Investigations into the feasibility of an on-line test methodology

This thesis aims to understand how information coding and the protocol that it supports can affect the characteristics of electronic circuits. More specifically, it investigates an on-line test methodology called IFIS (If it Fails It Stops) and its impact on the design, implementation and subsequent characteristics of circuits intended for application specific lC (ASIC) technology. The first study investigates the influences of information coding and protocol on the characteristics of IFIS systems. The second study investigates methods of circuit design applicable to IFIS cells and identifies the· technique possessing the characteristics most suitable for on-line testing. The third study investigates the characteristics of a 'real-life' commercial UART re-engineered using the techniques resulting from the previous two studies. The final study investigates the effects of the halting properties endowed by the protocol on failure diagnosis within IFIS systems. The outcome of this work is an identification and characterisation of the factors that influence behaviour, implementation costs and the ability to test and diagnose IFIS designs

Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

Condition monitoring and early fault diagnosis for wind turbines have become essential industry practice as they help improve wind farm reliability, overall performance and productivity. If not detected and rectified at early stages, some faults can be catastrophic with significant loss or revenue along with interruption to the business relying mainly on wind energy. The failure of Wind turbine results in system downtime and repairing or replacement expenses that significantly reduce the annual income. Such failures call for more systematized operation and maintenance schemes to ensure the reliability of wind energy conversion systems. Condition monitoring and fault diagnosis systems of wind turbine play an important role in reducing maintenance and operational costs and increase system reliability. This paper is aimed at providing the reader with the overall feature for wind turbine condition monitoring and fault diagnosis which includes various potential fault types and locations along with the signals to be analyzed with different signal processing methods

Use of Modal Representation for the Supporting Structure in Model Based Fault Identification of Large Rotating Machinery: Part 1 – Theoretical Remarks

Fault identification by means of model-based techniques, both in frequency and time domain, is often employed in diagnostics of rotating machines, when the main task is to locate and to evaluate the severity of the malfunction. The model of the fully assembled machine is composed by the submodels of the rotor, of the bearings and of the foundation, while the effect of the faults is modelled by means of equivalent force systems. Some identification techniques, such as the least squares identification in frequency domain, proposed by the authors, have proven to be quite robust even if the submodels are not fine-tuned. Anyhow, the use of a reliable model can increase the accuracy of the identification. Normally a supporting structure is represented by means of rigid foundation or by pedestals, i.e. 2 d.o.f. mass–spring–damper systems, but these kind of models are often not able to reproduce correctly the influence of the dynamical behaviour of the supporting structure on the shaft, especially in large machines where coupled modes are present. Therefore, peculiar aspect of this paper is the use of a modal foundation to model the supporting structure of the machine and the method is discussed in detail in this first part. The modal representation of the foundation is then introduced in the least squares identification technique in frequency domain

The use of mechanical redundancy for fault detection in non-stationary machinery

The classical approach to machinery fault detection is one where a machinery’s condition is constantly compared to an established baseline with deviations indicating the occurrence of a fault. With the absence of a well-established baseline, fault detection for variable duty machinery requires the use of complex machine learning and signal processing tools. These tools require extensive data collection and expert knowledge which limits their use for industrial applications. The thesis at hand investigates the problem of fault detection for a specific class of variable duty machinery; parallel machines with simultaneously loaded subsystems. As an industrial case study, the parallel drive stations of a novel material haulage system have been instrumented to confirm the mechanical response similarity between simultaneously loaded machines. Using a table-top fault simulator, a preliminary statistical algorithm was then developed for fault detection in bearings under non-stationary operation. Unlike other state of the art fault detection techniques used in monitoring variable duty machinery, the proposed algorithm avoided the need for complex machine learning tools and required no previous training. The limitations of the initial experimental setup necessitated the development of a new machinery fault simulator to expand the investigation to include transmission systems. The design, manufacturing and setup of the various subsystems within the new simulator are covered in this manuscript including the mechanical, hydraulic and control subsystems. To ensure that the new simulator has successfully met its design objectives, extensive data collection and analysis has been completed and is presented in this thesis. The results confirmed that the developed machine truly represents the operation of a simultaneously loaded machine and as such would serve as a research tool for investigating the application of classical fault detection techniques to parallel machines in non-stationary operation.Master's These

Fault Diagnosis Via Univariate Frequency Analysis Monitoring: A Novel Technique Applied to a Simulated Integrated Drive Generator

The purpose of this research was to develop a fault detection and diagnostic method that would be able to detect and isolate seeded faults in data that was generated from a simulated integrated drive generator. The approach to the solution for this problem is summarized below. A novel approach for the detection and diagnoses of an anomaly due the occurrence of a fault within a system has been developed. This innovative technique uses specific characteristics of the frequency spectrum of a univariate signal to monitor system health for abnormal behavior due to previously characterized component failure. A fault detection and diagnostic scheme was developed that used dual heteroassociative kernel regression models. The first of these empirical models estimates selected features from the analytical redundant spectrum characteristic profile of the exciter current using power demand, a stressor, placed on the system as input query. The predicted spectrum features were compared to the actual characteristic features, which resulted in the generation of a residual signal. This signal was then analyzed in order to determine if they were the result of normal system disturbances or a predefined fault. If a fault was detected, the residual signal was passed to the second model, which isolated, and given enough information, identified the specific component of components causing the anomaly. Two case studies are presented to illustrate the capability to detect, isolate, and identify a system anomaly. As demonstrated, the monitoring of the frequency spectrum of a single variable can provide adequate indication of equipment health. With the availability of the appropriate data, as in the first case, it is possible for the development of three-layer detection and diagnostic systems that provides fault detection, isolation, and identification. A three-layer detection and diagnostic system is essential in the development of more advance health monitoring and prognostic systems. Despite some shortcomings in the simulated data made available for this work, this method is believed to be applicable to data that more realistically captures real-world relationships, including sensor noise and faults that grow with time

