Transcript School of Computing and Engineering Diagnostic Engineering Research Group Diagnosis and Prognosis of Machinery Health based on Advanced Intelligent Computations Shukri Ali Abdusslam, 1st Year.

School of
Computing and Engineering
Diagnostic Engineering Research Group
Diagnosis and Prognosis of Machinery Health
based on Advanced Intelligent Computations
Shukri Ali Abdusslam, 1st Year PhD supervised by Prof. A. Ball and Dr. F.Gu
The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Abstract
The data from machinery health monitoring contains high noise components and low information content. The research is
concentrated on developing more advanced methods to analysis the data for more accurate diagnosis and prognosis of machinery
health. Rolling bearings are the most common components used in different machines and the data from them are representative in
terms of wide frequency bands, short impulses and random noise components. The method development is based on bearing
systems at the beginning. Vibration signal is employed as the data sources for the analysis. Advanced intelligent computations
which include non-linear time-series, various evolutionary algorithms, adaptive pattern algorithms, various neural networks and their
ensembles, non-linear system based data conditioning will be applied to the data and their diagnosis performance will be
investigated based on different degrees and types of faults from bearings. This research will produce a set of tools for accurate
diagnosis of machines based on the advanced the intelligent methods.
Test Facilities
Bearing Test Rig &
Vibration Measurement
Envelope of filtered signal
0.05
Amplitude
0.04
0.03
0.02
0.01
0
Contact Angle
BD
Ball Dia. (BD)
0
0.01
-3
0.02
0.03
Time(s)
0.05
0.06
Carpet level= 0.0000
0.0002
Peak value= 0.0009
0.0016
at Freqency= 79.27 79.27
1.5
PD
0.04
Envelope Spectrum
x 10
Amplitude
Aim
To develop advanced approaches
to the diagnosis and prognosis
based on advanced intelligent
computations which includes
nonlinear time-series, various
evolutionary algorithms, adaptive
pattern algorithms, various neural
networks and their ensembles,
non-linear system based data
conditioning, etc.
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0.5
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0
200
400
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Frequency(Hz)
1000
1200
Pitch Dia. (PD)
Envelope of filtered signal
0.015
0.01
Amplitude
Vibration Based Diagnosis
An undamaged bearing under load is subjected to complex forces and
moments. These include static forces such as shaft loads and preloads,
dynamic forces due to centrifugal loads, fluid pressure, traction and
friction. For a good bearing operating at a constant shaft speed and
load, all forces are in quasi-equilibrium.
0.005
0
A primary mode of bearing failure is due to localized fatigue spalling of bearing
elements: outer race, inner race, rolling elements and carriage. When such a
0.005
-4
0.01
0.015
Time(s)
0.02
0.025
0.03
Envelope Spectrum
x 10
Carpet level= 0.0000
0.0000
Peak value= 0.0001
0.0003
at Freqency= 31.71 63.42
2.5
2
Amplitude
defect exists, a rapid localised change in the elastic deformation of the
elements takes place and a corresponding transient force imbalance
occurs. The transient forces will then cause high impact vibration on the
bearing components and bearing housing.
Therefore, bearing faults can be detected by vibration measurement
and analysis. More advanced data processing methods are required to
achieve the detection and diagnosis of faults as early a stage as
possible.
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Frequency(Hz)
1000
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5. Interim Conclusion and future work
Analysis in the time domain, frequency domain and spectrum domain show good detection and diagnosis results. However, the amplitude of the features is not
sufficiently high for reliable diagnosis. Joint time-frequency analysis will be used to enhance the detection and diagnosis features.
Advanced data analysis approaches will be reviewed, In addition, condition monitoring data for evaluating the processing methods reviewed will be collected.
Furthermore, non-linear time-series will be investigated.