bluuebunny/crossref_metadata_2025 huggingface dataset:
Il riconoscimento nel Regno Unito di un provvedimento italiano in materia di legittima (Inghilterra, Del Curto v Del Curto, 2023)
Authors: Marongiu Buonaiuti Fabrizio
Published: March 2024

Tesi
Il Foreign Judgments (Reciprocal Enforcement) Act 1933 si applica al riconoscimento di sentenze italiane nel Regno Unito sulla base di una convenzione bilaterale tra i due Paesi. La sentenza Del Curto v Del Curto affronta e risolve persuasivamente il problema della qualificazione dell’azione introdotta davanti al giudice italiano, con la quale l’attrice faceva valere la propria qualità di erede nei confronti degli altri coeredi, alla stregua di un’azione in personam anziché in rem, con conseguente affermazione del corretto fondamento della giurisdizione del giudice d’origine. Non rilevano invece, ai fini del riconoscimento in base all’Act in questione, profili attinenti al diritto applicabile, salvo il limite dell’ordine pubblico, che non era stato tuttavia invocato dalla parte resistente, dovendosi assumere che la previsione della legittima in base al diritto italiano non possa sollevare un problema di contrasto con l’ordine pubblico inglese.
The author’s view
The Foreign Judgments (Reciprocal Enforcement) Act 1933 applies to the recognition of Italian judgments in the UK thanks to an existing bilateral convention. Del Curto v Del Curto addresses persuasively the qualification of the Italian action, where the plaintiff claimed her quality of heir vis-à-vis the other co-heirs, as an action in personam rather than in rem, thereby establishing the proper foundation of the originating court’s jurisdiction. No relevance is instead given, for the purposes of recognition under the 1933 Act, to issues concerning the applicable law, save for cases of incompatibility with public policy. No objection was raised by the defendant in this respect. It must be assumed that Italian provisions relating to reserved shares of a deceased’s estate are not incompatible with English public policy.
Experimental Studies of Ageing in Electrolytic Capacitors
Authors: Kulkarni Chetan , Biswas Gautam , Koutsoukos Xenofon , Celaya Jose , Goebel Kai
Published: March 2024

Understanding the ageing mechanisms of electronic components critical avionics systems such as the GPS and INAV are of critical importance. Electrolytic capacitors and MOSFET’s have higher failure rates among the components of DC-DC power converter systems. Our current work focuses on analyzing and modeling electrolytic capacitor degradation and its effects on the output of DC-DC converter systems. The output degradation is typically measured by an increase in ESR (Equivalent Series Resistance) and decrease in the capacitance value over long periods of use even under nominal operating conditions. Typically the primary effect of degradation is increased ripple current and this has adverse effects on downstream components. For example, in avionics systems where the power supply drives a GPS unit, ripple currents can cause glitches in the GPS position and velocity output, and this may cause errors in the Inertial Navigation (INAV) system, causing the aircraft to fly off course. In this paper, we present the details of our ageing methodology along with details of experiments and analysis of the results.
Towards Modeling the Effects of Lightning Injection on Power MOSFETs
Authors: Saha Sankalita , R. Celaya Jose , Saha Bhaskar , Wysocki Phil , F. Goebel Kai
Published: March 2024

Power electronics are widely used in critical roles in modern day aircrafts and hence their health management is of great interest. An important part of prognostics and health management of these devices is understand- ing the effect of high-stress events such as lightning and how they affect their aging. In this paper we present our study and analysis of lightning injection experiments with power MOSFETs in their ON state. We show the different kind of damages that can be caused by such events and analyze their effects on device performance parameters. In addition, we present a simple yet effective modeling technique that can model the degradation in these devices. Such models will play a valuable role in understanding the behavior of these damaged devices when operated under normal conditions later and subsequently in prognosis of their remaining useful life.We present our results on the performance of this modeling and the scope within which they can be utilized for accurate estimation of device damage.
A hybrid-logic approach towards fault detection in complex cyber-physical systems
Authors: Srivastava Nisheeth , Srivastava Jaideep
Published: March 2024

