Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

In this study, condition monitoring strategies are examined for gas turbine engines using vibration data. The focus is on data-driven approaches, for this reason a novelty detection framework is considered for the development of reliable data-driven models that can describe the underlying relationships of the processes taking place during an engine’s operation. From a data analysis perspective, the high dimensionality of features extracted and the data complexity are two problems that need to be dealt with throughout analyses of this type. The latter refers to the fact that the healthy engine state data can be non-stationary. To address this, the implementation of the wavelet transform is examined to get a set of features from vibration signals that describe the non-stationary parts. The problem of high dimensionality of the features is addressed by “compressing” them using the kernel principal component analysis so that more meaningful, lowerdimensional features can be used to train the pattern recognition algorithms. For feature discrimination, a novelty detection scheme that is based on the one-class support vector machine (OCSVM) algorithm is chosen for investigation. The main advantage, when compared to other pattern recognition algorithms, is that the learning problem is being cast as a quadratic program. The developed condition monitoring strategy can be applied for detecting excessive vibration levels that can lead to engine component failure. Here, we demonstrate its performance on vibration data from an experimental gas turbine engine operating on different conditions. Engine vibration data that are designated as belonging to the engine’s “normal” condition correspond to fuels and airto-fuel ratio combinations, in which the engine experienced low levels of vibration. Results demonstrate that such novelty detection schemes can achieve a satisfactory validation accuracy through appropriate selection of two parameters of the OCSVM, the kernel width γ and optimization penalty parameter ν. This selection was made by searching along a fixed grid space of values and choosing the combination that provided the highest cross-validation accuracy. Nevertheless, there exist challenges that are discussed along with suggestions for future work that can be used to enhance similar novelty detection schemes

Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews

These days with TV-shows and starred chefs, new kinds of cuisines appear in the market. The main cuisines like French, Italian, Japanese, Chinese and Indian are always appreciated but they are no longer the most popular. The new trend is the fusion cuisine, which is obtained by combining different main cuisines. The opening of a new restaurant proposing new kinds of cuisine produces a lot of excitement in people. They feel the need to try it and be part of this new culture. Yelp is a platform which publishes crowd sourced reviews about different businesses, in particular, restaurants. For some restaurants in Yelp if the kind of cuisine is available, usually, there is a tag only for the main cuisines, but there is no information for the fusion cuisine. There is a need to develop a system which is able to identify restaurants proposing fusion cuisine (novel or unknown cuisines). This proposal is to address the novelty detection task using Yelp reviews. The idea is that the semi-supervised Machine Learning models trained only on the reviews of restaurants proposing the main cuisine will be able to discriminate between restaurants providing the main cuisine and restaurants providing the novel ones. We propose effective novelty detection approaches for the unknown cuisine type identification problem using Long Short Term Memory (LSTM), autoencoder and Term-Frequency and Inverse Document Frequency(). Our main idea is to obtain features from LSTM, autoencoder and TF-IDF and use these features with standard semi-supervised novelty detection algorithms like Gaussian Mixture Model, Isolation Forest and One-class Support Vector Machines (SVM) to identify the unknown cuisines. We conducted extensive experiments that prove the effectiveness of our approaches. The score that we obtained has a very high discrimination power because the best value of AUROC for the novelty detection problem is 0.85 from LSTM. LSTM outperforms our baseline model of TF-IDF and the main motivation is due to its ability to retain only the useful parts of a sentence

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 40)

Abstracts are provided for 181 patents and patent applications entered into the NASA scientific and technical information system during the period July 1991 through December 1991. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 39)

Abstracts are provided for 154 patents and patent applications entered into the NASA scientific and technical information systems during the period Jan. 1991 through Jun. 1991. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 14)

Abstracts are cited for 213 patents and applications for patent introduced into the NASA scientific and technical information system during the period of July 1978 through December 1978. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or application for patent

On-Line Monitoring of Engine Health Through the Analysis of Contaminants in Engine Lubricant

Monitoring automobile liquids, such as engine lubricants, has received increasing attention recently mainly due to environmental and safety legislation, coat saving measures, and customer demand. Literature review in monitoring engine lubricant condition indicates systems approach, an intellectual discipline method to address complex problem, has never been used to monitor engine performance and health through the engine sub-systems such as lubricant system. The literature review also points toward deficiency in considering lubricant as a source of information for engine performance evaluation, and lack of understanding of engine lubricant as a medium with random properties. Engine lubricant condition reflects the state of health of engine through its properties. Recognition and analysis of the correlation between engine lubricant system based on the lubricant properties and engine performance is crucial to provide insight into engine health. The contribution of this research will be implementation of systems approach to monitor engine performance through engine lubricant using new methodologies of surface plasmon resonance, object shape based optical analysis and statistical optical analysis methodologies to monitor optical properties of lubricant with respect to aging process and contaminants in real time and on-line. Degradation of engine lubricant causes variation in the optical properties of lubricant such as refractive index, absorption, statistical optical characteristics, shape parameters and etc. The purpose of using surface plasmon resonance (SPR) is to study the change in the reflectivity and incidence angle caused by variation in the refractive index and absorption of lubricant due to its degradation and presence of contaminants. Utilization of SPR measurement for characterization of engine lubricant will develop new knowledge which can be used for on-line condition monitoring of lubricant quality. To investigate the variation in statistical optical characteristics of lubricant, this research also introduces two new methodologies. Statistical optic and object shape-based methodologies are based on the optical analysis of the distortion effect when an object image is obtained through a thin random medium. In the object shape-based optical analysis, several parameters of an acquired object image are measured and compared. In the statistical optic analysis methodology, statistical auto and cross-characteristics are used for the analysis of combined object-lubricant images. Both proposed methodologies utilize the comparison of measured and calculated parameters for fresh and contaminated lubricants. Proposed methodologies are verified experimentally showing ability to distinguish lubricant with different contamination individually and in a combined form. Capabilities of the proposed methodologies are extended to establish the linkage between accumulated travelled distance and the change in the optical statistical properties of the lubricant. Also, on board analysis to detect the presence of coolant, gasoline and water (1%-5%) are performed

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 38)

Abstracts are provided for 132 patents and patent applications entered into the NASA scientific and technical information system during the period July 1990 through December 1990. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application