HmcOmm Announces Results of Leak Detection Model Demonstration in Moriyama City and Initiatives for Next-Generation Leak Monitoring

Key facts

  • HmcOmm Announces Results of Leak Detection Model Demonstration in Moriyama City and Initiatives for Next-Generation Leak Monitoring
  • HmcOmm Inc. has confirmed the effectiveness of its AI-based wide-area risk analysis and acoustic analysis AI in a leak detection demonstration project with Moriyama City, Shiga Prefecture. The company plans to evolve towards a 'Leak Monitoring AI' that continuously monitors for signs of leaks, aiming to realize a next-generation infrastructure management model.
  • Source: PR Times
  • Date: June 13, 2026

Direct answer

HmcOmm Inc. has confirmed the effectiveness of its AI-based wide-area risk analysis and acoustic analysis AI in a leak detection demonstration project with Moriyama City, Shiga Prefecture. The company plans to evolve towards a 'Leak Monitoring AI' that continuously monitors for signs of leaks, aiming to realize a next-generation infrastructure management model.

Citation
HmcOmm Announces Results of Leak Detection Model Demonstration in Moriyama City and Initiatives for Next-Generation Leak Monitoring (June 13, 2026), PR Times
Source
PR Times
Date
June 13, 2026
HmcOmm Inc. has confirmed the effectiveness of its AI-based wide-area risk analysis and acoustic analysis AI in a leak detection demonstration project with Moriyama City, Shiga Prefecture. The company plans to evolve towards a 'Leak Monitoring AI' that continuously monitors for signs of leaks, aiming to realize a next-generation infrastructure management model.

📋 Article Processing Timeline

  • 📰 Published: June 13, 2026 at 01:30
  • 🔍 Collected: June 12, 2026 at 16:51
  • 🤖 AI Analyzed: June 12, 2026 at 18:15 (1h 24m after Collected)
HmcOmm Inc. (Headquarters: Minato-ku, Tokyo; President and CEO: Koji Sambon; hereinafter referred to as "HmcOmm") announced today its policy to confirm the effectiveness of its leak detection model based on subsequent demonstration progress and to advance initiatives towards realizing next-generation leak monitoring AI, following the demonstrations conducted with Moriyama City, Shiga Prefecture, as previously announced on October 24, 2025, in "Decision on the Direction of the Demonstration Phase for the Leak Detection System Using Satellite Data x FAST-D" and on April 3, 2026, in "Announcement Regarding the Results of the AI Leak Detection Demonstration Experiment in Moriyama City and Transition to the Social Implementation Phase (Progress of Disclosure Matters)". 1. Demonstration Results In this initiative, we conducted wide-area risk analysis utilizing water pipe information, geospatial data, satellite data, and repair history, as well as verification of a leak detection model using HmcOmm's acoustic analysis AI technology. The following results were confirmed in the demonstration with Moriyama City: Confirmed the effectiveness of AI-based wide-area leak risk analysis. Confirmed high-precision classification of leaks and non-leaks by leak sound analysis AI. Discovered and confirmed leaks at multiple locations through on-site surveys targeting high-risk areas. Confirmed the effectiveness of the leak detection process from wide-area analysis to on-site investigation. 2. Initiatives for Next-Generation Leak Monitoring Through this demonstration, the effectiveness of the leak detection model combining wide-area risk analysis and acoustic AI has been confirmed. Based on these findings, HmcOmm will proceed with initiatives to evolve from "Leak Detection AI" for discovering leaks to "Leak Monitoring AI" for continuously monitoring the signs of leaks. In the future, by combining wide-area risk analysis AI, acoustic judgment AI, and monitoring IoT, we aim to realize a next-generation infrastructure management model that prioritizes monitoring high-risk areas and supports early detection of abnormalities and preventive maintenance. Furthermore, we will proceed with considerations for transitioning from conventional Time-Based Maintenance (TBM) to Condition-Based Maintenance (CBM), which determines priorities based on equipment status. [Supplementary Material] Regarding the Demonstration Results of the Leak Detection Model in the Moriyama City Demonstration About HmcOmm Inc. President and CEO: Koji Sambon URL: https://hmcom.co.jp Established: July 24, 2012 Location: PMO Hamamatsucho III, 4th Floor, 2-10-6 Hamamatsucho, Minato-ku, Tokyo Business Activities: Research and development of elemental technologies based on artificial intelligence (AI) voice processing technology, and provision of solutions/services. For Media: HmcOmm Inc. IR Department hm_ir@hmcom.co.jp For Corporations: HmcOmm Inc. Sales Division sales_team@hmcom.co.jp TEL: 03-6550-9830 FAX: 03-6550-9831

FAQ

What technologies does HmcOmm's AI leak detection system use?

It utilizes water pipe information, geospatial data, satellite data, repair history, and proprietary acoustic analysis AI technology to classify the presence of leaks with high precision.

What specifically was confirmed in the Moriyama City demonstration experiment?

The effectiveness of AI-based wide-area risk analysis, high-precision leak determination by acoustic analysis AI, leak discovery through on-site surveys, and the overall leak detection process were confirmed.

What is the evolution from Leak Detection AI to Leak Monitoring AI?

The aim is to evolve beyond simply detecting leaks to a system that continuously monitors for signs of leaks, supporting early abnormality detection and preventive maintenance.

How will this technology impact infrastructure management?

It supports the transition from conventional periodic inspections to Condition-Based Maintenance (CBM), contributing to increased efficiency, cost reduction, and enhanced preventive maintenance in infrastructure management.

What are HmcOmm's strengths?

Our strengths lie in our proprietary technological capabilities based on AI voice processing technology and the proven practicality demonstrated through our field experiments. We aim to solve social issues in the infrastructure sector.