scispace - formally typeset
Search or ask a question

Showing papers on "Condition monitoring published in 1997"


Journal ArticleDOI
TL;DR: In this paper, the condition of a model drive-line consisting of various interconnected rotating parts, including an actual vehicle gearbox, two bearing housings, and an electric motor, all connected via flexible couplings and loaded by a disc brake, was investigated.

333 citations


Journal ArticleDOI
TL;DR: The concept of ensemble diversity is considered in some detail, and a hierarchy of four levels of diversity is presented, which is used in the description of the application of ensemble-based techniques to the case study of fault diagnosis of a diesel engine.
Abstract: An appropriate use of neural computing techniques is to apply them to problems such as condition monitoring, fault diagnosis, control and sensing, where conventional solutions can be hard to obtain. However, when neural computing techniques are used, it is important that they are employed so as to maximise their performance, and improve their reliability. Their performance is typically assessed in terms of their ability to generalise to a previously unseen test set, although unless the training set is very carefully chosen, 100p accuracy is rarely achieved. Improved performance can result when sets of neural nets are combined in ensembles and ensembles can be viewed as an example of the reliability through redundancy approach that is recommended for conventional software and hardware in safety-critical or safety-related applications. Although there has been recent interest in the use of neural net ensembles, such techniques have yet to be applied to the tasks of condition monitoring and fault diagnosis. In this paper, we focus on the benefits of techniques which promote diversity amongst the members of an ensemble, such that there is a minimum number of coincident failures. The concept of ensemble diversity is considered in some detail, and a hierarchy of four levels of diversity is presented. This hierarchy is then used in the description of the application of ensemble-based techniques to the case study of fault diagnosis of a diesel engine.

170 citations


Journal ArticleDOI
TL;DR: This is the first time the condition monitoring decision process has been modelled for real plant based upon filtering theory and the model fits the data well, gives a sensible answer to the actual problem, and is transferable to other condition monitoring contexts.

141 citations


Proceedings ArticleDOI
05 Oct 1997
TL;DR: In this paper, the authors proposed a method for sensorless on-line vibration monitoring of induction machines based on the relationship between the current harmonics in the machine and their related vibration harmonics.
Abstract: This paper proposes a method for sensorless on-line vibration monitoring of induction machines based on the relationship between the current harmonics in the machine and their related vibration harmonics. Initially, the vibration monitoring system records two baseline measurements of current and vibration with the machine operating under normal conditions. The baseline data is then evaluated to determine the critical frequencies to monitor on-line. Once these frequencies are determined, the baseline vibration measurement is simply used to scale the current harmonic signal to an estimated vibration level. Based on theoretical analysis, simulation results, and the experimental results shown here, a linear relationship between the current harmonics and vibration level can be assumed. The results of two experiments on a three-phase 230 V, 10 HP induction motor operating under no load are discussed and show the feasibility of this method for sensorless on-line vibration monitoring.

104 citations


Journal ArticleDOI
TL;DR: By integrating several AI technologies-including qualitative model-based reasoning-the Tiger system significantly cuts costs and improves performance by using control-system information to perform condition monitoring for gas-turbine engines.
Abstract: Gas turbines are critical to the operation of most industrial plants, and their associated maintenance costs can be extremely high. To reduce those costs and increase the availability of their gas turbines, plant operators have for many years relied on routine preventative maintenance-routinely checking and solving small problems before they grow into major ones. Recently, however, the power industry has moved sharply toward condition-based maintenance and monitoring. In this approach, intelligent computerized systems monitor gas turbines to establish maintenance needs based on the turbine's condition rather than on a fixed number of operating hours. By integrating several AI technologies-including qualitative model-based reasoning-the Tiger system significantly cuts costs and improves performance by using control-system information to perform condition monitoring for gas-turbine engines.

