Editorials from EE Faculty

The newsletter section of the department of electrical engineering. Our newsletters are filled with educational information, events, news, faculty research and publications, students and alumni

The Optimization of Vibration Data Analysis for the Detection and Diagnosis of Incipient Faults in Roller Bearings

The rolling element bearing is a key component of many machines. Accurate and timely diagnosis of its faults is critical for proactive predictive maintenance. The research described in this thesis focuses on the development of techniques for detecting and diagnosing incipient bearing faults. These techniques are based on improved dynamic models and enhanced signal processing algorithms. Various common fault detection techniques for rolling element bearings are reviewed in this work and a detailed experimental investigation is described for several selected methods. Envelope analysis is widely used to obtain the bearing defect harmonics from the spectrum of the envelope of a vibration signal. This enables the detection and diagnosis of faults, and has shown good results in identifying incipient faults occurring on the different parts of a bearing. However, a critical step in implementing envelope analysis is to determine the frequency band that contains the signal component corresponding to the bearing fault (the one with highest signal to noise ratio). The choice of filter band is conventionally made via manual inspection of the spectrum to identify the resonant frequency where the largest change has occurred. In this work, a spectral kurtosis (SK) method is enhanced to enable determination of the optimum envelope analysis parameters, including the filter band and centre frequency, through a short time Fourier transform (STFT). The results show that the maximum amplitude of the kurtogram indicates the optimal parameters of band pass filter that allows both outer race and inner race faults to be determined from the optimised envelope spectrum. A performance evaluation is carried out on the kurtogram and the fast kurtogram, based on a simulated impact signal masked by different noise levels. This shows that as the signal to noise ratio (SNR) reaches as low as -5dB the STFT-based kurtogram is more effective at identifying periodic components due to bearing faults, and is less influenced by irregular noise pulses than the wavelet based fast kurtogram. A study of the accuracy of rolling-bearing diagnostic features in detecting bearing wear processes and monitoring fault sizes is presented for a range of radial clearances. Subsequently, a nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics. Simulation results indicate that the vibrations at fault characteristic frequencies exhibit significant variability for increasing clearances. An increased vibration level is detected at the bearing characteristic frequency for an outer race fault, whereas a decreased vibration level is found for an inner race fault. Outcomes of laboratory experiments on several bearing clearance grades, with different local defects, are used herein for model validation purposes. The experimental validation indicates that the envelope spectrum is not accurate enough to quantify the rolling element bearing fault severity adequately. To improve the results, a new method has been developed by combining a conventional bispectrum (CB) and modulation signal bispectrum (MSB) with envelope analysis. This suppresses the inevitable noise in the envelope signal, and hence provides more accurate diagnostic features. Both the simulation and the experimental results show that MSB extracts small changes from a faulty bearing more reliably, enabling more accurate and reliable fault severity diagnosis. Moreover, the vibration amplitudes at the fault characteristic frequencies exhibit significant changes with increasing clearance. However, the vibration amplitude tends to increase with the severity of an outer race fault and decrease with the severity of an inner race fault. It is therefore necessary to take these effects into account when diagnosing the size of a defect

A wavelet-fuzzy based algorithm for condition monitoring and fault detection of a voltage source inverter.

Abstract Not Provided

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

Initialization Requirement in Developing of Mobile Learning 'Molearn' for Biology Students Using Inquiry-based learning

Inquiry-based learning is kind of learning activities that involves students’ entire capabilities in exploring and investigating particular objects or phenomenon using critical thinking skills. Recently, information technology tangibly contributes in any education aspects, including the existence of e-learning, a widely spreading learning model in the 21st century education. This study aims at initializing needs of developing mobile learning ‘Molearn’ based on inquiry-based method. By cooperating with Biology teacher community in senior high school, ‘Molearn’ provides IT-based medium in Biology learning process

Aeronautical Engineering: A continuing bibliography with indexes (supplement 207)

