Detection of Permanent Magnet DC Motor Failure Due to Brush Wear Using Parameter Estimation and Statistical Analysis

Failure detection of DC motors is a common study, and could be extremely useful in real world applications. Undiagnosed eminent motor failure could cause a range of effects, and without maintenance will inevitably occur. Motor faults can be classified as electrical or mechanical, both with wide ranges of causes. Electrical failure includes stator or rotor winding faults, inverter faults, position of sensor faults in brushless motors, bearing faults, and brush faults. Mechanical faults include bearing faults, broken rotor bar, rotor eccentricity faults, end ring faults, and load faults. The aim of this study was to observe the effect of brush fault within a permanent magnet DC (PMDC) motor. Carbon contact brushes are used in PMDC motors to transmit electrical current from the stator of the motor to the rotor of the motor, ensuring the rotation of the commutators. Over time, the carbon contact becomes worn down from commutators continually moving across them. As the contacts length is decreased, the spring holding it in place becomes more stretched out, putting in more effort to hold the brush in place. This introduces a resistance, referred to as a contact resistance, that can affect the motor speed and performance. Changes in speed and resistance can be measured and observed, and curves can be fitted to their relationship with statistical significance. We can also create a simulation method using basic differential equations that describe the motor and introducing random noise to the simulation with generation of random numbers for the motor parameters. Finally, a confidence interval is generated, and eminent motor failure can be predicted when values measured values stray from the simulated path. Erratic motor behavior can also be observed at the point of eminent motor failure

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

Estimación de características relevantes para el monitoreo de condición de motores de combustión interna a partir de señales de vibración

Condition monitoring of Internal Combustion Engines (ICE) benefits cost-effective operations in the modern industrial sector. Because of this, vibration signals are commonly monitored as part of a non-invasive approach to ICE analysis. However, vibration-based ICE monitoring poses a challenge due to the properties of this kind of signals. They are highly dynamic and non-stationary, let alone the diverse sources involved in the combustion process. In this paper, we propose a feature relevance estimation strategy for vibration-based ICE analysis. Our approach is divided into three main stages: signal decomposition using an Ensemble Empirical Mode Decomposition algorithm, multi-domain parameter estimation from time and frequency representations, and a supervised feature selection based on the Relief-F technique. Accordingly, we decomposed the vibration signals by using self-adaptive analysis to represent nonlinear and non-stationary time series. Afterwards, time and frequency-based parameters were calculated to code complex and/or non-stationary dynamics. Subsequently, we computed a relevance vector index to measure the contribution of each multi-domain feature to the discrimination of different fuel blend estimation/diagnosis categories for ICE. In particular, we worked with an ICE dataset collected from fuel blends under normal and fault scenarios at different engine speeds to test our approach. Our classification results presented nearly 98% of accuracy after using a k-Nearest Neighbors machine. They reveal the way our approach identifies a relevant subset of features for ICE condition monitoring. One of the benefits is the reduced number of parameters.El monitoreo de condición de motores de combustión interna (MCI) facilita que las operaciones del sector industrial moderno sean más rentables. En este sentido, las señales de vibración comúnmente son empleadas como un enfoque no invasivo para el análisis de MCI. Sin embargo, el monitoreo de MCI basado en vibraciones presenta un desafío relacionado con las propiedades de la señal, la cual es altamente dinámica y noestacionaria, sin mencionar las diversas fuentes presentes durante el proceso de combustión. En este artículo, se propone una estrategia de análisis de relevancia orientada al monitoreo de MCI basado en vibraciones. Este enfoque incorpora tres etapas principales: descomposición de la señal utilizando un algoritmo de Ensemble Empirical Mode Decomposition, estimación de parámetros multi-dominio desde representaciones en tiempo y frecuencia, y una selección supervisada de características basada en Relief-F. Así, las señales de vibración se descomponen utilizando un análisis auto-adaptativo para representar la no-linealidad y no-estacionariedad de las series de tiempo. Luego, para codificar dinámicas complejas y/o no estacionarias, se calculan algunos parámetros en el dominio del tiempo y de la frecuencia. Posteriormente, se calcula un vector de índice de relevancia para cuantificar la contribución de cada una de las características multidominio para discriminar diferentes categorías de estimación de mezcla de combustible y diagnóstico de MCI. Los resultados de clasificación obtenidos (cercanos al 98% de acierto) en una base de datos de MCI, revelan como la propuesta planteada identifica un subconjunto de características relevantes en el monitorio de condición de MCI

