Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

A Study of Iron-Nitrogen-Carbon Fuel Cell Catalysts: Chemistry – Nanostructure – Performance

Fuel cells have the potential to be a pollution-free, low-cost, and energy efficient alternative to the internal combustion engine for transportation and small-scale stationary power applications. The current state of fuel cell technology has already achieved two of these three lofty goals. The remaining barrier to wide-scale deployment is the high cost, which is primarily caused by dependence on large amounts of platinum to catalyze the energy conversion reactions. To overcome this barrier and facilitate the integration of fuel cells into mainstream applications, research into a new class of catalyst materials that do not require platinum is needed. There has been a significant amount of research effort directed toward the development of platinum-group metal free (PGM-free) catalysts, yet there is a lack of consensus on both the engineering parameters necessary to improve the technology and the fundamental science that would facilitate rational design. I have engaged in research on PGM-free catalysts based on inexpensive and abundant reagents, specifically: nicarbazin and iron. Catalysts made from these precursors have previously proven to be among the best PGM-free catalysts, but their continued advancement suffered from the same lack of understanding that besets all catalysts in this class. The work I have performed address both engineering concerns and fundamental underlying principles. I present results demonstrating correlations between physical structure, chemical speciation, and synthesis parameters, as well as addressing active site chemistry and likely locations. My research presented herein introduces new morphology analysis techniques and elucidates several key structure-to-property characteristics of catalysts derived from iron and nicarbazin. I discuss the development and application of a new length-scale specific surface analysis technique that allows for analysis of well-defined size ranges from a few nm to several microns. The existing technique of focused ion beam tomography is modified and optimized for platinum-group metal free catalyst layers, facilitating direct observation of catalyst integration into catalyst layers. I present evidence supporting the hypothesis that atomically dispersed iron coordinated with nitrogen are the dominant active sites in these catalysts. Further, that the concentration of surface oxides in the carbon structure, which can be directly influenced by synthesis parameters, correlates with both the concentration of active sites in the material and with fuel cell performance. Catalyst performance is hindered by the addition of carbon nanotubes and by the presence of metallic iron. Evidence consistent with the catalytic active sites residing in the graphitic plane is also presented

Liikeartefaktat elektrokardiografiassa

Movement of the patient during electrocardiograph (ECG) recording is a severe source of artifacts. Recent technical developments have enabled ECG recording without continuous supervision by experts. However, ECG recording outside of hospitals is prone to poor quality and movement artifacts. Therefore, it is important to study how and how much ECG recordings are affected by movement. Movement artifacts can hide signal components or mimic them, which causes false negative or false positive detections. Methods to manage movement artifacts include both computational and non-computational approaches. Computational approaches include, for example, adaptive filtering and machine learning methods. Additional variables that correlate with the artifact sources can be utilized in artifact recognition. For example, acceleration, impedance, and pressure signals have been studied as possible movement references. These additional signals are recorded by sensors that are placed on the ECG electrodes or on the patient’s body. In this thesis, the effect of movement artifacts is quantified using a simulation. The simulation makes use of open ECG databases. This study investigates how automated ECG analysis is affected by incremental increase in the movement artifact level. According to the results QRS detection statistics worsen with increased artifact levels. Capturing a movement reference for ECG is studied by experimental research. ECG and inertial measurement unit signals were recorded during different movements in order to analyze the creation of movement artifacts and movement reference signals. According to the results, placement of the movement reference signal sensor has a significant effect on the results. Different movements are captured better by different sensors and affect different ECG leads with different strengths.Potilaan liike sydänsähkökäyrämittauksen (EKG) aikana on merkittävä artefaktien lähde. Viimeaikainen teknologinen kehitys on mahdollistanut EKG-mittauksen ilman asiantuntijoiden jatkuvaa valvontaa. EKG-mittaukset sairaalaolosuhteiden ulkopuolella ovat kuitenkin erityisen alttiita huonolle signaalilaadulle ja liikeartefaktoille. Tämän vuoksi on tärkeää tutkia, miten ja kuinka paljon liike vaikuttaa EKG-mittauksiin. Liikeartefaktat voivat joko peittää tai jäljitellä EKG-signaalin eri osia, aiheuttaen vääriä negatiivisia tai vääriä positiivisia havaintoja. Liikeartefaktojen vaikutusta voidaan vähentää sekä laskennallisten että muiden menetelmien avulla. Laskennallisia menetelmiä ovat esimerkiksi adaptiivinen suodatus ja koneoppimismenetelmät. Artefaktojen lähteen kanssa korreloivia muuttujia mittaamalla voidaan edistää artefaktojen tunnistusta EKG-signaalista. Esimerkiksi kiihtyvyys-, impedanssi- ja painesignaalien käyttöä liikereferensseinä on tutkittu. Kyseisiä referenssisignaaleja voidaan mitata EKG-elektrodeihin tai potilaan kehoon kiinnitettävillä sensoreilla. Liikeartefaktojen vaikutuksen suuruutta tutkitaan tässä työssä simulaation avulla. Simulaatiossa hyödynnetään avoimia EKG-tietokantoja. Tutkimuksessa tarkastellaan sitä, miten vähittäinen liikeartefaktatason kasvu vaikuttaa automaattiseen EKG-analyysiin. Tulosten mukaan QRS-detektioon liittyvät tilastot huononevat artefaktatason kasvaessa. Liikereferenssin luomista tarkastellaan kokeellisen tutkimuksen avulla. EKG- ja inertiamittausyksikkö-signaaleja mitattiin erilaisten liikkeiden aikana, jotta voitaisiin havainnoida liikeartefaktojen ja liikesignaalin syntymistä. Tulosten mukaan liikereferenssiä mittaavan sensorin sijoituspaikalla on merkittävä vaikutus tuloksiin. Tietyt liikkeet saadaan paremmin mitattua eri tavoin sijoitettujen sensorien avulla. Lisäksi liikkeet vaikuttavat eri vahvuuksilla eri EKG-kytkentöihin

