Vol. 9; No. 1; 2006
Condition Monitoring in UK Defence – The Challenges Ahead
F. Hawkins
Abstract: Developments in the miniaturization of electronics, processing
power and improved distributed communications are creating new possibilities
for the health monitoring of military equipment. It is perceived that condition
monitoring technology can support the reform of logistic processes, deliver
savings and improve equipment availability. Targeting the technology intelligently
is a demanding process though; there are many issues to consider. This paper
places condition monitoring into perspective within wider reliability issues,
discusses some potential problem areas and some of the additional benefits
which successful exploitation of the technology can bring.
Keywords: Condition monitoring; HUMS; Defence; Mission reliability. Pages:
2 – 8. Refs: 9.
A Correlation between Acoustic Emission and Asperity Contact of Spur Gears
under Partial Elastohydrodynamic Lubrication
C. Keong & D. Mba
Abstract: The application of the Acoustic Emission (AE) technique to condition
monitoring of gears is still at a developmental stage. Understanding the
source of AE activity at the gear mesh is fundamental if this technique
is to be successfully employed for gear diagnostics and prognostics. This
paper presents results of an experimental programme that showed a correlation
between AE activity and asperity contact under partial elastohydrodynamic
lubrication. Based on the observations, it is postulated that AE can monitor
the effectiveness of a lubricant in minimising asperity contact.
Keywords: Acoustic emission; Elastohydrodynamic lubrication; Asperity contact;
Gearbox diagnosis. Pages: 9 – 14. Refs: 11.
Designing a Human Machine User Interface for a Condition Based Monitoring
System
P.A. Higgs, R. Parkin, M. Jackson & F. Zorriassatine
Abstract: The man-machine interface or method of human computer interaction
(HCI) represents the most crucial element of a computer system, as its design
determines eventual user acceptance and utilisation. Graphic User Interfaces
(GUIs) are the most popular form of HCI presently used today. In this paper,
practitioner desirable Condition Based Monitoring (CBM) GUI design are discussed.
Keywords: Condition based monitoring; Maintenance; Graphic user interface
design. Pages: 15 – 22. Refs: 15.
Artificial Neural Network Prediction and Quantification of Damage in Impeller
Shaft using Finite Element Simulation
S. Vijayakumar & S. Muthukumar
Abstract: This paper presents the application of ANNs for damage detection
and quantification in impeller shafts, which are used in radial fans at
power generation plants. The finite element model is simulated using three
dimensional shell elements available in ANSYS. During simulation, the symmetry
and other dynamic loads from rotating parts have been taken into account.
The ‘taking advantage symmetry’ approach and a wave front method
for static and modal analysis considerably reduce the computational time.
Using undamped free vibration response, the frequency response functions
(FRF) are extracted. For various presumed damages through element stiffness
reduction. A multiplayer back propagation ANN, with two hidden layers, is
designed using Matlab.
Keywords: Damage detection; Impeller shaft; Finite element modelling; Frequency
response functions. Pages: 23 – 29. Refs: 18.
Detection of Induction Motor Stator Winding and Unbalance Faults using
Hybrid Methods
W. Li & C.K. Mechefske
Abstract: This paper presents the results of a study aimed at the detection
of three phase induction motor stator winding and unbalance faults using
experimental techniques. Unlike broken bar and rolling element bearing faults,
a stator winding fault does not have well-defined fault frequencies in the
vibration, acoustic or stator winding current spectra. On the other hand,
an unbalance fault may yield vibration spectral harmonics that are similar
to other faults. For these reasons it is sometimes difficult to detect and
diagnose these faults by using only one monitoring scheme. This paper employs
a combination of vibration, stator current and acoustic methods. Simulated
motor fault experiments were conducted in a laboratory environment under
different speed and load conditions. Experimental results suggest that particular
spectral harmonics from measurements are sensitive to different motor faults.
Hybrid methods are highly recommended to obtain accurate monitoring results.
Keywords: Induction motors; Stator winding; Unbalance; Fauts; Hybrid methods.
Pages: 30 –36. Refs: 12.
Vol. 9; No. 2; 2006
A Requirements Management Approach Supporting Integrated Health Management
Systems Design
P. Soderholm
Abstract: System health management is an approach that is intended to improve
the dependability and safety of technical systems, and to decrease the combined
cost of operation and support. In order to achieve the potential benefits
of Health management, it is necessary to combine stakeholder requirements
with a thorough engineering knowledge. The aim of this paper is to present
a systemic, systematic, and stakeholder-centred Requirements Management
approach that supports the design of Integrated Health Management System
(IHMS). A holistic management model intended to increase stakeholder satisfaction
with a reduced amount of resources in also presented. This approach is applied
to a case-study related to a modern combat aircraft, which is a highly complex
and critical technical system
Keywords: Requirements management; Integrated health management system;
Case-study. Pages: 2 – 13. Ref: 33.
The Extended Kalman Filter as a Tool for Condition Monitoring in Hydraulic
Systems
Y. Chinniah
Abstract: Usually, parameters can be related to the health of the system
and as such, deviations from the normal values of the parameters can be
linked to developing faults. When the parameters cannot be measured directly
using sensors, they need to be estimated. The Extended Kalman Filter (EKF)
is a state and parameter estimation algorithm, which has recently been used
for early fault detection in fluid power systems. In this paper, the EKF
algorithm is described, and to illustrate the technique, it is applied to
a simulated model of a simple mass spring damper system and then to a simulated
model of a high performance hydrostatic system, the electrohydraulic actuator
(EHA). The EKF is used to estimate important parameters such as the spring
constant and the viscous damping coefficient in the mass spring damper system
and the effective bulk modulus in the hydrostatic system. Using simulation
studies, the ability for the filter to detect and estimate changes in the
parameters is investigated. The EKF is a viable tool for early fault detection
in hydraulic systems.
