Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Advances in power quality analysis techniques for electrical machines and drives: a review

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.Peer ReviewedPostprint (published version

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

Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods

Although electrochemical noise (EN) has been studied for decades, the optimal approach for the analysis of EN data remains uncertain. This research innovatively combined the use of recurrence quantification analysis of electrochemical noise data and machine learning methods to develop models for corrosion monitoring and corrosion type identification. Case studies demonstrate that the proposed methodologies are potentially feasible for the development of online corrosion monitoring programs

Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms

The importance of Predictive Maintenance is critical for engineering industries, such as manufacturing, aerospace and energy. Unexpected failures cause unpredictable downtime, which can be disruptive and high costs due to reduced productivity. This forces industries to ensure the reliability of their equip-ment. In order to increase the reliability of equipment, maintenance actions, such as repairs, replacements, equipment updates, and corrective actions are employed. These actions affect the flexibility, quality of operation and manu-facturing time. It is therefore essential to plan maintenance before failure occurs.Traditional maintenance techniques rely on checks conducted routinely based on running hours of the machine. The drawback of this approach is that maintenance is sometimes performed before it is required. Therefore, conducting maintenance based on the actual condition of the equipment is the optimal solu-tion. This requires collecting real-time data on the condition of the equipment, using sensors (to detect events and send information to computer processor).Predictive Maintenance uses these types of techniques or analytics to inform about the current, and future state of the equipment. In the last decade, with the introduction of the Internet of Things (IoT), Machine Learning (ML), cloud computing and Big Data Analytics, manufacturing industry has moved forward towards implementing Predictive Maintenance, resulting in increased uptime and quality control, optimisation of maintenance routes, improved worker safety and greater productivity.The present thesis describes a novel computational strategy of Predictive Maintenance (fault diagnosis and fault prognosis) with ML and Deep Learning applications for an FG304 series external gear pump, also known as a domino pump. In the absence of a comprehensive set of experimental data, synthetic data generation techniques are implemented for Predictive Maintenance by perturbing the frequency content of time series generated using High-Fidelity computational techniques. In addition, various types of feature extraction methods considered to extract most discriminatory informations from the data. For fault diagnosis, three types of ML classification algorithms are employed, namely Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB) algorithms. For prognosis, ML regression algorithms, such as MLP and SVM, are utilised. Although significant work has been reported by previous authors, it remains difficult to optimise the choice of hyper-parameters (important parameters whose value is used to control the learning process) for each specific ML algorithm. For instance, the type of SVM kernel function or the selection of the MLP activation function and the optimum number of hidden layers (and neurons).It is widely understood that the reliability of ML algorithms is strongly depen-dent upon the existence of a sufficiently large quantity of high-quality training data. In the present thesis, due to the unavailability of experimental data, a novel high-fidelity in-silico dataset is generated via a Computational Fluid Dynamic (CFD) model, which has been used for the training of the underlying ML metamodel. In addition, a large number of scenarios are recreated, ranging from healthy to faulty ones (e.g. clogging, radial gap variations, axial gap variations, viscosity variations, speed variations). Furthermore, the high-fidelity dataset is re-enacted by using degradation functions to predict the remaining useful life (fault prognosis) of an external gear pump.The thesis explores and compares the performance of MLP, SVM and NB algo-rithms for fault diagnosis and MLP and SVM for fault prognosis. In order to enable fast training and reliable testing of the MLP algorithm, some predefined network architectures, like 2n neurons per hidden layer, are used to speed up the identification of the precise number of neurons (shown to be useful when the sample data set is sufficiently large). Finally, a series of benchmark tests are presented, enabling to conclude that for fault diagnosis, the use of wavelet features and a MLP algorithm can provide the best accuracy, and the MLP al-gorithm provides the best prediction results for fault prognosis. In addition, benchmark examples are simulated to demonstrate the mesh convergence for the CFD model whereas, quantification analysis and noise influence on training data are performed for ML algorithms

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

Characterization Of Somatosensation In The Brainstem And The Development Of A Sensory Neuroprosthesis

Innovations in neuroprosthetics have restored sensorimotor function to paralysis patients and amputees. However, to date there is a lack of solutions available to adequately address the needs of spinal cord injury patients (SCI). In this dissertation we develop a novel sensor-brain interface (SBI) that delivers electric microstimulation to the cuneate nucleus (CN) to restore somatosensory feedback in patients with intact limbs. In Chapter II, we develop a fully passive liquid metal antenna using gallium-indium (GaIn) alloy injected in polydimethylsiloxane (PDM) channels to measure forces within the physiological sensitivity of a human fingertip. In Chapter III, we present the first chronic neural interface with the CN in primates to provide access to long-term unit recordings and stimulation. In Chapter IV, we demonstrate that microstimulation to the CN is detectable in a Three Alternative Force Choice Oddity task in awake behaving primates. In Chapter V, we explore the downstream effects of CN stimulation on primary somatosensory cortex, in the context of spontaneous and evoked spindles under sedation. In summary, these findings constitute a proof-of-concept for the sensory half of a bidirectional sensorimotor prosthesis in the CN

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required