Fault Detection of Circulation Pumps on the Basis of Motor Current Evaluation

2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work. https://doi.org/10.1109/TIA.2021.3085697[EN] Motor current signature analysis (MCSA) for fault detection has found widespread application, especially for induction motors (IM). The basis of MCSA is the evaluation of a motor¿s current. This analysis is now also used for other motor types and can be used to detect faults of the coupled load. The purpose of this paper is to examine whether MCSA can be used to detect faults in a wet-rotor pump. A total of three faults are examined. The results show that, compared to a healthy pump, all faults could be detected. However, a detailed analysis of frequency components has to be carried out to differentiate the faults. A circulation pump with a maximum power consumption of 1.1 kW was used as the test item.This work was supported in part by the German Federal Ministry for Economic Affairs and Energy within the framework "Entwicklung optimierter Regelungen hydraulischer Systeme in der Gebaudetechnik zur Steigerung der Energieeffizienz von Heizungs-und Klimatisierungssystemen" under Grant 03ET1613B.Becker, V.; Schwamm, T.; Urschel, S.; Antonino-Daviu, J. (2021). Fault Detection of Circulation Pumps on the Basis of Motor Current Evaluation. IEEE Transactions on Industry Applications. 57(5):4617-4624. https://doi.org/10.1109/TIA.2021.30856974617462457

Exploring the optimal potential of transient reflection method through mel-frequency ceptrums coefficient and artificial neural network for leak detection and size estimation in water distribution systems

