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TDK Launches SensEI edgeRX PRO Predictive Maintenance Sensor Node
Designed for harsh industrial environments, edgeRX PRO enables long-term monitoring with up to 10 years of battery life.
www.tdk.com
TDK Corporation has released the TDK SensEI edgeRX PRO, a high-performance sensor node designed to expand the edgeRX predictive maintenance platform. The unit combines multi-modal sensing and on-device machine learning in a compact enclosure to monitor machine health across manufacturing plants, energy utilities, HVAC systems, and industrial warehousing facilities.
Multi-Modal Sensing and Hardware Architecture
The edgeRX PRO integrates vibration, acoustic, magnetic, temperature, and rotational motion measurement capabilities within an IP67-rated industrial housing. Hardware inputs include an integrated six-axis inertial measurement unit (IMU), a three-axis magnetometer, and a digital acoustic microphone.
The device operates via internal battery power with an operational lifespan of up to ten years, or through a wired USB power connection. When connected to a wired source, the unit automatically prioritizes USB power, supporting higher data sampling frequencies and continuous execution of complex edge AI inference models during temporary testing or permanent installations.
Industrial Diagnostic Capabilities
The expanded sensory inputs and higher sampling rates support targeted diagnostic routines, including compressed air leak detection, acoustic anomaly tracking, shaft rotation and alignment evaluation, oil and gas leak identification, and mechanical bearing defect monitoring.
The node targets deployment across several industrial sectors:
- Manufacturing: Lithium battery production, semiconductor fabrication, precision machining, and iron and steel processing.
- Energy and Utilities: Electric power generation, natural gas distribution, and upstream oil and gas infrastructure.
- Facilities and Infrastructure: Smart building HVAC equipment, logistics warehousing, and heavy industrial machinery installations.
Additional Context
This section details technical specifications not included in the original news release.
Six-axis Inertial Measurement Units (IMUs) integrate a three-axis MEMS accelerometer and a three-axis MEMS gyroscope on a single silicon substrate. In predictive maintenance applications, high-bandwidth accelerometers capture high-frequency shock pulses and harmonic vibration patterns associated with inner and outer bearing raceway fatigue, gear mesh faults, and mechanical looseness. Digital MEMS microphones detect ultrasonic and acoustic emissions generated by high-pressure gas leaks or turbulent fluid bypass through failing seals, which typically emit energy in acoustic bands beyond baseline structural vibration.
Three-axis magnetometers in condition-based monitoring detect magnetic flux leakage and stator current asymmetries in induction motors, identifying rotor bar breakages, eccentric air gaps, and phase imbalances without invasive current clamps. Edge machine learning processing utilizes quantized neural network architectures, such as tiny convolutional networks or autoencoders running on low-power microcontrollers, allowing unsupervised anomaly detection directly on sensor nodes. IP67 ingress protection standards ensure complete protection against ingress of solid particulate matter and dust, as well as protection against harmful water ingress when submerged up to one meter deep for thirty minutes.
Edited by Romila DSilva, Induportals Editor, with AI assistance.
This section details technical specifications not included in the original news release.
Six-axis Inertial Measurement Units (IMUs) integrate a three-axis MEMS accelerometer and a three-axis MEMS gyroscope on a single silicon substrate. In predictive maintenance applications, high-bandwidth accelerometers capture high-frequency shock pulses and harmonic vibration patterns associated with inner and outer bearing raceway fatigue, gear mesh faults, and mechanical looseness. Digital MEMS microphones detect ultrasonic and acoustic emissions generated by high-pressure gas leaks or turbulent fluid bypass through failing seals, which typically emit energy in acoustic bands beyond baseline structural vibration.
Three-axis magnetometers in condition-based monitoring detect magnetic flux leakage and stator current asymmetries in induction motors, identifying rotor bar breakages, eccentric air gaps, and phase imbalances without invasive current clamps. Edge machine learning processing utilizes quantized neural network architectures, such as tiny convolutional networks or autoencoders running on low-power microcontrollers, allowing unsupervised anomaly detection directly on sensor nodes. IP67 ingress protection standards ensure complete protection against ingress of solid particulate matter and dust, as well as protection against harmful water ingress when submerged up to one meter deep for thirty minutes.
Edited by Romila DSilva, Induportals Editor, with AI assistance.
