Indirect multisignal monitoring and diagnosis of drill wear

A machine tool utilisation rate can be improved by an advanced condition monitoring system using modern sensor and signal processing techniques. A drilling test and analysis program for indirect tool wear measurement forms the basis of this thesis. For monitoring the drill wear a number of monitoring methods such as vibration, acoustic emission, sound, spindle power and axial force were tested. The signals were analysed in the time domain using statistical methods such as root mean square (rms) value and maximum. The signals were further analysed using Fast Fourier Transform (FFT) to determine their frequency contents. The effectiveness of the best sensors and analysis methods for predicting the remaining lifetime of a tool in use has been defined. The results show that vibration, sound and acoustic emission measurements are more reliable for tool wear monitoring than the most commonly used measurements of power consumption, current and force. The relationships between analysed signals and tool wear form a basis for the diagnosis system. Higher order polynomial regression functions with a limited number of terms have been developed and used to mimic drill wear development and monitoring parameters that follow this trend. Regression analysis solves the problem of how to save measuring data for a number of tools so as to follow the trend of the measuring signal; it also makes it possible to give a prognosis of the remaining lifetime of the drill. A simplified dynamic model has been developed to gain a better understanding of why certain monitoring methods work better than others. The simulation model also serves the testing of the developed automatic diagnostic method, which is based on the use of simplified fuzzy logic. The simplified fuzzy approach makes it possible to combine a number of measuring parameters and thus improves the reliability of diagnosis. In order to facilitate the handling of varying drilling conditions and work piece materials, the use of neural networks has been introduced in the developed approach. The scientific contribution of the thesis can be summarised as the development of an automatically adaptive diagnostic tool for drill wear detection. The new approach is based on the use of simplified fuzzy logic and higher order polynomial regression analysis, and it relies on monitoring methods that have been tested in this thesis. The diagnosis program does not require a lot of memory or processing power and consequently is capable of handling a great number of tools in a machining centre.reviewe

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

A tutorial

PM003/2016. Publisher Copyright: © 2022 The Author(s)The capabilities of bioanalytical mass spectrometry to (i) detect and differentiate viruses at the peptide level whilst maintaining high sample throughput and (ii) to provide diagnosis and prognosis for infected patients are presented as a tutorial in this work to aid analytical chemists and physicians to gain insights into the possibilities offered by current high-resolution mass spectrometry technology and bioinformatics. From (i) sampling to sample treatment; (ii) Matrix-Assisted Laser Desorption Ionization- to Electrospray Ionization -based mass spectrometry; and (iii) from clustering to peptide sequencing; a detailed step-by-step guide is provided and exemplified using SARS-CoV-2 Spike Y839 variant and the variant of concern SARS-CoV-2 Alpha (B.1.1.7 lineage), Influenza B, and Influenza A subtypes AH1N1pdm09 and AH3N2.publishersversionpublishe

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer-Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a competitive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which de- liver real benefits to patients by improving the diagnosis accuracy while reducing health care cost

How to dissect viral infections and their interplay with the host-proteome by immunoaffinity and mass spectrometry: A tutorial

The capabilities of bioanalytical mass spectrometry to (i) detect and differentiate viruses at the peptide level whilst maintaining high sample throughput and (ii) to provide diagnosis and prognosis for infected patients are presented as a tutorial in this work to aid analytical chemists and physicians to gain insights into the possibilities offered by current high-resolution mass spectrometry technology and bioinformatics. From (i) sampling to sample treatment; (ii) Matrix-Assisted Laser Desorption Ionization- to Electrospray Ionization -based mass spectrometry; and (iii) from clustering to peptide sequencing; a detailed step-by-step guide is provided and exemplified using SARS-CoV-2 Spike Y839 variant and the variant of concern SARS-CoV-2 Alpha (B.1.1.7 lineage), Influenza B, and Influenza A subtypes AH1N1pdm09 and AH3N2.Highlights: - Immunohistochemistry with magnetic core nanoparticles to isolate viruses. - The use of MALDI-MS for rapid virus detection is explained in detail; - The use of ESI-MS/MS to pinpoint host-patient crosstalk is explained in detail. - The absolute quantitative MS is explained for large-scale protein quantitation.This work received financial support from PT national funds (FCT/MCTES) through the projects UIDB/50006/2020 and UIDP/50006/2020 and from PROTEOMASS Scientific Society through the projects #PM001/2019 and #PM003/2016.info:eu-repo/semantics/publishedVersio