Behavioral synthesis from VHDL using structured modeling

This dissertation describes work in behavioral synthesis involving the development of a VHDL Synthesis System VSS which accepts a VHDL behavioral input specification and performs technology independent synthesis to generate a circuit netlist of generic components. The VHDL language is used for input and output descriptions. An intermediate representation which incorporates signal typing and component attributes simplifies compilation and facilitates design optimization.A Structured Modeling methodology has been developed to suggest standard VHDL modeling practices for synthesis. Structured modeling provides recommendations for the use of available VHDL description styles so that optimal designs will be synthesized.A design composed of generic components is synthesized from the input description through a process of Graph Compilation, Graph Criticism, and Design Compilation. Experiments were performed to demonstrate the effects of different modeling styles on the quality of the design produced by VSS. Several alternative VHDL models were examined for each benchmark, illustrating the improvements in design quality achieved when Structured Modeling guidelines were followed

An efficient Fault Localization Algorithm for IP/WDM Networks

We propose an algorithm for localizing multiple failures in an IP/WDM network. They can be either hard failures (unexpected events that interrupt suddenly the established channels) or soft failures (events that progressively degrade the quality of transmission). Hard failures are detected at the WDM layer, whereas soft failures can be detected at the optical layer if proper testing equipment is deployed, and/or by performance monitoring at a higher layer, which is here IP. The algorithm also tolerates missing and false alarms. Even without missing and false alarms, multiple fault localization is NP-hard. The diagnosis phase (i.e., the localization of the faulty components upon reception of the alarms) can however remain very fast, but at the expense of a very complex precomputation phase, carried out whenever the optical channels are set up or cleared down. We show how the algorithm performs on an example of an IP/WDM network

Multidimensional Tensor-Based Inductive Thermography With Multiple Physical Fields for Offshore Wind Turbine Gear Inspection

Condition monitoring (CM), fault diagnosis (FD), and nondestructive testing (NDT) are currently considered crucial means to increase the reliability and availability of wind turbines. Many research works have focused on CM and FD for different components of wind turbine. Gear is typically used in a wind turbine. There is insufficient space to locate the sensors for long-term monitoring of fatigue state of gear, thus, offline inspection using NDT in both manufacturing and maintenance processes are critically important. This paper proposes an inductive thermography method for gear inspection. The ability to track the properties variation in gear such as electrical conductivity, magnetic permeability, and thermal conductivity has promising potential for the evaluation of material state undertaken by contact fatigue. Conventional thermography characterization methods are built based on single physical field analysis such as heat conduction or in-plane eddy current field. This study develops a physics-based multidimensional spatial-transient-stage tensor model to describe the thermo optical flow pattern for evaluating the contact fatigue damage. A helical gear with different cycles of contact fatigue tests was investigated and the proposed method was verified. It indicates that the proposed methods are effective tool for gear inspection and fatigue evaluation, which is important for early warning and condition-based maintenance

Support vector machine based classification in condition monitoring of induction motors

Continuous and trouble-free operation of induction motors is an essential part of modern power and production plants. Faults and failures of electrical machinery may cause remarkable economical losses but also highly dangerous situations. In addition to analytical and knowledge-based models, application of data-based models has established a firm position in the induction motor fault diagnostics during the last decade. For example, pattern recognition with Neural Networks (NN) is widely studied. Support Vector Machine (SVM) is a novel machine learning method introduced in early 90's. It is based on the statistical learning theory presented by V.N. Vapnik, and it has been successfully applied to numerous classification and pattern recognition problems such as text categorization, image recognition and bioinformatics. SVM based classifier is built to minimize the structural misclassification risk, whereas conventional classification techniques often apply minimization of the empirical risk. Therefore, SVM is claimed to lead enhanced generalisation properties. Further, application of SVM results in the global solution for a classification problem. Thirdly, SVM based classification is attractive, because its efficiency does not directly depend on the dimension of classified entities. This property is very useful in fault diagnostics, because the number of fault classification features does not have to be drastically limited. However, SVM has not yet been widely studied in the area of fault diagnostics. Specifically, in the condition monitoring of induction motor, it does not seem to have been considered before this research. In this thesis, a SVM based classification scheme is designed for different tasks in induction motor fault diagnostics and for partial discharge analysis of insulation condition monitoring. Several variables are compared as fault indicators, and forces on rotor are found to be important in fault detection instead of motor current that is currently widely studied. The measurement of forces is difficult, but easily measurable vibrations are directly related to the forces. Hence, vibration monitoring is considered in more detail as the medium for the motor fault diagnostics. SVM classifiers are essentially 2-class classifiers. In addition to the induction motor fault diagnostics, the results of this thesis cover various methods for coupling SVMs for carrying out a multi-class classification problem.reviewe