100 citations


Journal ArticleDOI
TL;DR: In this paper, the authors present recent developments in providing tools for the diagnosis of faults or incipient faults in electric motor drives, including: sensorless torque measurement; direct detection of turn-to-turn short circuits; detection of cracked or broken rotor bars; and detection of bearing deterioration.
Abstract: Early detection of abnormalities in electric motors helps to avoid expensive failures. Motor current signature analysis (MCSA) implemented in a computer-based motor monitor can contribute to such condition-based maintenance functions. Such a system may also detect an abnormality in the process as well as the motor. Extensive online monitoring of the motors can lead to greater plant availability, extended plant life, higher quality product, and smoother plant operation. With advances in digital technology over the last several years, adequate data processing capability is now available on cost-effective, microprocessor-based, protective-relay platforms to monitor motors for a variety of abnormalities in addition to the normal protection functions. Such multifunction monitors, are displacing the multiplicity of electromechanical devices commonly applied for many years. Following some background information on motor monitoring, this article features recent developments in providing tools for the diagnosis of faults or incipient faults in electric motor drives, including: sensorless torque measurement; direct detection of turn-to-turn short circuits; detection of cracked or broken rotor bars; and detection of bearing deterioration.

90 citations


Journal ArticleDOI
01 Jun 1997
TL;DR: The use of neural networks are described as a method for automatically classifying the machine condition from the vibration time series, thus allowing maintenance before further damage occurs.
Abstract: Vibration analysis of rotating machinery can give an indication of possible faults, thus allowing maintenance before further damage occurs. Automating this analysis allows machinery to be run uattended for longer periods of time. This paper describes the use of neural networks as a method for automatically classifying the machine condition from the vibration time series. Several methods for the extraction of features to use as neural network inputs are described and compared. These methods are based upon measuring the zero lag higher-order statistics of the measured vibration time series. The time series for horizontal and vertical vibration signals are considered separately and combined to produce time series based upon the radius of the vibration displacement. The experimental set-up used for simulating unbalance and rub faults is described and classification success rates based upon each method reported. In particular, a classification success rate of over 99 per cent has been achieved.

85 citations


Journal ArticleDOI
TL;DR: In this paper, the effectiveness and reliability of different vibration analysis techniques for fault detection and diagnostics in cam mechanisms used in high performance automatic packaging machines is assessed and compared, and the results of the application of widely used techniques are given and their limitations are delineated.

73 citations


Journal ArticleDOI
TL;DR: In this paper, the authors assess the current condition of a structure using visual inspection, X-ray inspection, or detection of acoustic emissions, and combine the data obtained from the condition assessment with these models to estimate the remaining service life of the structure using Bayes' theorem.
Abstract: The need for reassessment of the fatigue life of existing structures is increasing as the world’s infrastructure ages. A fatigue life reassessment typically begins with an assessment of the current condition of the structure. The condition assessment techniques range from visual inspection to X-ray inspection or detection of acoustic emissions. The fatigue reliability of the structure can be estimated from probabilistic fatigue life or fracture mechanics models. The data obtained from the condition assessment can be combined with these models to estimate the remaining service life of a structure using Bayes’ theorem. Simulation techniques are often used to facilitate these calculations. If the remaining service life is inadequate, it may be desirable to repair the structure; however, repairs must be performed carefully to provide the desired benefit. On the other hand, economic factors may dictate a course of action other than repair, such as replacing the structure or changing the operation of the structure.

71 citations


Patent
07 Nov 1997
TL;DR: In this paper, a real-time electric motor diagnostics and condition monitoring system is presented, which includes a set of sensors, a processing unit, a memory and an output interface for communicating alarms, warnings and calculated operating parameter values or the like to a display device and to an external supervisor having wireless paging capability to alert a remote operator or maintenance personnel.
Abstract: A method and apparatus for real-time electric motor diagnostics and condition monitoring. While the motor is energized, dynamic operating parameters are determined and a notification signed is generated if predetermined criterion are satisfied. The diagnostic apparatus is integrated with the motor and includes a set of sensors, a processing unit, a memory and an output interface for communicating alarms, warnings and calculated operating parameter values or the like to a display device and to an external supervisor having wireless paging capability to alert a remote operator or maintenance personnel. In a normal operating mode, the processing unit calculates a general class of derived motor operating parameters such as over-temperature, over-voltage, over-current, excessive vibration, and phase imbalance. When an abnormal condition is observed, the processing unit modifies certain data acquisition parameters as necessary to effect an alternative data acquisition strategy which would more likely lead to data dispositive of the condition of the motor.

