Advancing the Online Monitoring of Variable Speed Machinery

Jordan McBain, P.Eng.mcbainjj@gmail.com

Sudbury, Ontario

Introduction

• Monitoring of machinery largely limited to constant conditions

• Changes in speed and load termed ‘nuisance parameters’

• Variable speed/load machinery ubiquitous

• Resonances/vibration power

Ref: Stack

Novelty Detection

• Limited data characterizing normal state– Little or no data for

abnormal states• Compute feature vectors

of vibration (e.g. AR model)

• Methods– SVDD and Statistical

Boundaries

Statistical Parameterization

• Vibration strongly tied to temp (speed)• Advanced by Keith Worden (Structural health monitoring)

– Segment feature vectors into small groups of modal value – Compute statistics for each group (bin)– Trend with regression or interpolation

• Suffers from– Double curse of dimensionality

• Describe healthy state for all segments of modal parameter

– Gaussian distribution• Good heuristic

Multi-Modal Novelty Detection

• Employ intuition from Statistical Parameterization– Don’t flatten data into bins– Add modal parameter (speed) to feature vector– Use any novelty detection technique– One parameter only

• Gaussian Distribution – eliminated

• Curse of dimensionality– Dependent on underlying

novelty detection technique

Experimental Methodology

Experimental Methodology• Sensors

– 2500 ppr Tach– 4 accel (10 kHz)– AE– Hall effect sensors– Inline torque meter

• Variable Speed/Fixed Load (10 Nm)• DAQ and Control

– NI FPGA and Accel Card

• Vibration data– Segmentation: 30 shaft rotations, 70% overlap, Gaussian window – Feature vectors: Auto-Regressive (AR) Models and Statistics– Training: 20% of data for training, 80% for validation

• Faults– Gears (96:32 and 80:48): missing tooth, root crack, chipped pinion– Bearings: rough ball, outer race, inner race, chopped ball

Classification Results

• No speed adaptation (SVDD)

Classification Results

• Statistical Parameterization

Classification Results

Conclusions

• No speed adaptation = poor results• Statistical Parameterization– Good results– Double Curse of Dimensionality– Gaussian Distribution

• Multi-Modal Novelty Detection– Comparable Results– More to come

Future Work

• Novelty Detection Augmented for Fault Detection with Variable Speed Machinery (MSSP)

• Multi-Modal Novelty Detection for Variable Load and Speed Machinery

• Other multi-modal novelty detection techniques– No modal sensors

References

• [1] J McBain, M Timusk. Fault detection in variable speed machinery: Statistical parameterization, Journal of Sound and Vibration 327 (2009) 623-646.

• [2] K Worden, H Sohn, CR Farrar. Novelty detection in a changing environment: Regression and interpolation approaches, J.Sound Vibrat. 258 (2002) 741-761.

• [3] JR Stack, TG Habetler, RG Harley. Effects of machine speed on the development and detection of rolling element bearing faults, IEEE Power Electronics Letters. 1 (2003) 19-21.