Existing data mining approaches to complex systems anomaly detection use uni-variate and/or multi-variate statistical hypothesis testing to assign anomaly scores to data streams associated with system components. The former approach assumes statistical independence of individual components, while the latter assumes substantial global systemic correlation. As a compromise between these two epistemological extremes, we present a data-mining approach hybridizing existing statistical techniques with theorem-proving methods to create a novel algorithm for anomaly detection, diagnosis and control in complex systems. Our algorithm takes sensor inputs from physical sensors providing system subcomponent performance data and outputs (i) a global systemic risk indicator and (ii) possible diagnosis hypotheses. We present results on three different systems, and in comparison with current state-of-the-art fault detection algorithms to demonstrate the viability of our approach. We ﬁnd that our algorithm proves robust towards increased data dimensionality in contrast with existing clustering-based fault detection methods and can also detect contextual faults that are undetectable using existing statistical techniques.
Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform
Authors: Daigle Matthew , Goebel Kai
Published: March 2024

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy
Gearbox Vibration Source Separation by Integration of Time Synchronous Averaged Signals
Authors: Zhang Guicai , Isom Joshua
Published: March 2024

This paper describes a simple approach for integrating all the time synchronous average (TSA) signals from multiple shafts of a gearbox to generate a composite time synchronous average which can be subtracted from the original signal to generate a second-order cyclostationary residual. This approach is compared with other techniques including an all-shaft TSA over the least common multiple of shaft rotation periods, high-pass filtering, and self-adaptive noise cancellation (SANC). The results demonstrate that the proposed approach produces an integrated TSA signal that includes only the shaft components, gear mesh components and the sidebands associated with all the shafts, while the residual contains the random vibration components and noise. The results produced by three alternative techniques do not separate the components as well or have a lower signal-to-noise ratio.
Health Assessment of Composite Structures in Unconstrained Environments Using Partially Supervised Pattern Recognition Tools
Authors: Ramasso Emmanuel , Placet Vincent , Gouriveau Rafael , Boubakar Lamine , Zerhouni Noureddine
Published: March 2024

The health assessment of composite structures from acoustic emission data is generally tackled by the use of clustering techniques. In this paper, the K-means clustering and the newly proposed Partially-Hidden Markov Model (PHMM) are exploited to analyse the data collected during mechanical tests on composite structures. The health assessment considered in this paper is made difficult by working in unconstrained environments. The presence of the noise is illustrated in several examples and is shown to distort strongly the results of clustering. A solution is proposed to filter out the noisy partition provided by the clustering methods. After filtering, the PHMM provides results which appeared closer to the expectations than the K-means. The PHMM offers the possibility to use uncertain and imprecise labels on the possible states, and thus covers supervised and unsupervised learning as special cases which makes it suitable for real applications.
Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications
Authors: B. Armstrong Jeffrey , L. Simon Donald
Published: March 2024

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of “health” parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.
Analysis of data quality issues in real-world industrial data
Authors: Hubauer Thomas , Lamparter Steffen , Roshchin Mikhail , Solomakhina Nina , Watson Stuart
Published: March 2024

In large industries usage of advanced technological methods and modern equipment comes with the problem of storing, interpreting and analyzing huge amount of information. Handling information becomes more complicated and important at the same time. So, data quality is one of major challenges considering a rapid growth of information, fragmentation of information systems, incorrect data formatting and other issues. The aim of this paper is to describe industrial data processing and analytics on the real- world use case. The most crucial data quality issues are described, examined and classified in terms of Data Quality Dimensions. Factual industrial information supports and illustrates each encountered data deficiency. In addition, we describe methods for elimination data quality issues and data analysis techniques, which are applied after cleaning data procedure. In addition, an approach to address data quality problems in large-scale industrial datasets is proposed. This techniques and methods comprise several well-known techniques, which come from both worlds of mathematical logic and also statistics, improving data quality procedure and cleaning results.
A Novel Computational Methodology for Uncertainty Quantification in Prognostics Using The Most Probable Point Concept
Authors: Sankararaman Shankar , Goebel Kai
Published: March 2024

This paper develops a novel computational approach to quantify the uncertainty in prognostics in the context of condition- based monitoring. Prognostics consists of two major steps; first, it is necessary to estimate the state of health at any time instant, and then, it is required to predict the remaining useful life of the engineering component/system of interest. While the topic of estimation has been addressed through different types of Bayesian tracking techniques, this paper primarily focuses on the second aspect of future prediction and remain- ing useful life computation, which is influenced by several sources of uncertainty. Therefore, it is important to identify these sources of uncertainty, and quantify their combined ef- fect on the remaining useful life prediction. The computation of uncertainty in remaining useful life can be treated as an uncertainty propagation problem which can be solved using probabilistic techniques. This paper investigates the use of the Most Probable Point approach (which was originally developed to estimate the failure probability of structural systems) for calculating the probability distribution of the remaining useful life prediction. The proposed methodology is illustrated using a battery which is used to power an un- manned aerial vehicle.
Sensor Fault Diagnosis in Quadrotors Using Nonlinear Adaptive Estimators
Authors: C Avram Remus , Zhang Xiaodong , Campbell Jacob
Published: March 2024

