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Early detection Product List and Ranking from 5 Manufacturers, Suppliers and Companies | IPROS GMS

Last Updated： Aggregation Period:Feb 04, 2026～Mar 03, 2026
This ranking is based on the number of page views on our site.

Early detection Manufacturer, Suppliers and Company Rankings

Last Updated： Aggregation Period:Feb 04, 2026～Mar 03, 2026
This ranking is based on the number of page views on our site.

  1. 旭化成エンジニアリング Tokyo//equipment
  2. キッツ Tokyo//Machine elements and parts
  3. シンテックホズミ Aichi//Automobiles and Transportation Equipment
  4. 4 TDSE Tokyo//Information and Communications
  5. 5 三和コンピュータ Tokyo//IT/Telecommunications
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Early detection Product ranking

Last Updated： Aggregation Period:Feb 04, 2026～Mar 03, 2026
This ranking is based on the number of page views on our site.

  1. Nearline-PM for Manufacturing: Equipment Anomaly Detection 旭化成エンジニアリング
  2. KISMOS Valve Monitoring System Remote Condition Diagnosis and Fault Prediction キッツ
  3. Predict equipment troubles! Detect abnormal sounds with AI! Streamline patrol inspections! シンテックホズミ
  4. 4 [Data Science Use Case] Fault Prediction Detection in Wind Power Generation Facilities TDSE
  5. 5 Fire prevention solution for detecting fire warnings in factories and similar environments. 三和コンピュータ

Early detection Product List

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Predict equipment troubles! Detect abnormal sounds with AI! Streamline patrol inspections!

Predict equipment troubles! Detect abnormal sounds with AI! Streamline patrol inspections!

Analyze sounds generated by machinery using AI. Automate inspection tasks that relied on human ears, and utilize it for predictive maintenance and anomaly detection!

The "FAST-D Monitoring Edition" is a service that can be utilized for preventive maintenance and predictive maintenance. It analyzes the sounds emitted by machines and equipment using AI, enabling early response to failures and determining the timing for parts replacement. ■Problems that can be solved - There are times when sudden failures occur, causing trouble. - Daily maintenance requires a lot of labor. - I want to predict failures, but I don't know the signs. - There is a shortage of personnel who can make good or bad judgments. - Machines and equipment are located in hard-to-access areas. - I want to monitor the condition, but the costs are too high. ■Cover multiple machines and equipment with one FAST-D Monitoring Edition It can determine abnormal sounds for an entire area (multiple units). (For 70db, it covers a radius of about 3-5m.) ★If you want to pinpoint the source of abnormal sounds, we recommend the following product. Product name: Abnormal Sound Detector (IoN SHINTEC) https://www.ipros.jp/product/detail/2000035887

  • Other measurement, recording and measuring instruments
  • Early detection

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Fire prevention solution for detecting fire warnings in factories and similar environments.

Fire prevention solution for detecting fire warnings in factories and similar environments.

Introducing a thermal camera that measures body surface temperature and monitors temperature to detect signs of fire!

Detects far infrared rays that cannot be captured by the human eye, allowing for the visibility of targets, measurement of surface body temperature, and temperature monitoring even in places without a light source. Sanwa Computer leverages this feature to capture abnormal temperature changes in objects, detect abnormal temperature rises that could lead to fires, and provide fire prevention solutions that enable prompt action. 《Measuring the surface temperature of objects captured by the camera → Detecting and notifying abnormal temperature rises that are invisible to the eye → Enabling prompt action》 Details of the fire prevention solution: https://www.sanwa-comp.co.jp/solution/p-security/firesign_detection.html Utilizing thermal cameras to measure temperature for fire prevention, which is surprisingly not well known: https://www.sanwa-comp.co.jp/article/column/fireprevention

  • Other safety equipment
  • Other security
  • Early detection

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[Data Science Use Case] Fault Prediction Detection in Wind Power Generation Facilities

[Data Science Use Case] Fault Prediction Detection in Wind Power Generation Facilities

Preventing non-operational hours caused by malfunctions in advance! A case of developing AI to detect signs of failure.

We will introduce a case where fault prediction detection was realized in wind power generation facilities. When a wind turbine experiences a failure, unplanned downtime occurs. By detecting signs of failure, we aimed to improve the efficiency of maintenance and inspections and enhance the operational efficiency of wind turbines. To address this, we developed an AI to detect failure signs from operational sensing data. This resulted in reduced operational costs through more efficient maintenance and inspection work, as well as improved operational rates by preventing unexpected accidents. 【Case Overview】 ■Industry: Social Infrastructure ■Business: Operations and Maintenance ■Challenge: Improvement of Inspection Efficiency ■Analytics and AI Solution - Determine abnormal conditions when deviating from a steady state - Predict whether equipment in an abnormal state will fail subsequently *For more details, please refer to the PDF document or feel free to contact us. - Related link - https://www.tdse.jp/case-study/fault-detection/

  • Company：TDSE
  • Price：Other
  • Embedded system design service
  • Early detection

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KISMOS Valve Monitoring System Remote Condition Diagnosis and Fault Prediction

KISMOS Valve Monitoring System Remote Condition Diagnosis and Fault Prediction

Contributing to the digital transformation of maintenance operations through state diagnosis and early fault detection from valve monitoring information using proprietary sensing, IoT, and AI technologies.

"KISMOS" is an air-operated actuator used in plants and factories, retrofitting IoT sensors to rotary valves such as ball valves and butterfly valves for on/off control, visualizing the valve's status and diagnosing its condition. Furthermore, it utilizes AI to visualize the changes in the valve's condition through trend graphs. It contributes to the digital transformation of maintenance operations by detecting signs of trouble and accumulating condition data. [Value Provided] - Labor-saving in valve maintenance operations (addressing labor shortages, reducing maintenance costs) - Maintaining and improving the quality of maintenance operations (retirement of veteran workers, experience-based decisions → data-driven decisions) - Preventing valve troubles before they occur (preventing unnecessary costs and wasted time, minimizing impacts on production plans) - Reducing maintenance costs (implementing only necessary maintenance through transitioning from TBM to CBM) [Features of KISMOS] - KISMOS can be started without initial investment (cost and time) - Leave the monitoring with KISMOS to us (contributing to labor-saving in monitoring tasks) - There are no restrictions on the manufacturers of the valves that can be monitored (compatible with other manufacturers' valves) - The IoT sensors are explosion-proof (Ex ia II CT4 Gb), suitable for valves in explosion-proof areas.

  • Other security and surveillance systems
  • Early detection

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Nearline-PM for Manufacturing: Equipment Anomaly Detection

Nearline-PM for Manufacturing: Equipment Anomaly Detection

No wiring required for easy installation! Early detection of equipment anomalies reduces maintenance costs.

In the manufacturing industry, production stoppages due to equipment failures can lead to significant losses. In particular, abnormalities in rotating equipment such as motors, pumps, and fans require early detection. Nearline-PM continuously monitors the vibrations of these machines, capturing signs of potential failures and enabling planned maintenance. This reduces the risk of unexpected breakdowns and supports stable operations. 【Application Scenarios】 - Rotating equipment such as motors, pumps, fans, and compressors - Abnormality detection in bearings - Predictive maintenance of equipment 【Benefits of Implementation】 - Reduction in maintenance costs - Prevention of unexpected failures - Improved equipment condition monitoring and efficiency on-site

  • Vibration Inspection
  • Vibration Monitoring
  • Safety Sensors
  • Early detection

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