Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

International audienc

Data-Driven and Hybrid Methods for Naval Applications

The goal of this PhD thesis is to study, design and develop data analysis methods for naval applications. Data analysis is improving our ways to understand complex phenomena by profitably taking advantage of the information laying behind a collection of data. In fact, by adopting algorithms coming from the world of statistics and machine learning it is possible to extract valuable information, without requiring specific domain knowledge of the system generating the data. The application of such methods to marine contexts opens new research scenarios, since typical naval problems can now be solved with higher accuracy rates with respect to more classical techniques, based on the physical equations governing the naval system. During this study, some major naval problems have been addressed adopting state-of-the-art and novel data analysis techniques: condition-based maintenance, consisting in assets monitoring, maintenance planning, and real-time anomaly detection; energy and consumption monitoring, in order to reduce vessel consumption and gas emissions; system safety for maneuvering control and collision avoidance; components design, in order to detect possible defects at design stage. A review of the state-of-the-art of data analysis and machine learning techniques together with the preliminary results of the application of such methods to the aforementioned problems show a growing interest in these research topics and that effective data-driven solutions can be applied to the naval context. Moreover, for some applications, data-driven models have been used in conjunction with domain-dependent methods, modelling physical phenomena, in order to exploit both mechanistic knowledge of the system and available measurements. These hybrid methods are proved to provide more accurate and interpretable results with respect to both the pure physical or data-driven approaches taken singularly, thus showing that in the naval context it is possible to offer new valuable methodologies by either providing novel statistical methods or improving the state-of-the-art ones

Acta Universitatis Sapientiae - Electrical and Mechanical Engineering

Series Electrical and Mechanical Engineering publishes original papers and surveys in various fields of Electrical and Mechanical Engineering

Deep Learning based Prediction of Clogging Occurrences during Lignocellulosic Biomass Feeding in Screw Conveyors

Over the last decades, there have been substantial government and private sector investments to establish a commercial biorefining industry that uses lignocellulosic biomass as feedstock to produce fuels, chemicals, and other products. However, several biorefining plants experienced material conveyance problems due to the variability and complexity of the biomass feedstock. While the problems were reported in most conveyance unit operations in the biorefining plants, screw conveyors merit special attention because they are the most common conveyors used in biomass conveyance and typically function as the last conveyance unit connected to the conversion reactors. Thus, their operating status affects the plant production rate. Therefore, detecting emerging clogging events and, ultimately, proactively adjusting operating conditions to avoid downtime is crucial to improving overall plant economics. One promising solution is the development of sensor systems to detect clogging to support automated decision-making and process control. In this study, two deep learning based algorithms are developed to detect an imminent clogging event based on the current signature and vibration signals extracted from the sensors connected to the benchtop screw conveyor system. The study focuses on three biomass materials (switchgrass, loblolly pine, and hybrid poplar) and is designed around three research objectives. The first research objective examines the relationship between the occurrence of clogging in a screw conveyor and the current and vibration signals on the different feedstocks to establish the presence of clogging event fingerprint that could be exploited in automated decision-making and process-control. The second research objective applies two deep learning algorithms to the current and vibration signals to detect the imminent occurrence of clogging and its severity for decision making with an optimization procedure. The third objective examines the robustness of the optimized deep learning algorithm to detection imminent clogging events when feedstock properties (size distribution and moisture contents) vary. In the long-term, the early clogging detection methodology developed in this study could be leveraged to develop smart process controls for biomass conveyance

Fault Diagnosis of Gearbox Based Pitch Drives in Wind Turbines : Fault Detection by Support Vector Machine Using Motor Current Signal Analysis

Master's thesis in Renewable energy (ENE500)The growing dependence on wind power in recent years has increased the demand for reliantwind turbines. The pitch system of a wind turbine is one of the components with the highest failure rates. The most common way of diagnosing pitch system faults is currently through vibration analysis, which requires the installation of vibration sensors. This thesis presents a non-intrusive method for fault detection of the planetary gearbox in an electric wind tur-bine pitch system. The method is based on using the three-phase motor currents from the induction motor of the pitch system to calculate a DC offset using Extended Park’s vector approach (EPVA). Basic statistical formulas are used to extract features from both the time-and frequency-domain of the DC offset, where fast Fourier transform (FFT) is used to find the frequency-domain values. These features, along with the amplitudes of the characteristic frequencies of the planetary gearbox and its bearing, are used in the principal component analysis(PCA) to generate features that are used to train a support vector machine (SVM) classifier. This method is validated by using labeled data from the induction motor of a pitch system testbench to classify three health conditions. One of the health conditions are a healthy system, and the two other are artificially seeded faults in the system’s two-stage gearbox. These faults are a partially cracked tooth in one of the first stage planet gears, and an outer race fault inthe bearing at the input shaft. The results indicate that the proposed method is capable of classifying each of the three health conditions

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Automated On-line Fault Prognosis for Wind Turbine Monitoring using SCADA data

Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A Supervisory Control and Data Acquisition (SCADA) system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT’s health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several programmes have been made for that purpose; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. This thesis develops an automated on-line fault prognosis system for WT monitoring using SCADA data, concentrating particularly on WT pitch system, which is known to be fault significant. A number of preliminary activities were carried out in this research. They included building a dedicated server, developing a data visualisation tool, reviewing the existing WT monitoring techniques and investigating the possible AI techniques along with some examples detailing applications of how they can be utilised in this research. The a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (APK-ANFIS) was selected to research in further because it has been shown to be interpretable and allows domain knowledge to be incorporated. A fault prognosis system using APK-ANFIS based on four critical WT pitch system features is proposed. The proposed approach has been applied to the pitch data of two different designs of 26 Alstom and 22 Mitsubishi WTs, with two different types of SCADA system, demonstrating the adaptability of APK-ANFIS for application to variety of technologies. After that, the Alstom results were compared to a prior general alarm approach to show the advantage of prognostic horizon. In addition, both results are evaluated using Confusion Matrix analysis and a comparison study of the two tests to draw conclusions, demonstrating that the proposed approach is effective

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

Reconnaissance des défauts de la machine asynchrone : application des modèles d’intelligence artificielle

Les machines asynchrones sont omniprésentes dans les systèmes de production automatisé à cause de leur robustesse et leur facilitée de mise en oeuvre. Néanmoins, ces moteurs électriques concèdent tout de même des défauts (ex : court-circuit entre spires, barre rotoriques rompues) menant à des arrêts non planifiés. Par conséquent, les industries manufacturières investissent des ressources importantes afin de les éviter avec des programmes de maintenance qui sont partiellement inefficace. C’est dans ce contexte que, depuis plusieurs décennies, des chercheurs proposent des travaux permettant de diagnostiquer l’état des machines asynchrones. Cependant, les solutions ne donnent que très rarement la localisation et l’estimation du degré de sévérité des anomalies qui ne permet pas de prioriser les actions pour l’amélioration de la maintenance. De plus, la majorité des moyens de diagnostic ne sont pas adaptifs à d’autres gammes de moteur et les études ne prennent pas en compte la commande des machines asynchrones pour les applications à vitesse et couple variables. Ainsi, nous proposons dans cette thèse une nouvelle approche pour l’amélioration du processus de maintenance par la reconnaissance des défauts de la machine asynchrone reposant principalement sur l’exploitation des modèles d’intelligence artificielle. Celle-ci permettra de détecter, de localiser et d’estimer le degré de sévérité des anomalies du moteur grâce à ses courants statoriques. La solution donnée dans cet ouvrage est adaptif et surtout a été testé pour une machine possédant une commande et un asservissement de vitesse avec des différents profils de vitesse et couple variables. Pour ce faire, la recherche proposée exploite les modèles mathématiques de la machine asynchrone et de ses défauts afin de simuler les différents comportements de celle-ci. Les simulations serviront à créer des bases de données grâce à l’extraction de caractéristiques issue du traitement des signaux. Chacune des séries de données appartient à une catégorie décrivant le défaut du moteur. Par la suite, des algorithmes de classification permettront de reconnaître les anomalies de la machine asynchrone. Nous présentons également une approche hiérarchique qui améliore le taux de reconnaissance des défectuosités du moteur à induction. Ce projet se situant à la frontière des domaines du génie électrique, du génie informatique et des mathématiques constitue un défi complexe et formidable de recherche scientifique. Induction machines are omnipresent in production systems because of their sturdiness and their ease of implementation. Nevertheless, these electrical motors still concede failures (e.g. inter-turn short circuit, broken rotor bar), which may lead to unplanned shutdowns. Consequently, manufacturing industries invest significant resources to avoid them with maintenance, which is partially inefficient. In this context, some studies propose solutions to abnormal diagnostic conditions of the induction machine. Nevertheless, they rarely localize the defect and estimate the severity of the failure, which does not allow prioritizing action for the maintenance improvement. In addition, solutions are not adaptive for other motors, and studies do not include the control part very useful for speed and torque variable applications. Thus, in this thesis, we propose a new approach improving the maintenance process by the recognition of the induction machine failures. It relies mainly on Artificial Intelligence models and will allow to detect, localize and to estimate the degree of severity of the asynchronous motor faults thanks to the exploitation of current signals. The solution given in this project is adaptive and have been tested for induction machines operating with a speed and drives control. In addition, several speed and resistant torque profiles have been applied. To do this, the research proposed exploits the mathematical models of the induction machine operating under the healthy and faulty conditions. Simulations allow creating some datasets thanks to the feature extractions and the signals processing. Each vector of data belongs to a category describing the failure. Then, classification algorithms will recognize the induction machine defects. We also present a hierarchical approach, which improves the recognition rate. This project being a mix of electrical engineering, informatics and mathematic is a complex and amazing challenge of scientific research