85 research outputs found
Data classification and forecasting using the Mahalanobis-Taguchi method
Classification and forecasting are useful concepts in the field of condition monitoring. Condition monitoring refers to the analysis and monitoring of system characteristics to understand and identify deviations from normal operating conditions. This can be performed for prediction, diagnosis, or prognosis or a combination of any these purposes. Fault identification and diagnosis are usually achieved through data classification, while forecasting methods are usually used to accomplish the prediction objective. Data gathered from monitoring systems often consists of multiple multivariate time series and is fed into a model for data analysis using various techniques. One of the data analysis techniques used is the Mahalanobis-Taguchi strategy (MTS) because of its suitability for multivariate data analysis. MTS provides a means of extracting information in a multidimensional system by integrating information from different variables into a single composite metric. MTS is used to conduct analysis on the measurement parameters and seeks a correlation with the result while also seeking to optimize the analysis by identifying variables of importance strongly correlated with a defect or fault occurrence. This research presents the application of a MTS based system for predicting faults in heavy duty vehicles and the application of MTS in a multiclass classification problem. The benefits and practicality of the methodology in industrial applications are demonstrated through the use of real world data and discussion of results. --Abstract, page iv
DEVELOPMENT OF DIAGNOSTIC AND PROGNOSTIC METHODOLOGIES FOR ELECTRONIC SYSTEMS BASED ON MAHALANOBIS DISTANCE
Diagnostic and prognostic capabilities are one aspect of the many interrelated and complementary functions in the field of Prognostic and Health Management (PHM). These capabilities are sought after by industries in order to provide maximum operational availability of their products, maximum usage life, minimum periodic maintenance inspections, lower inventory cost, accurate tracking of part life, and no false alarms. Several challenges associated with the development and implementation of these capabilities are the consideration of a system's dynamic behavior under various operating environments; complex system architecture where the components that form the overall system have complex interactions with each other with feed-forward and feedback loops of instructions; the unavailability of failure precursors; unseen events; and the absence of unique mathematical techniques that can address fault and failure events in various multivariate systems.
The Mahalanobis distance methodology distinguishes multivariable data groups in a multivariate system by a univariate distance measure calculated from the normalized value of performance parameters and their correlation coefficients. The Mahalanobis distance measure does not suffer from the scaling effect--a situation where the variability of one parameter masks the variability of another parameter, which happens when the measurement ranges or scales of two parameters are different.
A literature review showed that the Mahalanobis distance has been used for classification purposes. In this thesis, the Mahalanobis distance measure is utilized for fault detection, fault isolation, degradation identification, and prognostics.
For fault detection, a probabilistic approach is developed to establish threshold Mahalanobis distance, such that presence of a fault in a product can be identified and the product can be classified as healthy or unhealthy. A technique is presented to construct a control chart for Mahalanobis distance for detecting trends and biasness in system health or performance. An error function is defined to establish fault-specific threshold Mahalanobis distance.
A fault isolation approach is developed to isolate faults by identifying parameters that are associated with that fault. This approach utilizes the design-of-experiment concept for calculating residual Mahalanobis distance for each parameter (i.e., the contribution of each parameter to a system's health determination). An expected contribution range for each parameter estimated from the distribution of residual Mahalanobis distance is used to isolate the parameters that are responsible for a system's anomalous behavior.
A methodology to detect degradation in a system's health using a health indicator is developed. The health indicator is defined as the weighted sum of a histogram bin's fractional contribution. The histogram's optimal bin width is determined from the number of data points in a moving window. This moving window approach is utilized for progressive estimation of the health indicator over time. The health indicator is compared with a threshold value defined from the system's healthy data to indicate the system's health or performance degradation.
A symbolic time series-based health assessment approach is developed. Prognostic measures are defined for detecting anomalies in a product and predicting a product's time and probability of approaching a faulty condition. These measures are computed from a hidden Markov model developed from the symbolic representation of product dynamics. The symbolic representation of a product's dynamics is obtained by representing a Mahalanobis distance time series in symbolic form.