Unmanned Aerial Vehicles (UAVs) have attracted significant attentions in recent years due to their potentials in various military and civilian applications. Small UAVs are often e- quipped with low-cost and lightweight micro-electro-mechanical systems (MEMS) inertial measurement units including 3-axis gyro, accelerometer and magnetometer. The measurements provided by gyros and accelerometers often suffer from bias and excessive noise as a result of temperature variations, vibration, etc. This paper presents a sensor fault diagnostic method for quadrotor UAVs. Specifically, we consider the faults in the gyro and accelerometer. A model-based sensor fault detection and isolation (FDI) estimation method is presented. The proposed FDI method adopts the idea that accelerometer and gyroscopic measurements coincide with the translational and rotational forces represented in the UAV dynamics. Thus, the faults in accelerometer and gyroscope can be represented as virtual actuator faults in the quadrotor state equations. Two diagnostic estimators are designed to provide structured FDI residuals allowing simultaneous detection and isolation of gyroscope and accelerometer sensor bias. In addition, nonlinear adaptive estimators are designed to provide an estimate of the unknown sensor bias. The parameter convergence property of the adaptive estimation scheme is an- alyzed. Simulation studies utilizing a nonlinear quadrotor UAV model are used to illustrate the effectiveness of the proposed method.
A Generalized Machine Fault Detection Method Using Unified Change Detection
Authors: Wang Wenyi , Forrester David , Frith Peter
Published: March 2024

Many different techniques have been developed for detecting faults in rotating machinery. This is because different fault types typically require different techniques for the effective detection of the fault. However, for many new or unknown fault types, we have found that the existing detection techniques are either incapable or ineffective and that we therefore need to come up with brand new methods after the fault event. This can significantly constrain the usefulness and effectiveness of Prognostic Health Management (PHM) systems. In this paper we attempt to look at detecting global changes in the synchronously averaged signals as the machine’s health status progresses from healthy to faulty, and to define one unified signal processing technique and its associated condition indicators for the detection of changes caused by various types of faults in rotating machinery. The proposed method is conceptually very simple, and its effectiveness is demonstrated using vibration data from machines with several different types of faults. The results have shown that this single unified change detection approach can be very effective in detecting and trending changes caused by many different types of machine faults.
A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data
Authors: Liao Linxia , Minhas Raj , Rangarajan Arvind , Kurtoglu Tolga , De Kleer Johan
Published: March 2024

We propose a framework of self-aware machines based on data collected using the MTConnect protocol. Beyond exist- ing applications of OEE (Overall Equipment Effectiveness) reporting, the proposed framework integrates multiple sources of information for work-piece and machine condition monitoring, and equipment time to failure prediction in manufacturing processes, and provides feedback to shop supervisor. Firstly, we propose a method to predict component wear and failure based on operational data. ICP (Interactive Closest Point) algorithm is used to find the best matching tool path given a certain tool number to identify similar machining pro- cesses. The result of ICP tool path matching, together with other parameters such as spindle speed, feed rate and tool number, are used to adaptively cluster the machining pro- cesses. For each process cluster, a particle filter based prognostic algorithm is used to predict tool wear and/or spindle bearing failure. Secondly, we propose to use anomaly detection methods to detect changes in normal behavior of the machines. Various machine learning algorithms are utilized to detect anomalies based on real-time data, and a voting mechanism is used to decide when to trigger an alarm. Thirdly, the axes traverse is aggregated to provide a measure of the wear on various axes in the machine, which is correlated to errors in position comparing to the commanded positions and nominal tool paths. Spindle load verse rotating speed is also examined to facilitate shop floor scheduling to avoid damage caused by unintentionally excessive machine usage. The proposed framework has been demonstrated using published data from two Mazak machine tools.
Are Current Prognostic Performance Evaluation Practices Sufficient and Meaningful?
Authors: Sankararaman Shankar , Saxena Abhinav , Goebel Kai
Published: March 2024

