Iotellect - Predictive Maintenance

Solution Group

Industry

Infrastructure Management

Multiple

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

PLCs

IoT edge gateways

for on-premise data aggregation

3rd party SCADA and DCS software

Sensors

for telemetry and telematics (e.g. vibration, temperature, amperage, differential pressure, and more)

Connect Devices

Connectivity & Management

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

Integration

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

Analytics

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

UI/UX

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

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

Predictive Maintenance