This bibliography lists 484 reports, articles and other documents introduced into the NASA scientific and technical information system in November 1986

Development of a Portable and Easy-to-Use EEG System to be Employed in Emergency Situations

This thesis describes the development and evaluation of two portable devices intended for the recording of the electroencephalogram (EEG) in emergency situations. The topic originated from the EEG in Emergency Medicine (EEGEM) project, which seeks to develop the necessary technology and methodology that will help reduce the cost, the preparation time, and the overall complexity associated with EEG nowadays. The work contained herein builds upon the results obtained during previous Master theses that were completed in this project in order to obtain two systems that can be used in to investigate the feasibility and clinical value of EEG in emergency medicine (EM). Before starting the work, a thorough investigation of the EEG signal, which included its origins and its diagnostic potential, was carried out. Existing instrumentation was analyzed as well as factors that influence the quality of the recording. Since the EEG is an established diagnostic tool, it was necessary to follow existing recording guidelines. The recording guidelines of the American Clinical Neurophysiology Society (ACNS) were summarized and employed in the design stages of this study. A review of commercial EEG recorders and quick application EEG caps revealed the absence of an integrated solution for recording this signal in EM. Two systems were developed, one that is able to measure 1 channel of EEG while the other can measure six. The 1-channel system's particularity is that it allows a person's EEG to be displayed on a standard electrocardiogram (ECG) monitor. It features a high input impedance, low noise amplifier that increases the EEG signal's amplitude in order to allow it to be displayed on an ECG monitor. The amount of amplification is dynamically adjusted depending on the peak-to-peak amplitude of the EEG signal. After every gain change, the EEG recording is temporarily interrupted and a sinusoidal signal with an amplitude equivalent to 100 μV at the current gain level is outputted. The user interface is made up of a red, green and blue (RGB) light-emitting diode (LED) unit and a capacitive button that starts/stops the recording. The 6-channel system interfaces with a computer and consists of three parts: a wire-less EEG (WEEG) recording device, a quick-application cap, and recording software that runs on a computer. The WEEG device is able to measure 6 channels of EEG and tri-axial acceleration for the identification of movement artifacts. The recorded data is transmitted to a measurement computer by means of a 2.4 GHz wireless protocol. The author worked with the group from the Department of Automation Science and Engineering (ASE) that developed the previous versions of the device in order to reduce the size of the system and to improve its integration with the measurement computer. An initial prototype of a quick-application electrode cap for out-of-hospital measurements that can be performed by non-EEG specialists was designed by M.Sc. Salmi. It was made up of easily sterilizeable materials that were also elastic. Due to its many straps and adjustment points as well as the floating electrode leads, the band was not easy to apply. This study reports a simplified version of the cap that possesses only two attachment points and can be easily applied even with the patient in the supine position. Also, in the present version, the electrode leads are firmly attached to the cap. The past version of the recording software, which was developed by M.Sc. Pänkälä, had only basic functionality. It displayed the EEG signals, stored them, and allowed the WEEG device to be configured and patient information to be saved. Digital low-pass filtering of the displayed data, the ability to control the vertical sensitivity as well as the time scale, automatic uploading of the recorded file, and an implementation of the aEEG algorithm were added during this thesis. Also, information about the recording can now be stored together with the recorded signals. Furthermore, the software's us-ability was improved by means of a simple graphical user interface (GUI), which makes all functions easily accessible. During the evaluation of the two prototype systems, the electrical and software performance were ascertained. In the electrical tests, the operating time of the device, the common mode rejection (CMR), the frequency response, the noise level, and the signal to noise ratio (SNR) of the two systems were measured. In order to assess the reliability of the software of the two systems, both static and functional tests were conducted. The results obtained from the testing of the systems indicate that they offer similar performance to those offered by commercial EEG recording systems. This demonstrates that they can be used to investigate the clinical indications of EEG in EM. /Kir1