Water pipeline systems are critical infrastructures that provide potable water to communities. The design and operation of these systems are complex and require careful consideration of various factors, such as system reliability. Regular maintenance and inspection of pipelines and other components are necessary to prevent leaks and ensure that the system operates effectively. The efficient detection and accurate estimation of leaks in water distribution systems are crucial for maintaining the integrity and functionality of the infrastructure. This research aims to unleash the full potential of the transient reflection method through the integration of Mel-Frequency Cepstral Coefficients (MFCC) and Artificial Neural Network (ANN) techniques for leak detection and size estimation in water distribution systems. By leveraging the combined power of signal processing and machine learning, this study aim to advance the state-of-the-art methodologies for leak detection and size estimation, providing more accurate and efficient approaches based on transient reflection method. The objectives of this research are to explores the application of MFCC as a signal processing technique to extract vital information from the transient reflection signals. The transient reflection signals carry valuable insights into the characteristics of the water distribution system and can aid in identifying leaks. Furthermore to investigate and select significant features derived from the transient reflection signals that reflect the nature of leak size. Finally, is to develop and validate an ANN-based model for leak size estimation that harnesses the power of the extracted TRM features. To achieve these objectives, extensive experimentation and analysis will be conducted using transient reflection method obtained from laboratory scale water distribution systems. The data will be collected from various sizes of leaks. The collected dataset will serve as the foundation for training and validating the developed ANN model. Performance evaluation metrics, such as accuracy, precision, recall, and mean squared error, will be utilized to assess the effectiveness and reliability of the leak detection and size estimation technique. The expected outcomes of this research include advancements in leak detection and size estimation techniques in water distribution systems. The integration of MFCC and ANN techniques has the potential to significantly improve the accuracy and efficiency of leak detection, leading to timely identification and mitigation of leaks. The developed estimation model can aid in assessing the severity of leaks, enabling more effective allocation of resources for repair and maintenance activities. Ultimately, the findings of this research will contribute to the enhancement of water distribution system management, promoting water conservation and minimizing the adverse impacts of leaks on infrastructure and the environment. In conclusion, this research endeavors to unleash the full potential of the transient reflection method through the integration of MFCC and ANN techniques for leak detection and size estimation in water distribution systems. By leveraging signal processing and machine learning, this study aims to advance the state-of-the-art methodologies and provide more accurate and efficient approaches to address the challenges associated with leak detection and size estimation. The outcomes of this research have the potential to significantly benefit water management authorities, utilities, and researchers working in the field of water distribution system management and conservation

Channel characterisation and modelling for transcranial Doppler ultrasound.

The detection of micro-embolic signals (MES) is a mature application of transcranial Doppler (TCD) ultrasound. It involves the identification of abnormally highpitched signals within the arterial waveform as a method of diagnosis and prediction of embolic complications in stroke patients. More recently, algorithms have been developed to help characterise and classify MES using advanced signal processing techniques. These advances aim to improve our understanding of the causes of cereberovascular disease, helping to target the most appropriate interventions and quantifying the risk to patients of further stroke events. However, there are a number of limitations with current TCD systems which reduce their effectiveness. In particular, improvements in our understanding of the scattering effects in TCD ultrasound propagation channels will benefit our ability to develop algorithms that more robustly and reliably identify the consistency and material make-up of MES. This thesis explores TCD propagation channels in three related research areas. Firstly, a method of characterising TCD ultrasound propagation channels is proposed. Isotropic and non-isotropic three dimensional space (3-D) spherical scattering channel models are described in terms of theoretical reference models, simulation models, and sum of sinusoids (SoS) simulators, allowing the statistical properties to be analysed and reported. Secondly, a TCD ultrasound medical blood flow phantom is described. The phantom, designed to replicate blood flow in the middle cerebral arteries (MCA) for TCD ultrasound studies, is discussed in terms of material selection, physical construction and acoustic characteristics, including acoustic velocity, attenuation and backscatter coefficients. Finally, verification analysis is performed on the non-isotropic models against firstly, the blood flow phantom, and secondly, a patient recordings database. This analysis expands on areas of agreement and disagreement before assessing the usefulness of the models and describing their potential to improve signal processing approaches for detection of MES. The proposed non-isotropic channel reference model, simulation model, SoS simulator, and blood flow phantom are expected to contribute to improvements in the design, testing, and performance evaluation of future TCD ultrasound systems

Pipeline leak detection

In the present research two techniques are applied for leak detection in pipelines. The first method is a hardware-based technique which uses ultrasonic wave\u27s emission for pipeline inspection. Ultrasonic waves are propagated in the pipe walls and reflected signal from leakage will be used for pipe analysis. Several Pipes with various dimensions and characteristics are modeled by finite element method using ANSYS. Second order longitudinal modes of ultrasonic waves are emitted in their walls. For this purpose, excited frequency is calculated such that it excites the second order longitude mode. In order to investigate the behavior of emitted wave in contact with leakage, four sensors are used in outer surface of pipe. Waves are reflected when encountering leakage and the leak location is recognized knowing the wave emission speed and flight time of backscattered signals. Wavelet transform is used for processing these signals and recognizing leak location. This method is tested on several pipe models and it presents satisfactory results for short pipes. The second approach is a software-based method which works based on the transient model of the pipeline. In this method the outputs from simulated pipeline are compared to those measured from flow meters and if their difference goes beyond a threshold value, leak is detected. For leak localization a gradient pressure technique is applied which needs pressure slope measurements at inlet and outlet of the pipeline. Several cases with leak at various positions are studied. This method works well with high accuracy for long pipelines. --Abstract, page iii