Nearline PM for Energy Equipment
Wireless vibration sensor contributing to the early detection of bearing abnormalities.
In the energy industry, bearing abnormalities in rotating machinery are a significant issue that can lead to equipment downtime and decreased efficiency. Especially in power plants and facilities, early detection of bearing abnormalities is essential for stable operation and cost reduction. Nearline PM detects bearing abnormalities in high-frequency bands above 10 kHz, allowing for early identification of signs of failure and enabling proactive measures. 【Usage Scenarios】 - Power generation equipment - Plant equipment - Rotating machinery such as pumps, fans, and compressors 【Benefits of Implementation】 - Stable operation of equipment - Reduction in maintenance costs - Decrease in downtime due to failures
basic information
**Features** - Wireless vibration sensor that requires no wiring work - High-frequency support (17kHz) to detect initial abnormalities in bearings - Long battery life design with approximately 5 years of usage - Easy operation and diagnostics through a dedicated app - Can be retrofitted, making it ideal for small starts **Our Strengths** Asahi Kasei Engineering Corporation provides integrated engineering services from design and construction to maintenance, offering total support for our customers' equipment maintenance. We also provide technical support from qualified vibration diagnostics professionals, delivering optimal solutions tailored to our customers' needs from implementation to aftercare.
Price information
Please feel free to contact us.
Delivery Time
Model number/Brand name
Nearline PM
Applications/Examples of results
Rotating machines such as motors, fans, blowers, and pumps.
catalog（4）
Download All Catalogs
Recommended products
Distributors
Asahi Kasei Engineering Corporation is a company that conducts design, procurement, and construction (EPC) with an eye toward maintenance, and also proposes appropriate maintenance based on design data. In addition to maintenance, it is also possible to propose information systems that consider production efficiency and production losses. In terms of maintenance, we are developing equipment diagnostic devices that utilize digital technology.