Case studies were performed to demonstrate the capability of the proposed methodology for real time health monitoring. Notebook computers were exposed to a set of environmental conditions representative of the extremes of their life cycle profiles. The performance parameters were monitored in situ during the experiments, and the resulting data were used as a training dataset. The dataset was also used to identify specific parameter behavior, estimate correlation among parameters, and extract features for defining a healthy baseline. Field-returned computer data and data corresponding to artificially injected faults in computers were used as test data
A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions
Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.Peer ReviewedPostprint (published version
Sensor data-based decision making
Increasing globalization and growing industrial system complexity has amplified the interest in the use of information provided by sensors as a means of improving overall manufacturing system performance and maintainability. However, utilization of sensors can only be effective if the real-time data can be integrated into the necessary business processes, such as production planning, scheduling and execution systems. This integration requires the development of intelligent decision making models that can effectively process the sensor data into information and suggest appropriate actions. To be able to improve the performance of a system, the health of the system also needs to be maintained. In many cases a single sensor type cannot provide sufficient information for complex decision making including diagnostics and prognostics of a system. Therefore, a combination of sensors should be used in an integrated manner in order to achieve desired performance levels. Sensor generated data need to be processed into information through the use of appropriate decision making models in order to improve overall performance. In this dissertation, which is presented as a collection of five journal papers, several reactive and proactive decision making models that utilize data from single and multi-sensor environments are developed. The first paper presents a testbed architecture for Auto-ID systems. An adaptive inventory management model which utilizes real-time RFID data is developed in the second paper. In the third paper, a complete hardware and inventory management solution, which involves the integration of RFID sensors into an extremely low temperature industrial freezer, is presented. The last two papers in the dissertation deal with diagnostic and prognostic decision making models in order to assure the healthy operation of a manufacturing system and its components. In the fourth paper a Mahalanobis-Taguchi System (MTS) based prognostics tool is developed and it is used to estimate the remaining useful life of rolling element bearings using data acquired from vibration sensors. In the final paper, an MTS based prognostics tool is developed for a centrifugal water pump, which fuses information from multiple types of sensors in order to take diagnostic and prognostics decisions for the pump and its components --Abstract, page iv
Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods
This research extends the field of hyperspectral target detection by developing autonomous anomaly detection and signature matching methodologies that reduce false alarms relative to existing benchmark detectors, and are practical for use in an operational environment. The proposed anomaly detection methodology adapts multivariate outlier detection algorithms for use with hyperspectral datasets containing tens of thousands of non-homogeneous, high-dimensional spectral signatures. In so doing, the limitations of existing, non-robust, anomaly detectors are identified, an autonomous clustering methodology is developed to divide an image into homogeneous background materials, and competing multivariate outlier detection methods are evaluated for their ability to uncover hyperspectral anomalies. To arrive at a final detection algorithm, robust parameter design methods are employed to determine parameter settings that achieve good detection performance over a range of hyperspectral images and targets, thereby removing the burden of these decisions from the user. The final anomaly detection algorithm is tested against existing local and global anomaly detectors, and is shown to achieve superior detection accuracy when applied to a diverse set of hyperspectral images. The proposed signature matching methodology employs image-based atmospheric correction techniques in an automated process to transform a target reflectance signature library into a set of image signatures. This set of signatures is combined with an existing linear filter to form a target detector that is shown to perform as well or better relative to detectors that rely on complicated, information-intensive, atmospheric correction schemes. The performance of the proposed methodology is assessed using a range of target materials in both woodland and desert hyperspectral scenes
Software defect prediction using maximal information coefficient and fast correlation-based filter feature selection
