Skip to Content

Digital Health Rounds

Automation of Mobile Inspections Using AI


1. Current Challenges

  • Manual processes: Equipment inspections require manual form filling, verification with documentation, and visual inspection, which takes 30-50% of specialists' time.​
  • Human factor: Risk of errors in defect assessment (up to 15% of cases) and missing critical deviations.
  • Delays in analysis: The bypass data is processed retroactively, which slows down the response to malfunctions.
  • High costs: Equipment downtime due to untimely repairs costs industrial companies 5-20% of their annual budget.

2. Power of AI

The global experience demonstrates the successful implementation of various AI technologies in inspection and monitoring processes in industrial enterprises and in the energy sector:

  1. AI assistants for accessing documentation and data:
    • Integration with databases of technical documentation, standards, and historical data on equipment status.
    • Using voice and text queries for instant information retrieval in field conditions.
    • Examples: solutions from IBM Maximo Visual Inspection, Siemens MindSphere, Honeywell Forge.
  2. Computer vision technologies:
    • Analysis of images and videos using computer vision algorithms to detect defects, leaks, corrosion, overheating, and other anomalies.
    • Automatic comparison of the current state of the equipment with reference parameters.
    • Examples: systems from companies like FLIR Systems (thermal cameras with AI), drones with computer vision (for example, from DJI).
  3. Speech recognition and logging:
    • Automation of the process of recording notes and filling out forms during inspections through voice commands.
    • Speech-to-text conversion and data structuring in mobile applications.
    • Examples: voice assistants based on Amazon Alexa for Business, Google Cloud Speech-to-Text API.
  4. AI for data analysis and predictive maintenance:
    • Using predictive analytics for forecasting failures and planning repairs.
    • Machine learning models for assessing equipment wear.
    • Examples: GE Predix, Schneider Electric EcoStruxure.
  5. Integration of IoT and mobile devices:
    • Use of wearable devices (smart glasses, augmented reality helmets) to display real-time information about equipment status.
    • Examples: Microsoft HoloLens, RealWear HMT-1.

3. Features 

Based on the analysis of the best global practices, key elements of the mobile inspection automation system can be identified:

  1. Mobile application with AI assistant:
    • Voice and text control.
    • Instant access to documentation, instructions, and data on previous inspections.
    • Functions for filling out forms and logging through voice commands.
  2. Computer vision:
    • Automatic analysis of images taken with smartphone cameras, drones, or stationary devices.
    • Defect recognition (cracks, corrosion, deformations) and equipment condition monitoring in real-time.
  3. Augmented Reality (AR):
    • Use of wearable devices to overlay information on real objects (for example, displaying points of potential defects).
  4. Predictive analytics:
    • Data processing from IoT sensors and mobile devices for failure prediction.
    • Generation of recommendations for addressing identified issues.
  5. Centralized control system:
    • Integration with EAM/CMMS and ERP/APS.
    • Consolidation of data on equipment status, repair plans, and inspection results.

4. Value

System capabilities:

  • Operational detection of equipment defects with minimization of the human factor.
  • Transparency and completeness of data on equipment status.
  • Acceleration of the process of walkthroughs and inspections.
  • Automatic logging and reduction of reporting time.
  • Fault prediction and repair planning automation.

Achievable business effects:

  1. Time savings:
    • Reduction of time for inspections and data analysis by 30-40%.
  2. Improving service quality:
    • Elimination of errors due to human factors.
    • More accurate identification of problem areas and their documentation.
  3. Extension of equipment resource:
    • Timely detection of malfunctions and prevention of emergency situations.
  4. Cost savings:
    • Reduction of costs for unplanned repairs and downtime.
    • Reduction of labor costs.
  5. Improving work productivity:
    • Route optimization for inspections.
    • Reduction of workload on specialists.
  6. Reduction of risks for personnel:
    • Elimination of the need for direct contact with potentially hazardous areas through the use of drones and remote IoT data collection.

5. Implementation

  • Payback period: 12-18 months (for a company with 500+ units of equipment).​
  • Implementation stages:
    1. Pilot on critical assets (turbines, compressors, generators).
    2. Integration with ERP.
    3. Employee training.
  • Cost: From 50K USD (SaaS model) to 2M USD (custom turnkey solution).

6. Summary

The integration of AI into mobile inspection processes in industry and energy can significantly enhance their efficiency. Implementing such a system requires substantial investments; however, the resulting business effects allow for a quick return on investment through increased equipment reliability, reduced downtime, and improved labor productivity of specialists.

Using AI Solutions to Enhance Electricity Metering Systems