Hidden Markov Model-based Methods In Condition Monitoring of Machinery Systems

Ph.DDOCTOR OF PHILOSOPH

Approach for Improved Signal-Based Fault Diagnosis of Hot Rolling Mills

Der hier vorgestellte Ansatz ist in der Lage, zwei spezifische schwere Fehler zu erkennen, sie zu identifizieren, zwischen vier verschiedenen Systemzuständen zu unterscheiden und eine Prognose bezüglich des Systemverhaltens zu geben. Die vorliegende Arbeit untersucht die Zustandsüberwachung des komplexen Herstellungsprozesses eines Warmbandwalzwerks. Eine signalbasierte Fehlerdiagnose und ein Fehlerprognoseansatz für den Bandlauf werden entwickelt. Eine Literaturübersicht gibt einen Überblick über die bisherige Forschung zu verwandten Themen. Es wird gezeigt, dass die große Anzahl vorheriger Arbeiten diese Thematik nicht gelöst hat und dass weitere Untersuchungen erforderlich sind, um eine zufriedenstellende Lösung der behandelten Probleme zu erhalten. Die Entwicklung einer neuen Signalverarbeitungskette und die Signalverarbeitungsschritte sind detailliert dargestellt. Die Klassifikationsaufgabe wird in Fehlerdiagnose, Fehleridentifikation und Fehlerprognose differenziert. Der vorgeschlagene Ansatz kombiniert fünf verschiedene Methoden zur Merkmalsextraktion, nämlich Short-Time Fourier Transformation, kontinuierliche Wavelet Transformation, diskrete Wavelet Transformation, Wigner-Ville Distribution und Empirical Mode Decomposition, mit zwei verschiedenen Klassifikationsalgorithmen, nämlich Support-Vektor Maschine und eine Variation der Kreuzkorrelation, wobei letztere in dieser Arbeit entwickelt wurde. Kombinationen dieser Merkmalsextraktion und Klassifikationsverfahren werden an Walzkraft-Daten aus einer Warmbreitbandstraße angewendet.The approach introduced here is able to detect two specific severe faults, to identify them, to distinguish between four different system states, and to give a prognosis on the system behavior. The presented work investigates the condition monitoring of the complex production process of a hot strip rolling mill. A signal-based fault diagnosis and fault prognosis approach for strip travel is developed. A literature review gives an overview about previous research on related topics. It is shown that the great amount of previous work does not cope with the problems treated in this work and that further investigation is necessary to provide a satisfactory solution. The design of a new signal processing chain is presented and the signal processing steps are detailed. The classification task is differentiated into fault detection, fault identification and fault prognosis. The proposed approach combines five different methods for feature extraction, namely short time Fourier transform, continuous wavelet transform, discrete wavelet transform, Wigner-Ville distribution, and empirical mode decomposition, with two different classification algorithms, namely support vector machine and a variation of cross-correlation, the latter developed in this work. Combinations of these feature extraction and classification methods are applied to rolling force data originating from a hot strip mill

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

PROBABILISTIC FAULT DIAGNOSIS AND PROGNOSIS IN PRECOGNITIVE MAINTENANCE FOR HIGH-PERFORMANCE MANUFACTURING INDUSTRIES

Ph.DDOCTOR OF PHILOSOPH