintenance solution developed to your demands. Leverage low code dataprep and machine learning framework for trend recognition, anomaly detection and more.

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

Predictive Maintenance Devices

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PLCs
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IoT edge gateways
for on-premise data aggregation
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3rd party SCADA and DCS software
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Sensors
for telemetry and telematics (e.g. vibration, temperature, amperage, differential pressure, and more)
  • Out-of-the-box support for most industrial protocols and standards including Modbus, OPC DA/HDA, OPC UA, MQTT, IEC-104, etc.
  • Reliable compressed and encrypted bridging of all collected data from different edge servers to the public or a private cloud
  • Native support for industrial data series with device-side timestamps and quality metrics

Connectivity and Management

  • Flexible southbound integration with SCADA and MES systems
  • Low code integration with CMMS and MRO maintenance management software
  • Northbound integration with ERP systems for amending machine data with various parameters

Integration

  • Best-in-class data preparation environment based on visual workflows, expression and query languages, and other advanced tools
  • Flexible machine learning module with dozens of algorithms and hundreds of hyperparameters
  • Evaluation of two key equipment health indicators, Time to Failure (TTF) and Remaining Useful Life (RUL)
  • Assessment of remaining operating cycles, mileage, transaction counts, and other machine-specific metrics
  • Reporting through various statistical diagrams with correlation to external sources (e.g. seasonal demands, change in raw material grade, process revision, etc.)
  • Identification of deviations from normal operating conditions that may indicate impending failures
  • Analysis of machine downtime trends and their effects such as production gaps, employee overtimes, final product/commodity quality variations, supply-chain gaps, etc.
  • Evaluation of maintenance costs against potential risks and operational impacts to prioritize maintenance actions
  • Optimization of maintenance schedules based on predictive insights to minimize downtime and maximize asset availability

Analytics

  • Data science console for preparing datasets and training models
  • BI-style dashboarding for proper visualization of ML-based statistics
  • Organization of assets into categories or hierarchical structures for easier navigation and management
  • Equipment and process specific visualization of cost and performance for "prepare vs repair" analysis
  • Presentation of scenarios on equipment failures and maintenance recommendations
  • Data drill down for investigation of failure and anomaly root causes in complex fault scenarios

UI/UX

Key Features
for Your Predictive Maintenance System

Customers and Partners

  • System Integrators
  • Independent Software Vendors
  • Manufacturing/Production Industries
  • OEMs/ODMs

Solution Users and Developers

  • Dedicated low code developers
  • SCADA/HMI engineers
  • Maintenance engineers
  • Data scientists and analysts
  • Industrial process engineers
Customer success team
Community
Online training

Assistance