Predicting equipment troubles! Detecting abnormal sounds with AI! Streamlining patrol inspections!
Analyze sounds generated by machinery using AI. Automate inspection tasks that were previously reliant 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 - Sudden failures can be troublesome. - 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 places. - 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, the range is approximately 3-5 meters in radius) ★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
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basic information
■System Overview Edge terminals (mini PCs) and microphones are installed on-site, and you can use the system simply by accessing a dedicated cloud from your PC. *An internet connection is required separately. ■Basic Functions - Sound Collection: Collect sounds when operating normally. - Model Creation: Create an AI model from the collected data. - Monitoring: Compare the created model with sounds after operation begins, displaying differences in sound as numerical values. Review recorded data during abnormal judgment. - Other Settings: Adjust abnormal judgment levels and notification settings, etc. Reference: What is AI Abnormal Sound Detection? AI abnormal sound detection is a technology that uses machine learning to differentiate between sounds produced when machines, objects, or living beings are operating normally and when they are in an abnormal state, aiding in stable monitoring, abnormal detection, and anomaly prediction. It is an initiative to teach AI the insights that skilled craftsmen use to make judgments, and it is believed that "everything that can be detected by the human ear is detectable." Sound-based abnormal sound detection can be utilized across a wide range of industries and sectors, including abnormal detection in factory infrastructure, non-destructive testing, machine sound detection, as well as footstep detection, security, and sounds produced by humans and animals.
Price information
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Delivery Time
※About 1.5 months
Applications/Examples of results
■Purpose We excel at detecting abnormalities in rotating machinery such as pumps, belt fans, and motors. ■Practical Example: Reduced workload of patrol inspection tasks by 15% Although new employees join every month, the continuous turnover has prevented the accumulation of know-how in equipment patrol tasks, leading to a situation where only veteran workers are conducting the inspections. By utilizing the FAST-D Monitoring Edition, the explanation of inspection items related to sound has become easier, allowing us to delegate tasks to new team members. ★If you want to pinpoint the source of abnormal noise, we recommend the following product. Product Name: Abnormal Noise Detector (IoN SHINTEC) https://www.ipros.jp/product/detail/2000035887
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Based on the experience cultivated in the automotive industry, we will contribute through solutions utilizing transport robots (Automated Guided Vehicles - AGV), VR, and manual production. By integrating robotics and digital technology, we support all aspects of our customers' business development, production, sales, and services through solution proposals. <Official Website> https://www.shcl.co.jp/ <Official SNS> ◆YouTube: https://www.youtube.com/channel/UCrYf9b5DMkJcpsz1sG3W42w ◆Instagram: https://www.instagram.com/shintechozumi_official/?hl=ja