This paper investigates the shortcomings of performance evaluation for prognostic algorithms, particularly in the presence of uncertainty. To that end, the various elements of a prognostic algorithm (present health state estimation, future load condition, degradation model, and damage threshold) and their effects on prognostics are examined. Each of these elements contribute to overall prediction performance and therefore it is important to distinguish between (1) assessment of the correctness of information regarding these quantities, and (2) the assessment of correctness of the prognostic algorithm. The need for proper accounting for uncertainty in the various associated elements is discussed. Next, the shortcomings of traditional comparisons between ground truth and algorithm prediction is discussed. Several scenarios are pointed out where misleading interpretations about evaluation out-comes are possible. In order to address these shortcomings an “informed evaluation” methodology is being proposed, where the algorithm is informed with future loading/operating conditions before comparing against ground truth. Additionally, the importance of estimating the accuracy of aggregating the different sources of uncertainty using rigorous mathematical procedures is also emphasized. While this discussion does not target developing new metrics, it highlights key criteria for an accurate performance evaluation process under uncertainty and proposes new measures to accomplish this goal.
Comparison and ensemble of temperature-based and vibration-based methods for machinery prognostics
Authors: Kuria Kimotho James , Sextro Walter
Published: March 2024

This paper presents a comparison of a number of prognos- tic methods with regard to algorithm complexity and perfor- mance based on prognostic metrics. This information serves as a guide for selection and design of prognostic systems for real-time condition monitoring of technical systems. The methods are evaluated on ability to estimate the remaining useful life of rolling element bearing. Run-to failure vibration and temperature data is used in the analysis. The sampled prognostic methods include wear-temperature correlation method, health state estimation using temperature measurement, a multi-model particle filter approach with state equation parameter adaptation utilizing temperature measurements, prognostics through health state estimation and mapping extracted features to the remaining useful life through regression approach. Although the performance of the methods utilizing the vibration measurements is much better than the methods using temperature measurements, the methods using temperature measurements are quite promising in terms of reducing the overall cost of the condition monitoring system as well as the computational time. An ensemble of the presented methods through weighted average is also introduced. The results show that the methods are able to estimate the remaining useful life within error bounds of ±15%, which can be further reduced to ±5% with the ensemble approach.
Fault diagnostics and evaluation in cryogenic loading system using optimization algorithm
Authors: Ponizovskaya-Devine Ekaterina , G Luchinsky Dmitry , Kodali Anu , Khasin Michael , Timucin Dogan , Sass Jarred , Perotti Jose , Brown Barbara
Published: March 2024

Physics-based approach to the cryogenic flow health management is presented. It is based on fast and time-accurate physics models of the cryogenic flow in the transfer line. We discuss main features of one of these models – the homogeneous moving front model and presents results of its validation. The main steps of the approach including fault detection, identification, and evaluation are discussed. A few examples of faults are presented. It is shown that dynamic features of the faults naturally form a number of ambiguity groups. A D-matrix approach to optimized identification of these faults is briefly outlined. An example of discerning and evaluating faults within one ambiguity group using optimization algorithm is considered in more details. An application of this approach to the Integrated Health Management of cryogenic loading is discussed.
Distributed Adaptive Fault-Tolerant Consensus Control of Multi-Agent Systems with Actuator Faults
Authors: Khalili Mohsen , Zhang Xiaodong , Cao Yongcan , A. Muse Jonathan
Published: March 2024

This paper presents an adaptive fault-tolerant control (FTC) scheme for leader-follower consensus control of uncertain mobile agents with actuator faults. A local FTC component is designed for each agent in the distributed system by using local measurements and certain information exchanged between neighboring agents. Each local FTC component consists of a fault detection module and a reconfigurable controller module comprised of a baseline controller and an adaptive fault-tolerant controller activated after fault detection. Under certain assumptions, the closed-loop system stability and leader-follower consensus properties of the distributed system are rigorously established. A simulation example is used to illustrate the effectiveness of the FTC method.
A Self-Organizing Map-Based Monitoring System for Insulated Gate Bipolar Transistors Operating in Fully Electric Vehicle
Authors: Rigamonti Marco , Baraldi Piero , Zio Enrico , Alessi Allegra , Astigarraga Daniel , Galarza Ainhoa
Published: March 2024