Signal Processing for NDE

Nowadays, testing and evaluating of industrial equipment using nondestructive tests, is a fundamental step in the manufacturing process. The complexity and high costs of manufacturing industrial components, require examinations in some way about the quality and reliability of the specimens. However, it should be noted, that in order to accurately perform the nondestructive test, in addition to theoretical knowledge, it is also essential to have the experience and carefulness, which requires special courses and experience with theoretical education. Therefore, in the traditional methods, which are based on manual testing techniques and the test results depend on the operator, there is the possibility of an invalid inference from the test data. In other words, the accuracy of conclusion from the obtained data is dependent on the skill and experience of the operator. Thus, using the signal processing techniques for nondestructive evaluation (NDE), it is possible to optimize the methods of nondestructive inspection, and in other words, to improve the overall system performance, in terms of reliability and system implementation costs. In recent years, intelligent signal processing techniques have had a significant impact on the progress of nondestructive assessment. In other words, by automating the processing of nondestructive data and signals, and using the artificial intelligence methods, it is possible to optimize nondestructive inspection methods. Hence, improve overall system performance in terms of reliability and Implementation costs of the system. This chapter reviews the issues of intelligent processing of nondestructive testing (NDT) signals

Ultrasonic Characterization of Cast Austenitic Stainless Steel Microstructure: Discrimination between Equiaxed- and Columnar-Grain Material – An Interim Study

Ultrasonic nondestructive evaluation (NDE) and inspection of cast austenitic stainless steel (CASS) components used in the nuclear power industry is neither as effective nor reliable as is needed due to detrimental effects upon the interrogating ultrasonic beam and interference from ultrasonic backscatter. The root cause is the coarse-grain microstructure inherent to this class of materials. Some ultrasonic techniques perform better for particular microstructural classifications and this has led to the hypothesis that an ultrasonic inspection can be optimized for a particular microstructural class, if a technique exists to reliably classify the microstructure for feedback to the inspection. This document summarizes scoping experiments of in-situ ultrasonic methods for classification and/or characterization of the material microstructures in CASS components from the outside surface of a pipe. The focus of this study was to evaluate ultrasonic methods and provide an interim report that documents results and technical progress. An initial set of experiments were performed to test the hypothesis that in-service characterization of cast austenitic stainless steel (CASS) is feasible, and that, if reliably performed, such data would provide real-time feedback to optimize in-service inspections in the field. With this objective in mind, measurements for the experiment were restricted to techniques that should be robust if carried forward to eventual field implementation. Two parameters were investigated for their ability to discriminate between different microstructures in CASS components. The first parameter was a time-of-flight ratio of a normal incidence shear wave to that of a normal incidence longitudinal wave (TOFRSL). The ratio removed dependency on component thickness which may not be accurately reported in the field. The second parameter was longitudinal wave attenuation. The selected CASS specimens provided five equiaxed-grain material samples and five columnar-grain material samples for a two-class discrimination problem. Qualitative TOFRSL estimates and a threshold algorithm classified all 10 material samples correctly and indicated a reliable and robust technique. Qualitative longitudinal wave attenuation estimates and a threshold algorithm also classified all 10 materials samples correctly; however, the technique was not as robust as TOFRSL. The experiments provided promising results and demonstrated that good potential exists for future development of techniques to implement real-time classification of CASS material. However, the reported measurements need to be substantiated with measurements on additional specimens