Software quality ensures that applications that are developed are failure free. Some modern systems are intricate, due to the complexity of their information processes. Software fault prediction is an important quality assurance activity, since it is a mechanism that correctly predicts the defect proneness of modules and classifies modules that saves resources, time and developers’ efforts. In this study, a model that selects relevant features that can be used in defect prediction was proposed. The literature was reviewed and it revealed that process metrics are better predictors of defects in version systems and are based on historic source code over time. These metrics are extracted from the source-code module and include, for example, the number of additions and deletions from the source code, the number of distinct committers and the number of modified lines. In this research, defect prediction was conducted using open source software (OSS) of software product line(s) (SPL), hence process metrics were chosen. Data sets that are used in defect prediction may contain non-significant and redundant attributes that may affect the accuracy of machine-learning algorithms. In order to improve the prediction accuracy of classification models, features that are significant in the defect prediction process are utilised. In machine learning, feature selection techniques are applied in the identification of the relevant data. Feature selection is a pre-processing step that helps to reduce the dimensionality of data in machine learning. Feature selection techniques include information theoretic methods that are based on the entropy concept. This study experimented the efficiency of the feature selection techniques. It was realised that software defect prediction using significant attributes improves the prediction accuracy. A novel MICFastCR model, which is based on the Maximal Information Coefficient (MIC) was developed to select significant attributes and Fast Correlation Based Filter (FCBF) to eliminate redundant attributes. Machine learning algorithms were then run to predict software defects. The MICFastCR achieved the highest prediction accuracy as reported by various performance measures.School of ComputingPh. D. (Computer Science
An uncertainty prediction approach for active learning - application to earth observation
Mapping land cover and land usage dynamics are crucial in remote sensing since farmers
are encouraged to either intensify or extend crop use due to the ongoing rise in the world’s
population. A major issue in this area is interpreting and classifying a scene captured in
high-resolution satellite imagery. Several methods have been put forth, including neural
networks which generate data-dependent models (i.e. model is biased toward data) and
static rule-based approaches with thresholds which are limited in terms of diversity(i.e.
model lacks diversity in terms of rules). However, the problem of having a machine learning
model that, given a large amount of training data, can classify multiple classes over different
geographic Sentinel-2 imagery that out scales existing approaches remains open.
On the other hand, supervised machine learning has evolved into an essential part of many
areas due to the increasing number of labeled datasets. Examples include creating classifiers
for applications that recognize images and voices, anticipate traffic, propose products, act
as a virtual personal assistant and detect online fraud, among many more. Since these
classifiers are highly dependent from the training datasets, without human interaction or
accurate labels, the performance of these generated classifiers with unseen observations
is uncertain. Thus, researchers attempted to evaluate a number of independent models
using a statistical distance. However, the problem of, given a train-test split and classifiers
modeled over the train set, identifying a prediction error using the relation between train
and test sets remains open.
Moreover, while some training data is essential for supervised machine learning, what
happens if there is insufficient labeled data? After all, assigning labels to unlabeled datasets
is a time-consuming process that may need significant expert human involvement. When
there aren’t enough expert manual labels accessible for the vast amount of openly available
data, active learning becomes crucial. However, given a large amount of training and
unlabeled datasets, having an active learning model that can reduce the training cost of
the classifier and at the same time assist in labeling new data points remains an open
problem.
From the experimental approaches and findings, the main research contributions, which
concentrate on the issue of optical satellite image scene classification include: building
labeled Sentinel-2 datasets with surface reflectance values; proposal of machine learning
models for pixel-based image scene classification; proposal of a statistical distance based
Evidence Function Model (EFM) to detect ML models misclassification; and proposal of
a generalised sampling approach for active learning that, together with the EFM enables
a way of determining the most informative examples.
Firstly, using a manually annotated Sentinel-2 dataset, Machine Learning (ML) models
for scene classification were developed and their performance was compared to Sen2Cor the reference package from the European Space Agency – a micro-F1 value of 84%
was attained by the ML model, which is a significant improvement over the corresponding
Sen2Cor performance of 59%. Secondly, to quantify the misclassification of the ML models,
the Mahalanobis distance-based EFM was devised. This model achieved, for the labeled
Sentinel-2 dataset, a micro-F1 of 67.89% for misclassification detection. Lastly, EFM was
engineered as a sampling strategy for active learning leading to an approach that attains
the same level of accuracy with only 0.02% of the total training samples when compared
to a classifier trained with the full training set.