69 citations


Patent
Shintaro Yokoyama1, Kouichi Kojima1
20 May 1997
TL;DR: In this paper, a driving condition-monitoring apparatus for an automotive vehicle monitors the driving condition of a driver of the automotive vehicle and determines whether the driver's driving condition is abnormal or not based on the data generated by the monitoring system.
Abstract: A driving condition-monitoring apparatus for an automotive vehicle monitors a driving condition of a driver of the automotive vehicle. At least one of behavior of the vehicle, a driving operation of the driver, and at least one condition of the driver is detected to thereby generate driving condition-indicative data indicative of the driving condition of the driver. It is determined whether the driving condition of the driver is abnormal, based on the driving condition-indicative data generated. When it is not determined that the driving condition of the driver is abnormal, a degree of normality of the driving condition of the driver is determined by inputting a plurality of pieces of the driving condition-indicative data to a neural network. At least one of warning and control of the vehicle is carried out depending on a result of the determination as to whether the driving condition of the driver is abnormal and the degree of normality of the driving condition of the driver.

Journal ArticleDOI
TL;DR: In this paper, a non-linear principal component analysis (NPCA) is proposed to reduce the dimensionality of the process by creating a new set of variables, principal components, which attempt to reflect the true underlying system dimension.

Journal ArticleDOI
18 May 1997
TL;DR: In this paper, the authors describe how commercial finite element packages may be used to simulate rotor faults and hence enhance the capability of practical condition monitoring schemes and develop accurate models of the machine under faulted conditions.
Abstract: This paper describes how commercial finite element packages may be used to simulate rotor faults and hence enhance the capability of practical condition monitoring schemes. Accurate models of the machine under faulted conditions are developed using both fixed mesh and time-stepping finite element packages. Some known causes of inaccuracy between models and experimental data are accounted for and the results of the simulation are compared with those obtained from a laboratory test rig. These models form the basis of a method to investigate fault types which present condition monitoring schemes cannot detect.

Journal ArticleDOI
TL;DR: The efficiency and advantages of the intelligent diagnostic procedure in precisely monitoring and quantifying the fault development are systematically brought out considering this bearing system.
Abstract: The objective of this paper is the development of an efficient intelligent diagnostic procedure that considers several diagnostic indices for the quantification of developing faults and for monitoring machine condition. In this procedure, the condition monitoring is performed based on the on-line vibration measurements, and further, the fault quantification is formulated into a multivariate trend analysis. Self-organizing neural networks are then deployed to perform the multivariable trending of the fault development. The attributes for the disordering of “knots” in the trend analysis are determined. The disordering of neural network units is then eliminated by suitably altering the self-organizing neural network algorithm. Applications of this diagnostic procedure to the condition monitoring and life estimation of a bearing system are fully developed and demonstrated. The efficiency and advantages of the intelligent diagnostic procedure in precisely monitoring and quantifying the fault development are systematically brought out considering this bearing system.

Journal ArticleDOI
TL;DR: In this paper, it is shown that, by modeling backlash as a microscopic impact, its presence can be detected and possibly measured using only simple sensors, and the results can be quantified for a test-bed mechanism.

Proceedings ArticleDOI
01 Sep 1997
TL;DR: In this paper, a review of condition monitoring methods both as a diagnostic tool and as a technique for failure identification in high voltage induction motors in the petrochemical industry is presented.
Abstract: The study gives a synopsis over condition monitoring methods both as a diagnostic tool and as a technique for failure identification in high voltage induction motors in the petrochemical industry. New running experience data for 356 units are registered and processed statistically to reveal the connection between motor data, protection and condition monitoring methods, maintenance philosophy and different types of faults. The different types of faults are further analyzed to failure-initiators, contributors and underlying causes. The results have been compared with those of a previous survey by these authors, and one by an IEEE Report. Methods of fault detection are reviewed and analyzed: mechanical (vibrations, shock pulses, acoustic, speed fluctuations), electro mechanical (currents, surges, partial discharges, leakage fluxes) and temperature-, oil particle-, gas, analysis, and performance- and visual methods. Different types of fault generate different frequency components in vibration spectra, in motor supply current, in torque and in magnetic field, and relationships between fault types and spectra have been studied.