Keywords: Condition monitoring; Hydraulic systems; Extended Kalman filters.
Pages: 14 – 22. Refs: 21.
Condition Monitoring and Fault Diagnosis of Engineering and Manufacturing
Systems. Part I: Vibro-Acoustics Monitoring
C.K. Mukhopadhyay, T. Jayakumar, Baldev Raj & B.K.N. Rao
Abstract: Machinery vibration and noise play a major part in industrial
plants and machinery. There are many reported instances where the performance,
reliability, safety and health of both physical and human assets have significantly
deteriorated, thereby shortening not only the useful life-cycle but also
the availability, maintainability and productivity of these valuable assets.
Intelligent sensing, monitoring and diagnosing the root cause failure of
these industrial assets is now urgent than ever before. In this state-of-the-art
review, the authors have attempted to highlight some of the current trends
and progress in the world of vibration and acoustic emission technology
for condition monitoring applications.
Keywords: Condition monitoring: Failure diagnosis; Noise and Vibration;
Sensor technology; Acoustic emission. Pages: 23 – 40. Refs: 88.
Vol. 9; No. 3; 2006
Intelligent Systems
S.J. Wilcox
Abstract: In this paper, the nature of intelligence is explored briefly
and artificial intelligence in introduced. Three techniques that can allow
a computer to achieve limited intelligence are discussed and some examples
are presented.
Keywords: Intelligent systems; Expert systems; Fuzzy systems; ANNs; Genetic
Algorithms. Pages: 2 – 6. Refs: 6.
Optimisation of Heat Treatment Parameters using Artificial Intelligence
Techniques
Z. S. Chong, S.J. Wilcox and J. Ward
Abstract: This paper describes the work undertaken to apply ANNs to model
the cold alloy-steel bars and the heat treatment parameters with their end-product
quality characteristics. Standard multi-layered feed forward ANNs were employed
to represent the functional mapping of inputs such as physical dimension,
material composition and the parameters of the heat treatment cycles to
the Brinell Hardness (HB)and Ultimate Tensile Strength (UTS). The ANNs were
then integrated into a Genetic Algorithm (GA) search strategy to identify
the best material characteristics and furnace operating parameters in order
that both HB and UTS are maximised. The results demonstrated that such a
hybrid strategy can deliver sensible results
Keywords: Heat treatment; Cold-alloy steel bars; ANNs; GA; Hybrid strategy.
Pages: 7 – 14. Refs: 18.
Induction Motor Fault Detection and Diagnosis using Artificial Neural Networks
L. Li and C.K. Mechefske
Abstract: This paper investigates induction motor fault detection and diagnosis
using ANN. These techniques include feed-forward back-propagation networks
(FFBPN) and self organizing maps (SOM), used individually and in combination.
Common motor faults such as bearing faults, stator winding fault, unbalanced
rotor and broken bars are considered. The ANNs were trained and tested using
dynamic measurements of stator currents and mechanical vibration signals.
The effects of different network structures and the training set sizes on
the performance of the ANNs are discussed. In addition, incipient motor
fault detection has been investigated.
Keywords: Induction motors; Fault detection and diagnosis; Feed-forward
networks; Self-organizing maps networks. Pages: 15 – 23. Refs: 17.
How to Diagnose the Wear of Rolling Element Bearings based on Indirect
Condition Monitoring Methods
A. Jantunen
Abstract: Detection of wear of the components in rotating machinery is usually
based on indirect methods. The prediction of how much time there is left
to maintain these components before they break down is not easy. This paper
attempts to tackle this issue related to rotating machinery and in particular
to rolling element bearings. The developed approach starts from the idea
of modelling the wear of the component. A limited number of condition monitoring
parameters are used for diagnosis of the fault. These parameters are used
as input in the form of higher order polynomial regression functions with
a limited number of terms. The regression functions can give prognosis of
the development of the fault. The severity of the situation is analysed
using simplified fuzzy logic.
Keywords: Wear; Rolling element bearings; Indirect condition monitoring;
Higher polynomial regression. Pages: 24 – 38. Refs: 40.
Constrained Optimisation of Pulverised Coal Fired Boilers using Genetic
Algorithms and Artificial Neural Networks
C.K. Tan, S. Kakietek, S.J. Wilcox and J. Ward
Abstract: This paper examines an approach to optimise the combustion air
distribution to a 200MW pulverised coal fired boilers using ANNs and Genetic
Algorithm (GA). The nitrogen oxide (Nox) and carbon monoxide (CO) emissions
as well as the carbon burnout characteristics of the boiler were investigated
through a series of field experiments. On the basis of experimental results,
three ANNs were used to model the NOx and CO emissions and the unburned
carbon (UBC) in the fly ash of the boiler respectively. Once trained, the
ANNs were coupled with a GA to determine the optimum solution of the ANN
models. Minimum human efforts and little insight into the details of the
combustion mechanisms within the boiler are required to generate the optimal
settings of the combustion air distribution. The combinations of ANNs and
a GA represents a technically plausible approach to determine the boiler
air settings for improved combustion behaviour.
Keywords: Genetic algorithm; ANNs; Optimisation; Boiler; Coal combustion.
Pages: 39 – 46. Refs: 9.