With the help of the above-mentioned research contributions, we were able to provide
an open-source Sentinel-2 image scene classification package which consists of ready-touse
Python scripts and a ML model that classifies Sentinel-2 L1C images generating a
20m-resolution RGB image with the six studied classes (Cloud, Cirrus, Shadow, Snow,
Water, and Other) giving academics a straightforward method for rapidly and effectively
classifying Sentinel-2 scene images. Additionally, an active learning approach that uses, as
sampling strategy, the observed prediction uncertainty given by EFM, will allow labeling
only the most informative points to be used as input to build classifiers; Sumário:
Uma Abordagem de Previsão de Incerteza para
Aprendizagem Ativa – Aplicação à Observação da Terra
O mapeamento da cobertura do solo e a dinâmica da utilização do solo são cruciais na
deteção remota uma vez que os agricultores são incentivados a intensificar ou estender as
culturas devido ao aumento contÃnuo da população mundial. Uma questão importante
nesta área é interpretar e classificar cenas capturadas em imagens de satélite de alta resolução.
Várias aproximações têm sido propostas incluindo a utilização de redes neuronais
que produzem modelos dependentes dos dados (ou seja, o modelo é tendencioso em relação
aos dados) e aproximações baseadas em regras que apresentam restrições de diversidade
(ou seja, o modelo carece de diversidade em termos de regras). No entanto, a criação de
um modelo de aprendizagem automática que, dada uma uma grande quantidade de dados
de treino, é capaz de classificar, com desempenho superior, as imagens do Sentinel-2 em
diferentes áreas geográficas permanece um problema em aberto.
Por outro lado, têm sido utilizadas técnicas de aprendizagem supervisionada na resolução
de problemas nas mais diversas áreas de devido à proliferação de conjuntos de dados etiquetados.
Exemplos disto incluem classificadores para aplicações que reconhecem imagem
e voz, antecipam tráfego, propõem produtos, atuam como assistentes pessoais virtuais e
detetam fraudes online, entre muitos outros. Uma vez que estes classificadores são fortemente
dependente do conjunto de dados de treino, sem interação humana ou etiquetas
precisas, o seu desempenho sobre novos dados é incerta. Neste sentido existem propostas
para avaliar modelos independentes usando uma distância estatÃstica. No entanto, o problema
de, dada uma divisão de treino-teste e um classificador, identificar o erro de previsão
usando a relação entre aqueles conjuntos, permanece aberto.
Mais ainda, embora alguns dados de treino sejam essenciais para a aprendizagem supervisionada,
o que acontece quando a quantidade de dados etiquetados é insuficiente? Afinal,
atribuir etiquetas é um processo demorado e que exige perÃcia, o que se traduz num envolvimento
humano significativo. Quando a quantidade de dados etiquetados manualmente por
peritos é insuficiente a aprendizagem ativa torna-se crucial. No entanto, dada uma grande
quantidade dados de treino não etiquetados, ter um modelo de aprendizagem ativa que
reduz o custo de treino do classificador e, ao mesmo tempo, auxilia a etiquetagem de novas
observações permanece um problema em aberto.
A partir das abordagens e estudos experimentais, as principais contribuições deste trabalho,
que se concentra na classificação de cenas de imagens de satélite óptico incluem:
criação de conjuntos de dados Sentinel-2 etiquetados, com valores de refletância de superfÃcie;
proposta de modelos de aprendizagem automática baseados em pixels para classificação de cenas de imagens de satétite; proposta de um Modelo de Função de Evidência (EFM)
baseado numa distância estatÃstica para detetar erros de classificação de modelos de aprendizagem;
e proposta de uma abordagem de amostragem generalizada para aprendizagem
ativa que, em conjunto com o EFM, possibilita uma forma de determinar os exemplos mais
informativos.