Journal ArticleDOI
TL;DR: In this article, a method for removing the load effects from the monitored quantity of the machine is presented by comparing the actual stator current to a model reference value which includes the load effect Simulation results illustrating the effects on the spectrum of monitored quantity are given for both constant and eccentric air gaps when in the presence of an oscillating load.
Abstract: Fault conditions in induction machines such as eccentric air gaps and broken rotor bars result in harmonics in the stator current of the motor which can be used to monitor the health of the machine. However, variations in the load torque at multiples of rotational speed typically have the same effect on the motor current spectrum. When monitoring a single phase of the stator current, this load effect can prevent the detection of a fault condition by producing current components that obscure those harmonics related to the condition of the machine. This paper presents a new method for removing the load effects from the monitored quantity of the machine. This is accomplished by comparing the actual stator current to a model reference value which includes the load effect Simulation results illustrating the effects on the spectrum of the monitored quantity are given for both constant and eccentric air gaps when in the presence of an oscillating load.

01 Jan 1997
TL;DR: In this paper, the authors present a maintenance policy for modern machinery that must run at high availability and effectiveness which cannot be achieved without an effective maintenance policy, which is difficult and expensive to implement.
Abstract: Modern machinery is expensive and therefore must run at high availability and effectiveness which cannot be achieved without an effective maintenance policy. Condition monitoring, (CM), techniques ...

Journal ArticleDOI
TL;DR: Vibration data from an induction machine is employed to investigate higher order properties associated with electrical machine faults, resulting in improved ANN diagnoses of faults.
Abstract: Vibration data from an induction machine is employed to investigate higher order properties associated with electrical machine faults. Three fault conditions are investigated together with all possible permutations. By considering combinations of faults, interesting higher order properties are identified and presented, ultimately resulting in improved ANN diagnoses of faults.

Journal ArticleDOI
Ian Howard1
01 Apr 1997
TL;DR: In this paper, the authors present two higher-order spectral analysis techniques, the bispectrum and trispectrum, and demonstrate how they can be used to detect phase coherence between various frequency components.
Abstract: The vibration signals measured from rotating machinery can be very complex and a number of machine malfunctions can create complicated modulation patterns which are sometimes difficult to detect and to understand. Conventional linear spectral analysis will be of limited use in particular instances when frequency components interact together to form new spectral components due to some non-linear process. Under these circumstances, various signal processing tools are available for performing sophisticated analysis of the measured vibration to detect the non-linear interaction of frequency components and hence changes in machine performance and condition. This paper presents two higher-order spectral analysis techniques, the bispectrum and the trispectrum, and demonstrates how they can be used to detect phase coherence between various frequency components. The theoretical relationship of the higher-order spectral techniques to the power spectrum is given along with the derivation of the normalized bispectrum...