Insulated Gate Bipolar Transistors (IGBTs) are one of the most used power semiconductor devices for energy conversion applications, due to their high performance. In this work we have developed a monitoring system for IGBTs installed in Fully Electric Vehicles (FEVs), which are operating under very variable working conditions. The monitoring system is based on a Self-Organizing Map (SOM), trained considering data collected from healthy IGBTs. An indicator of the IGBT degradation is defined as the distance between the measured SOM input vector, i.e., the signal measured on the monitored IGBT, and its SOM Best Matching Unit (BMU) representative of an healthy IGBT in similar working conditions. Then, a method based on the definition of a utility function for the identification of the threshold value to be used for the classification of the IGBT degradation state is proposed. The approach is verified with respect to experimental data collected from an inverter connected to an electric motor, and is shown able to identify the IGBTs degradation state regardless of the actual operating condition.
Neural Network-Based Gear Failure Prediction in a Brushless DC Actuation System
Authors: Chen Yan , Lucas Christina , Lee Jay , Buehner Michael
Published: March 2024

Due to its inherent efficiency and reliability, brushless DC (BLDC) driven actuation systems are widely used in a variety of industries such as aerospace, electric transportation and industrial positioning. However, it is inevitable that various types of faults can develop in the actuator either from the BLDC motor or geared positioning systems. This paper, focusing on actuator load positioning system failures, proposes a data-driven based failure prediction method. Run-to-failure data is first collected from test-beds of specific BLDC actuation systems and then critical features representing system performance are extracted. There are also dynamic behavioral tests used, which are designed to provide discrete measurements reflecting system health conditions. Ultimately, based on optimized mapping between the two groups of information, a general neural network model is developed to establish a nonlinear trajectory model for failure progression. The model also allows for prediction of gear failure without the interruption of performing dynamic behavioral tests during continuous working condition. This approach provides for real time monitoring of system behavior as well as possibility of the predicting the Remaining Useful Life (RUL) of the actuation system. Although many efforts have been done to predict gear wear based on vibration signal, the proposed method is formulated within a "sensor-less" environment and makes full use of existing on-board sensing information, which provides the possibility of a closed-loop control system for life management.
Comparison of Model-based Vs. Data-driven Methods for Fault Detection and Isolation in Engine Idle Speed Control System
Authors: Yang Ruochen , Rizzoni Giorgio
Published: March 2024

An internal combustion engine operating at idle is regulated by a feedback controller so that it runs at a preset idle speed without stalling when no acceleration is requested from the driver. Idle speed control is affected by numerous disturbances, ranging from accessory loads to environmental conditions. Because of the regulating behavior of the controller, faults, especially actuator faults, may affect sensor measurements in a way very similar to disturbances, system uncertainty or noise. This poses a challenge to the fault detection and isolation (FDI) problem for this system. In this paper, two fundamentally different fault diagnoses approaches are used to detect and isolate faults. A model-based residual generation scheme as well as a data-driven linear discriminant analysis scheme is developed to solve the FDI problem even when faults are concurring in addition to system uncertainty, disturbance and noise. Their performances are compared side by side using data gathered from an experimentally validated simulator for an engine idle system that considers an actuator fault, a sensor fault, several system uncertainties and disturbance (operating conditions), and sensor noise. The results show that comparable performance can be achieved with both schemes and some comments are made about each approach.
A Modelling Ecosystem for Prognostics
Authors: Astfalck Lachlan , Hodkiewicz Melinda , Keating Adrian , Cripps Edward , Pecht Michael
Published: March 2024

This paper evaluates data-driven asset prognostic models from a modelling ecosystem perspective, which includes data description, uncertainty quantification, model selection justification and validation, and application limitations. An easily accessible and comprehensive ecosystem enables efficient reproducibility of previous work to facilitate both the adoption of the models by industry and the development of future scientific methods. The results of this study enable the development of a list of ecosystem elements to accompany the publication of new models. By describing the ecosystem in the communication of new models, researchers can ensure the reproducibility of their models in the wider prognostic community.
Using Deep Learning Based Approaches for Bearing Remaining Useful Life Prediction
Authors: Deutsch Jason , He David
Published: March 2024

Traditional data driven prognostics requires establishing explicit model equations and much prior knowledge about signal processing techniques and prognostic expertise, and therefore is limited in the age of big data. This paper presents a deep learning based approach for bearing remaining useful life (RUL) prediction with big data. This approach has the ability to automatically extract important features that can be used for RUL predictions. The presented approach is tested and validated using data collected from bearing run-to-failure tests and compared with existing PHM methods. The test results show the promising bearing RUL prediction performance of the deep learning based approach.
Deriving Prognostic Continuous Time Bayesian Networks from Fault Trees
Authors: Perreault Logan , Thornton Monica , Sheppard John W.
Published: March 2024

