Request For Quotation

Request For Information

RotoSense™


Category: Sentinel Motion

Ridgetop Group’s RotoSense advanced rotational vibration sensor (RVS) enables easy extraction of high-resolution acoustic signatures from rotating components in harsh environments.
Ridgetop’s innovative Internet of Things (IoT)-compatible RotoSense wireless instrument (shown below) helps engineers perform dynamic analysis of rotating interaction and develop improved designs for rotating components. In addition, data from this instrument can be used with Sentinel Motion algorithms that detect and predict faults based on external acoustic signatures, for prognostic purposes. The result is an improvement in safety, performance, and maintenance costs for future generations of rotating components

Overview

  • Application: Rotary/ Vibrational Equipement
  • Sensor:RotoSense Pinion/Planetary
  • Data Type:Rotational/Vibrational Measurement
  • Reasoner: ARULE
  • GUL: Sentinel Dashboard

Description

Ridgetop’s innovative Internet of Things (IoT)-compatible RotoSense wireless instrument (shown below) helps engineers perform dynamic analysis of rotating interaction and develop improved designs for rotating components. In addition, data from this instrument can be used with Sentinel Motion algorithms that detect and predict faults based on external acoustic signatures, for prognostic purposes. The result is an improvement in safety, performance, and maintenance costs for future generations of rotating components

Sensors can be packaged for different applications, such as inside pinion and planetary gears, or other types of enclosures for sensing non-rotational vibration.
Integration
Low-cost development kits (shown on next page) are available to assist in rapidly deploying RotoSense as a monitoring device in mechanical systems.
Ridgetop application engineers are also available to help with integration.

Applications:

  • Sensing tool wear, chatter, or spindle balance in CNC
  • Real-time monitoring of downhole drill vibration in oil and gas exploration
  • Train wheel abnormal vibration detection
  • Detect Vibrational Signatures in rotational shafts for usein early warning faults
  • Inclination and vibrational testing

Features & Benefits

  • Condition-based maintenance (CBM) of helicopter drive trains
  • High sensitivity
  • Supports hundreds of nodes in a sensor network
  • Compatibility with prognostics and health management technology
  • IoT (Internet of Things)-compatible wireless technology
  • Fast data download
  • Increases system reliability
  • Triaxial measurements
  • Anomaly detection

Specifications

PARAMETER SPECIFICATION
On-board accelerometers  3 discrete MEMS accelerometers, 2 tangential and 1 radial
RPM maximum limit 6,000 
Accelerometer range ±70 g or ±250 g or ±500 g 
Measurement sensitivity 16 mV/g or 4.4 mV/g or 2.2 mV/g
Temperature sensor range 0 to 70 °C
Anti-aliasing filter bandwidth 6,000 Hz 
Analog-to-digital(A/D)converter Three successive approximation, 16-bit resolution ADCs
Synch sampling Single-node support 
Sample rate 43,680 Hz or 0.001 Hz to 1 KHz (programmable) 
Data storage capacity 2 megabits 
Data logging mode 2 megabits (43,680 samples) 
RF data packet standard IEEE 802.15.4 open communication architecture
RF data downloading 1 minute to download full memory buffer mode/streaming mode
*Battery 3.6 V Tadiran battery
*Power consumption 0.1 W 
*Operating temperature 70 °C
*Maximum acceleration limit ±500 g
*Enclosure Plastic
*Dimensions H = 0.79 in. (20 mm), L = 2.6 in. (66.3 mm), W = 2 in. (50 mm) 
*Weight 1.6 oz. (45.359 grams) 
Software Monitoring interface Windows 7, 32- or 64-bit compatible
Gateway communication protocol USB and Ethernet 

 

Software

Adaptive Remaining Useful Life Reasoner for Prognostics and Health Management (PHM) Applications
ARULE Product Brief
Ridgetop’s Adaptive Remaining Useful Life Estimator (ARULE) is a powerful reasoner to determine the remaining useful life (RUL) and state of health (SoH) of complex systems. Working from acquired sensor data, ARULE employs an advanced prediction method related to extended Kalman filtering (EKF) to produce new RUL and SoH estimates for each new sensor data point.
ARULE is versatile and can be used for determining electronic and mechanical fatigue damage. The reasoner calculates fault-to-failure progression (FFP) signatures, accurate RUL (time-to-failure) estimates, and SoH estimates, which provide an early warning indicator for system maintenance personnel to schedule service to the system prior to catastrophic failure.
ARULE relies on diagnostic sensor data and a predefined model to produce an RUL estimate. It requires a sensor to "sense" data that are above a predefined "good-as-new" floor and below a "failed" ceiling. A new RUL estimate is produced based on changes to the model space; additionally, the new RUL estimate is used to produce a new SoH estimate.

Applications

  • Power systems
  • Battery management systems
  • Actuator control systems
  • Cable connection integrity