Dissertation
01 Jan 1997
TL;DR: In this article, a new maintenance approach was developed to overcome some of the limitations of total productive maintenance, TPM, and reliability-centered maintenance, RCM, and is based upon the Deming managerial feedback cycle (Plan-Do-Check-Act).
Abstract: Modern machinery is expensive and therefore must run at high availability and effectiveness which cannot be achieved without an effective maintenance policy. Condition monitoring, (CM), techniques can be utilised to reduce or arrest the rate of deterioration of a component so increasing operating life. The main result reported in this thesis is that the rolling element bearing’s mean effective life could be extended appreciably if an existing vibration-based maintenance policy is used effectively. This result is achieved by a combination of data analysis and logical development. A new maintenance approach was developed to overcome some of the limitations of total productive maintenance, TPM, and reliability-centered maintenance, RCM, and is based upon the Deming managerial feedback cycle (Plan-Do-Check-Act). This method is called Total Quality Maintenance, (TQMain). It is a methodology to sustain and improve continuously the technical and economic effectiveness of the manufacturing process elements. It is shown logically that by using vibration-based monitoring, (VBM), program in the frame of a common database for a plant IT-system the causes behind quality deviations and failures can be identified and eliminated effectively at an early stage and the company’s economics would be improved. The condition-based maintenance effectiveness and accuracy are usually related to the ability of the CM system to detect failure causes and follow defect development. Criteria to select the most cost-effective VBM system and the most cost-effective vibration-based maintenance policy are developed. But, in most real cases, CM systems are not utilised effectively and companies are satisfied with the partial savings achieved in maintenance cost. It is shown, by two case studies, that improvements in vibration-based maintenance can be achieved by effective feedback of the results of failures, renewal condition and VBM history analysis. Unfortunately the data coverage and quality in these studies were not sufficient, among other reasons due to the low number of failures and long bearing lives, so the conclusions, although supported by the work results, remain strong qualitative indications, rather than statistical proof. Criteria to measure the effect of improvements to confirm whether it is economically beneficial and to identify the basic reasons why it is not, are developed. The contribution of this thesis is: The development of a sequential method for the selection and improvement of a cost-effective vibration-based maintenance policy. This methodology can be used to justify, on economic criteria, the use of VBM systems to indicate when rolling element bearings should be renewed. It is achieved through; Tools used in quality and maintenance technology and reliability analysis have either been modified or developed beyond their original concept such as; Maintenance cost equation, Total Time On Test-plots. Development of new tools to monitor maintenance effectiveness and accuracy, select the most informative CM parameter(s) and cost-effective vibration-based maintenance policy. A new maintenance approach, (TQMain), and a new envelope alarming method for VBM programs are also developed. Theoretical basis for improvements to the effectiveness of vibration-based maintenance of bearings in paper mill machines and two case studies which tend to confirm the theory.

Journal Article
TL;DR: In this paper, acoustic emission testing (AE) was employed to monitor sound indications emanating from a specially designed and built gear box and various types of failures were purposely induced, simulating the possible wear and tear conditions that a gearbox may undergo during its useful life.
Abstract: Acoustic emission testing (AE) was employed in this research to monitor sound indications emanating from a specially designed and built gear box. Various types of failures were purposely induced, simulating the possible wear and tear conditions that a gearbox may undergo during its useful life. Results obtained at different speeds were plotted against causes of failure. Subsequent analysis revealed the importance of correlating AE results with known failures. The technique developed for early failure diagnosis has the potential of utilizing AE as a tool for predictive maintenance.

Journal ArticleDOI
TL;DR: In this paper, a method is described which attempts to offer a solution to the problem when the engine is not able to produce its maximum power, while there is no obvious fault or error.
Abstract: Diagnosis of diesel engines is not new and various methods have been proposed in the past for fault diagnosis. The problems relating to marine diesel engines, especially medium- and high-speed engines, are due mainly to their large size, which does not allow the use of trial and error methods, and their high operating speed. The most difficult problem occurs when the engine is not able to produce its maximum power, while there is no obvious fault or error. In the present work a method is described which attempts to offer a solution to such problems. The method is a thermodynamic one based on a simulation model and the processing of measured engine data. Presented is an application of the method to a medium-speed marine diesel engine, which suffered from low power output accompanied by high exhaust gas temperatures. The results from application of the method show that the problem is not a direct one, but is caused by many factors that result in improper operation. With this method, the current engine condition can be discovered, and suggestions made for proper tuning or repair. After conducting such an analysis, a vessel was able to achieve its maximum cruising speed, showing that the proposed method is a promising one.