Probabilistic graphical models have been applied successfully to a number of Prognostic and Health Management (PHM) applications. Continuous time Bayesian networks (CTBNs) are one such model, and they are capable of representing discrete systems that evolve in continuous time. In this work, we propose a method for constructing a CTBN from a fault tree, a model often used for evaluating system reliability. Additionally, we provide a method for reducing the number of required CTBN parameters by pruning unnecessary portions of the fault tree. Furthermore, we take advantage of the information encoded in the remaining gates of the tree and make use of the Noisy-OR model, offering additional reductionsin the number of parameters needed to specify the CTBN model. We show how a CTBN derived from a fault tree can be combined with a CTBN derived from a D-matrix to form a unified model. This allows for a description of faults and effects that evolve in continuous time based on test outcomes. We demonstrate the derivation and parameterization processes using a running example, and show how the resulting model can be queried to obtain information about the state of the system over time.
Fault Detection By Segment Evaluation Based On Inferential Statistics For Asset Monitoring
Authors: Atamuradov Vepa , Medjaher Kamal , Lamoureux Benjamin , Dersin Pierre , Zerhouni Noureddine
Published: March 2024

Detection of unexpected events (e.g. anomalies and faults) from monitoring data is very challenging in machine health assessment. Hence, abrupt or incipient fault detection from the monitoring data is very crucial to increase asset safety, availability and reliability. This paper presents a generic methodology for abrupt and incipient fault detection and feature fusion for health assessment of complex systems. Proposed methodology consists of feature extraction, feature fusion, segmentation and fault detection steps. First of all, different features are extracted using descriptive statistics. Secondly, based on linearly weighted data fusion algorithm, extracted features are combined to get the generic and representative feature. Afterward, combined feature is divided into homogeneous segments by sliding window segmentation algorithm. Finally, each segment is further evaluated by coefficient of variability which is used in inferential statistics, to evaluate health state changes that indicate asset faults. To illustrate its effectiveness, the methodology is implemented on point machine and Li-ion battery monitoring data to detect abrupt and incipient faults. The results show that proposed methodology can be effectively used in fault detection for asset monitoring.
Data driven modeling and estimation of accumulated damage in mining vehicles using on-board sensors
Authors: Jakobsson Erik , Frisk Erik , Pettersson Robert , Krysander Mattias
Published: March 2024

The life and condition of a MT65 mine truck frame is to a large extent related to how the machine is used. Damage from different stress cycles in the frame are accumulated over time, and measurements throughout the life of the machine are needed to monitor the condition. This results in high demands on the durability of sensors used. To make a monitoring system cheap and robust enough for a mining application, a small number of robust sensors are preferred rather than a multitude of local sensors such as strain gauges. The main question to be answered is whether a low number of robust on-board sensors can give the required information to recreate stress signals at various locations of the frame. Also the choice of sensors among many different locations and kinds are considered. A final question is whether the data could also be used to estimate road condition. By using accelerometer, gyroscope and strain gauge data from field tests of an Atlas Copco MT65 mine truck, coherence and Lassoregression were evaluated as means to select which signals to use. ARX-models for stress estimation were created using the same data. By simulating stress signals using the models, rain flow counting and damage accumulation calculations were performed. The results showed that a low number of on-board sensors like accelerometers and gyroscopes could give enough information to recreate some of the stress signalsmeasured. Together with a linear model, the estimated stress was accurate enough to evaluate the accumulated fatigue damage in a mining truck. The accumulated damage was also used to estimate the condition of the road on which the truck was traveling. To make a useful road monitoring system some more work is required, in particular regarding how vehicle speed influences damage accumulation.
Gear Fault Diagnostics Using Extended Phase Space Topology
Authors: Mohamad T. Haj , Nataraj C.
Published: March 2024