Em primeiro lugar, usando um conjunto de dados Sentinel-2 etiquetado manualmente,
foram desenvolvidos modelos de Aprendizagem Automática (AA) para classificação de cenas
e seu desempenho foi comparado com o do Sen2Cor – o produto de referência da
Agência Espacial Europeia – tendo sido alcançado um valor de micro-F1 de 84% pelo classificador,
o que representa uma melhoria significativa em relação ao desempenho Sen2Cor
correspondente, de 59%. Em segundo lugar, para quantificar o erro de classificação dos
modelos de AA, foi concebido o Modelo de Função de Evidência baseado na distância de
Mahalanobis. Este modelo conseguiu, para o conjunto de dados etiquetado do Sentinel-2
um micro-F1 de 67,89% na deteção de classificação incorreta. Por fim, o EFM foi utilizado
como uma estratégia de amostragem para a aprendizagem ativa, uma abordagem
que permitiu atingir o mesmo nÃvel de desempenho com apenas 0,02% do total de exemplos
de treino quando comparado com um classificador treinado com o conjunto de treino
completo.
Com a ajuda das contribuições acima mencionadas, foi possÃvel desenvolver um pacote
de código aberto para classificação de cenas de imagens Sentinel-2 que, utilizando num
conjunto de scripts Python, um modelo de classificação, e uma imagem Sentinel-2 L1C,
gera a imagem RGB correspondente (com resolução de 20m) com as seis classes estudadas
(Cloud, Cirrus, Shadow, Snow, Water e Other), disponibilizando à academia um método
direto para a classificação de cenas de imagens do Sentinel-2 rápida e eficaz. Além disso, a
abordagem de aprendizagem ativa que usa, como estratégia de amostragem, a deteção de
classificacão incorreta dada pelo EFM, permite etiquetar apenas os pontos mais informativos
a serem usados como entrada na construção de classificadores
Combining adaptive and designed statistical experimentation : process improvement, data classification, experimental optimization and model building
Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references.Research interest in the use of adaptive experimentation has returned recently. This historic technique adapts and learns from each experimental run but requires quick runs and large effects. The basis of this renewed interest is to improve experimental response and it is supported by fast, deterministic computer experiments and better post-experiment data analysis. The unifying concept of this thesis is to present and evaluate new ways of using adaptive experimentation combined with the traditional statistical experiment. The first application uses an adaptive experiment as a preliminary step to a more traditional experimental design. This provides experimental redundancy as well as greater model robustness. The number of extra runs is minimal because some are common and yet both methods provide estimates of the best setting. The second use of adaptive experimentation is in evolutionary operation. During regular system operation small, nearly unnoticeable, variable changes can be used to improve production dynamically. If these small changes follow an adaptive procedure there is high likelihood of improvement and integrating into the larger process development. Outside of the experimentation framework the adaptive procedure is shown to combine with other procedures and yield benefit. Two examples used here are an unconstrained numerical optimization procedure as well as classification parameter selection. The final area of new application is to create models that are a combination of an adaptive experiment with a traditional statistical experiment.(cont.) Two distinct areas are examined, first, the use of the adaptive experiment to determine the covariance structure, and second, the direct incorporation of both data sets in an augmented model. Both of these applications are Bayesian with a heavy reliance on numerical computation and simulation to determine the combined model. The two experiments investigated could be performed on the same physical or analytical model but are also extended to situations with different fidelity models. The potential for including non-analytical, even human, models is also discussed. The evaluative portion of this thesis begins with an analytic foundation that outlines the usefulness as well as the limitations of the procedure. This is followed by a demonstration using a simulated model and finally specific examples are drawn from the literature and reworked using the method. The utility of the final result is to provide a foundation to integrate adaptive experimentation with traditional designed experiments. Giving industrial practitioners a solid background and demonstrated foundation should help to codify this integration. The final procedures represent a minimal departure from current practice but represent significant modeling and analysis improvement.by Chad Ryan Foster.Sc.D
Multivariate Analysis in Management, Engineering and the Sciences
Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field
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