DOI
01 Jan 1997
TL;DR: A novel model-based methodology has been proposed that has integrated four levels of information processing in a structured hierarchy and allows for automatic generation of fault symptoms in the form of qualitative variation of system physical parameters by on-line processing of low-quality raw sensor data.
Abstract: Safety and functionality of a fluid power control system can considerably be increased by implementing predictive maintenance routines. Modern predictive maintenance practices are based on automatic condition monitoring and fault diagnosis of the system components. In most cases, low-quality raw sensor data are directly monitored for constraint violations or threshold crossings. Subsequent fault diagnosis is often performed by a knowledge-based expert system based on "order-of-magnitude reasoning". This means that quantitative sensor data are first transformed into more understandable "linguistic terminologies" such as "low", high", etc., and are then assessed by production rules in order to diagnose system (or component) faults. A major problem with this technique is that it is not usually feasible to directly measure the desired quantity, e.g., the flow rate inside a valve. Another problem is the association of noise and variations with directly measured signals, which might be misinterpreted as faults, especially in highly dynamic systems. In practice, failure modes often involve a change in the model structure, which may be interpreted as change(s) in one or several system parameters. The theme of this thesis is on automatic generation of fault symptoms in the form of qualitative variation of system physical parameters by on-line processing of low-quality raw sensor data. To accomplish this, a novel model-based methodology has been proposed that has integrated four levels of information processing in a structured hierarchy: 1. State/parameter estimation of the hydraulic system components using state-space models, stochastic signal processing techniques such as Kalman filtering, and raw sensor data from the hydraulic system. 2. Monitoring and change detection in the identified parameters of the system components, using statistical tests, such as sequential probability ratio test. 3. Generation of fault symptoms in the form of qualitative changes in the physical parameter values, such as "increased", "decreased", etc. 4. Fault recognition by fault symptom classification using neural network pattern classifiers, 5. Fault diagnosis maintenance aiding using knowledge-based expert systems. ii By using a second-order linear system as an example, we have shown how each element of the proposed hierarchical methodology effectively processes the lower quality data received from the previous element and provides higher quality information for the next element in the hierarchy, so that an incipient fault or an abrupt failure can be successfully detected and diagnosed. The proposed fault detection and diagnosis (FDD) technique has also been applied on a real hydraulic test rig which has been built in the Robotics and Control Laboratory, at UBC. The hydraulic test rig has a two-stage proportional directional flow control valve, which has been thoroughly modelled for simulation of faults. A step-by-step methodology has been adopted to obtain the physical valve parameters from static measurements, as well as through numerical search techniques using dynamic measurements. In order to estimate the system parameters and states in real-time, nonlinear state-space models have been developed for various hydraulic components, including the two-stage servovalve, a hydraulic cylinder, and a manipulator. Extended Kalman Filtering (EKF) is applied on the state-space models to get the parameter estimates. Only low-cost robust sensors such as pressure transducers and position sensors have been used for this purpose. More expensive or hard-to-measure states such as flow rates and orifice areas are predicted using novel state-space models. One of the major achievements of this thesis has been incorporation of a novel state-space model for a valve orifice area that allows us not only to obtain accurate estimates of the flow rate through the valve, but also to detect several incipient faults and abrupt failures in the valve and its connecting ports. The valve orifice area is considered as a nonlinear unknown function of the valve spool position. No a priori knowledge about the orifice profile or the spool deadband size is assumed. The functional relationship, along with the deadband size are automatically revealed during the on-line estimation process, while the decision as to which port is open to the flow is made internally. Experimental results were promising and showed that the identified valve orifice area is an excellent measure in quick detection and diagnosis of incipient or gradual faults, as well as and abrupt failures, in servovalves and servo-actuator systems.

Journal ArticleDOI
TL;DR: In this paper, the authors deal with the basic principles, which may help in identifying its diagnostic ability, the scope of its diagnostic capabilities, the instrumentation in vogue for its monitoring and the state-of-the-art of the monitoring techniques and programs.
Abstract: Vibration is an effective tool in detecting and diagnosing some of the incipient failures of machines and equipment. The present paper deals with the basic principles, which may help in identifying its diagnostic ability, the scope of its diagnostic capabilities, the instrumentation in vogue for its monitoring and the state-of-the-art of the monitoring techniques and programs. A few case studies are also given to illustrate how machine troubles/failures are diagnosed with the help of vibration signatures.