This paper applies a novel feature extraction method called Extended Phase Space Topology (EPST) in order to diagnose various faults in a gear-train system. The EPST method, that our research team has been developing, is based on characterizing the vibration data using the topology of phase space, computing its density distribution and then expanded in a series of orthogonal functions. The resulting coefficients are subsequently used in a machine learning algorithm. For this study, multiple test gears with different health conditions (such as a healthy gear and defective gears with root crack on one tooth, multiple cracks on five teeth and missing tooth) are studied. The vibration data of a gear-train is measured by a triaxial accelerometer installed on the test. Results indicate that EPST is efficient in diagnosing the status of the health of the gear system and characterizing the dynamic behavior. Moreover, the EPST procedure does not require a priori knowledge about the dynamics of the system. EPST needs no noise reduction, signal prepossessing, feature ranking or selection, and therefore can easily be applied in a relatively automated process.
Lithium-ion Battery Remaining Useful Life Prediction with Long Short-term Memory Recurrent Neural Network
Authors: Liu Yuefeng , Zhao Guangquan , Peng Xiyuan , Hu Cong
Published: March 2024

Lithium-ion batteries play critical roles in many electronic devices. It is necessary to develop a reliable and accurate remaining useful life (RUL) prediction approach to provide timely maintenance or replacement of battery systems. A novel RUL prediction approach based on Long Short-term Memory (LSTM) Recurrent Neural Network (RNN) is proposed in this paper. LSTM is able to capture long-term dependencies and model sequential data among the capacity degradation of lithium-ion batteries. The advantages of our proposed method include: 1) obtaining high prediction accuracy without accurate physics-based model or expertise and 2) decreasing the cumulation errors by multi-step ahead prediction each time, while traditional RUL method predicts one-step ahead once and then uses the current estimated value to predict next one, which causes cumulation errors increased. The Center for Advanced Life Cycle Engineering (CALCE) battery datasets are used to demonstrate the effectiveness of the proposed method. The results show that, compared with echo state networks (ESN), the proposed method has higher accuracy, more stable and reliable performance for lithium-ion batteries RUL prediction.
Impact Damage Prediction for Wave Energy Converters
Authors: Meekins Ryan , Adams Stephen , Farinholt Kevin , Hipwell Nathan , Desrosiers Michael , Beling Peter
Published: March 2024

Marine and hydrokinetic energy is of growing interest across the globe because it has the potential to provide a large source of renewable energy from the world’s oceans and rivers. These marine and hydrokinetic devices, such as wave energy converters, must operate remotely in all weather conditions, including severe storms. Thus, these devices can suffer from structural damage affecting their performance and lifespan. Therefore, there is interest in developing structural health monitoring systems that can identify new damage, estimate its severity, and then make a decision to provide crews with a maintenance or control recommendation. In this study, we investigate using the electromechanical impedance response of piezoelectric transducers to actively monitor the structural health of composite materials similar to those used in several marine and hydrokinetic devices. Recurring impact damage experiments were completed on five plates using a test drop stand, consisting of five consecutive impacts at the same location for each plate. Classification and regression methods were evaluated in an attempt to predict impact damage on a new plate. Machine learning algorithms were used on data collected over a frequency range of 10 kHz to 100 kHz for two types of piezoelectric transducers.
Actuator Fault-Detection for Autonomous Underwater Vehicles Using Unsupervised Learning
Authors: Kemp Matt , Raanan Ben
Published: March 2024

Many Autonomous Underwater Vehicles (AUV) have high rates of false-alarms because their health management relies on user-generated rules. The false-alarm rate could be substantially smaller if fault-detection were based on actual actuator performance instead of heuristics. We collectedperformance data on a critical AUV actuator, a mass-shifter, and from the data developed an unsupervised fault detector. We found that a small number of features were sufficient to detect known and novel faults with a high probability of detection and a low false alarm rate. We also found that npoint false-alarm reduction schemes performed poorly due to correlation during startup.
Uncertainty in Prognostics and Health Management: An Overview
Authors: Sankararaman Shankar , Goebel Kai
Published: March 2024

This paper presents an overview of various aspects of uncertainty quantification in prognostics and health management. Since prognostics deals with predicting the future behavior of engineering systems and it is almost practically impossible to precisely predict future events, it is necessary to account for the different sources of uncertainty that affect prognostics, and develop a systematic framework for uncertainty quantification and management in this context. Researchers have developed computational methods for prognostics, both in the context of testing-based health management and conditionbased health management. However, one important issue is that, the interpretation of uncertainty for these two different types of situations is completely different. While both the frequentist (based on the presence of true variability) and Bayesian (based on subjective assessment) approaches are applicable in the context of testing-based health management, only the Bayesian approach is applicable in the context of condition-based health management. This paper explains that the computation of the remaining useful life ismoremeaningful in the context of condition-based monitoring and needs to be approached as an uncertainty propagation problem. Numerical examples are presented to illustrate the various concepts discussed in the paper.
Data Acquisition and Signal Analysis from Measured Motor Currents for Defect Detection in Electromechanical Drive Systems
Authors: Lessmeier Christian , Enge-Rosenblatt Olaf , Bayer Christian , Zimmer Detmar
Published: March 2024

