Health assessment of rotary machinery based on integrated feature selection and Gaussian mixed model

Bearing failure is the most common failure mode of all rotary machinery failures, and can interrupt the production in a plant causing unscheduled downtime and production losses. A bearing failure also has the potential to damage machinery causing soaring machinery repair and/or replacement costs. In order to prevent unexpected bearing failure, a health assessment method is proposed in this paper. It employs an integrated feature selection approach and Gaussian mixture model (GMM). Firstly, the integrated feature selection approach, which combines empirical mode decomposition (EMD), singular value decomposition (SVD) and Principal Component Analysis (PCA), processes nonlinear and non-stationary vibration signals of a bearing and extracts features for health assessment. Then, GMM is utilized to evaluate and track the health degradation of the bearing in terms of confidence values (CV). This method, which is notable for bearing health tracking and detect the defect at its incipient stage, can be used without the need for failure datasets in applications. Finally, the feasibility and efficiency of this method was validated by two datasets of different bearing experiments

A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

Fault diagnostic methods are challenged by their applications to industrial components operating in evolving environments of their working conditions. To overcome this problem, we propose a Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach (4SFD), which allows dynamically selecting the features to be used for performing the diagnosis, detecting the necessity of updating the diagnostic model and automatically updating it. Within the proposed approach, the main novelty is the semi-supervised feature selection method developed to dynamically select the set of features in response to the evolving environment. An artificial Gaussian and a real world bearing dataset are considered for the verification of the proposed approach

RADIS : a real-time anomaly detection intelligent system for fault diagnosis of marine machinery

By enhancing data accessibility, the implementation of data-driven models has been made possible to empower strategies in relation to O&M activities. Such models have been extensively applied to perform anomaly detection tasks, with the express purpose of detecting data patterns that deviate significantly from normal operational behaviour. Due to its preeminent importance in the maritime industry to adequately identify the behaviour of marine systems, the Real-time Anomaly Detection Intelligent System (RADIS) framework, constituted by a Long Short-Term Memory-based Variational Autoencoder in tandem with multi-level Otsu's thresholding, is proposed. RADIS aims to address the current gaps identified within the maritime industry in relation to data-driven model applications for enabling smart maintenance. To assess the performance of such a framework, a case study on a total of 14 parameters obtained from sensors installed on a diesel generator of a tanker ship is introduced to highlight the implementation of RADIS. Results demonstrated the capability of RADIS to be part of a diagnostic analytics tool that will promote the implementation of smart maintenance within the maritime industry, as RADIS detected an average of 92.5% of anomalous instances in the presented case study

Integrated performance prediction and quality control in manufacturing systems

textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin

Effect of HFS Based Feature Selection on Cluster Analysis

Diplomová práce se zabývá shlukovou analýzou. Shlukování má své základy v mnoha oblastech lidského vědění zahrnujících získávání dat, statistiku, biologii a strojové učení. Hlavní náplní práce je zpracování rešerše metod shlukové analýzy, metod pro stanovení počtu shluků a stručný přehled metod selekce příznaků v úlohách bez učitele. Neméně důležitou součástí je realizace softwaru pro porovnání různých metod shlukové analýzy se zaměřením na úspěšnost při stanovování počtu shluků a řazení jednotlivých instancí do správných tříd. Součástí programu je implementace metody selekce příznaků HFS. Experimentální ověření metod proběhlo ve vývojovém prostředí Matlab. Ve svém závěru diplomová práce porovnává úspěšnost shlukovacích metod na datech se známými výstupními třídami a posuzuje přínos metody selekce příznaků HFS v úlohách bez učitele pro úspěšnost shlukové analýzyMaster´s thesis is focused on cluster analysis. Clustering has its roots in many areas, including data mining, statistics, biology and machine learning. The aim of this thesis is to elaborate a recherche of cluster analysis methods, methods for determining number of clusters and a short survey of feature selection methods for unsupervised learning. The very important part of this thesis is software realization for comparing different cluster analysis methods focused on finding optimal number of clusters and sorting data points into correct classes. The program also consists of feature selection HFS method implementation. Experimental methods validation was processed in Matlab environment. The end of master´s thesis compares success of clustering methods using data with known output classes and assesses contribution of feature selection HFS method for unsupervised learning for quality of cluster analysis.