Proceedings ArticleDOI
25 May 1997
TL;DR: In this paper, transfer function measurements on four identical 132/66/11 kV 30 MVA power transformers have been presented to identify faults such as winding deformation and displacement, inter-turn and inter-disc faults.
Abstract: A Power transformer is a critical unit within a power network. A large transformer failure could cause long interruptions and costly repairs. Therefore, it is desirable to detect potential failures as early as possible. Model based diagnosis such as the transfer function method is becoming increasingly popular in transformer condition monitoring. The transfer function method is particularly useful in identifying faults such as winding deformation and displacement, inter-turn and inter-disc faults. In this paper, practical experiences in transfer function measurements on four identical 132/66/11 kV 30 MVA power transformers has been presented.

Journal ArticleDOI
TL;DR: In this paper, it is shown that the symptom models used in vibration condition monitoring, for condition recognition and prediction, can be in most cases limited to Weibull and Frechet models.

Dissertation
01 Jan 1997
TL;DR: In this article, the authors consider various aspects of the use of remote sensing, geographical information systems and Bayesian knowledge-based expert system technologies for broad-scale monitoring of land condition in the Western Australian wheat belt.
Abstract: This thesis considers various aspects of the use of remote sensing, geographical information systems and Bayesian knowledge-based expert system technologies for broad-scale monitoring of land condition in the Western Australian wheat belt.The use of remote sensing technologies for land condition monitoring in Western Australia had previously been established by other researchers, although significant limitations in the accuracy of the results remain. From a monitoring perspective, this thesis considers approaches for improving the accuracy of land condition monitoring by incorporating other data into the interpretation process.Digital elevation data provide one potentially useful source of information. The use of digital elevation data are extensively considered here. In particular, various methods for deriving variables relating to landform from digital elevation data and remotely sensed data are reviewed and new techniques derived.Given that data from a number of sources may need to be combined in order to produce accurate interpretations of land use/condition, methods for combining data are reviewed. Of the many different approaches available, a Bayesian approach is adopted.The approach adopted is based on relatively new developments in probabilistic expert systems. This thesis demonstrates how these new developments provide a unified framework for uniting traditional classification methods and methods for integrating information from other spatial data sets, including data derived from digital elevation models, remotely sensed imagery and human experts.Two applications of the techniques are primarily considered. Firstly, the techniques are applied to the task of salinity mapping/ monitoring and compared to existing techniques. Large improvements are apparent. Secondly, the techniques are applied to salinity prediction, an application not previously considered by other researchers in this domain. The results are encouraging. Finally limitations of the approach are discussed.

Proceedings ArticleDOI
01 Sep 1997
TL;DR: In this paper, an online condition monitoring technique for detecting broken rotor bars in squirrel cage induction motors is presented, by evaluating both current and vibration signals and taking cognisance of interbar currents.
Abstract: This paper reviews aspects of online condition monitoring techniques for detecting broken rotor bars in squirrel cage induction motors, by evaluating both current and vibration signals and taking cognisance of interbar currents. The paper shows that significant interbar currents reduce the unbalance brought about by a broken rotor bar, and thus why it is necessary to focus attention elsewhere in the current spectrum to diagnose a broken bar. This paper also explains the technique of monitoring the vibration spectrum and the effect of interbar currents which produce tell-tale components in the vibration spectrum. Two separate online condition monitoring techniques can be used in conjunction with one another to provide an accurate diagnosis of squirrel cage induction motors with broken rotor bars. The theoretical and experimental results provide significant proof of the validity of these two online condition monitoring techniques. The natural progression is to provide a software program to perform the intricate mathematical analysis for industrial use.

Journal ArticleDOI
TL;DR: In this paper, the applicability of monitoring condition degradation in printed wiring assemblies (PWA) due to potential wearout failure mechanisms is discussed, and techniques are provided to assess the remaining life.
Abstract: Condition-based health management of electronic systems involves monitoring the system condition using real-time, in-situ sensing techniques and taking appropriate maintenance actions based on the physics-of-failure (PoF) interpretation of the collected data. This paper discusses the applicability of monitoring condition degradation in printed wiring assemblies (PWA) due to potential wearout failure mechanisms. Techniques are provided to assess the remaining life. © 1997 by John Wiley & Sons, Ltd.