This paper presents the development of a diagnostic method which uses the measurement of motor currents in order to detect defects in electromechanical systems. It focusses on two main topics: the acquisition of experimental data, and the development of the diagnostic method. The data acquisition was crucial for the successful development of a dedicated signal analysis method. For this purpose, a test rig for generating experimental training data was created. The rig provides the ability to simulate a wide range of defects experimentally. Different types of artificial defects, such as bearing damage or misalignments, were used; these are described in detail in the second section of the paper. The experimental data was obtained under varying operational conditions. Using all possible settings of operational parameters for data generation would mean excessive experimental time and effort. Therefore, a special approach using the theory of “Design of Experiments” was applied. By using a fractional factorial design based on orthogonal arrays, the number of experiments could be reduced significantly. Details of this approach are given in the third section. The main ideas of the classification algorithm, including some of the results, are summarized in the fourth section. A special method using a combination of Principal Component Analysis and Linear Discriminant Analysis was designed for the correct detection of damage or misalignments. With this method, a successful classification of the systems’ health state could be obtained.
Model-based predictive maintenance techniques applied to automotive industry
Authors: Longo Nicola , Serpi Valeria , Jacazio Giovanni , Sorli Massimo
Published: March 2024

In automotive industry, and generally in the mass production world, maintenance is a very critical issue requiring special attention since every single stop causes a huge loss in term of item produced due to very small cycle time.
Basing on this observations, in the last years, a lot of efforts has been put in failure prevention and condition based maintenance; as an example in Fiat Chrysler Automobiles (FCA) the WCM (World Class Manufacturing) became part of its culture and the area dedicated to Professional Maintenance makes possible many step forwards. The ways WCM reaches the zero breakdown are Time Based Maintenance (TBM) and Condition Based Maintenance (CBM) but further improvements can be reached  with focus on cost reduction and by optimizing the component usage without arriving to a fault.
In this paper, after an overview of maintenance techniques adopted in FCA plants worldwide, a model-based approach is suggested for a COMAU hemming tool named RHEvo. After the development of a simplified model, we try to estimate the actual status of internal components making use of Neural Network.
Focusing on the internal springs, the aging affects the elastic coefficient because of fatigue phenomena. As will be shown, under certain assumptions the cracks presence affects the nominal elastic coefficient; therefore, starting from the estimation coming from the Neural Network, it is possible to model an equivalent crack length. Finally, basing on stochastic crack growth model proposed by Yang and Manning an estimation of internal spring’s Remaining Useful Life Estimation (RULE) is calculated.
Hybrid Model for on-line Detection of Gas Turbine Lean Blowout Events
Authors: Iannitelli Matteo , Allegorico Carmine , Garau Francesco , Capanni Marco
Published: March 2024

Modern dry low NOx combustors can target very low emissions levels by operating at a lean air/gas ratio. However, ultra-lean combustion is extremely susceptible to thermoacoustic combustion instabilities and Lean Blowout (LBO), which can lead to large pressure oscillations in the combustor and decreased durability of components.
Conventional on-board diagnostics embedded in the Unit Control Panel (UCP) of a Gas Turbine, continuously check the health status of the combustion section at a high scan rate and raise alarms when abnormal conditions occur. While ensuring protection and control, UCP control logics may not provide precise indications on the nature of the issue and further troubleshooting, also using specific tools, is typically required.
In a changing environment where Industrial Internet of Things (IIoT) is offering a chance to drive productivity and growth, online Monitoring and Diagnostic (M&D) software and services on connected units are becoming strategic to increase asset availability and reliability, as well as reducing maintenance costs.
In this paper, we present a hybrid analytic, which combines physics-based and data-driven models, for the detection of Lean Blowout conditions on Gas Turbines equipped with Dry Low NOx multi-can combustion system. Regarding the data-driven model, we face a problem of classification and exploit dimensionality reduction to reduce the number of variables under consideration. During the development, different techniques are tested and benchmarked.
The analytic is trained on real LBO events and finally is deployed in a production environment to process incoming on-line data acquired from monitored units. Obtained results are presented.