bluuebunny/crossref_metadata_2025 huggingface dataset:
Factoring Dynamic Bayesian Networks using Possible Conﬂicts
Authors: J. Alonso-Gonzalez Carlos , Moya Noemi , Biswas Gautam
Published: March 2024

Dynamic Bayesian Networks (DBNs) are temporal probabilistic graphical models that represent in a very compact way dynamic systems. They have been used for model based diagnosis of complex systems because they naturally cope with uncertainties in the diagnosis process, particularly sensor uncertainty in noisy environments. A caveat of DBN is the complexity of the inference procedure which is usually performed with Particle Filtering algorithms. Recently, factoring has been proposed to decompose a DBN into subsystems, distributing the diagnosis process and reducing the computational burden. This paper proposes decomposing a system with Possible Conﬂicts (PCs) and, afterwards, building a DBN factor from each resultant PC. The method can be systematically applied to a state space representation of a dynamic system to obtain minimal observable subsystems with analytical redundancy. Assuming single fault hypothesis and known fault modes, the method allows performing consistency based fault detection, isolation and identiﬁcation with the unifying formalism of DBN. The three tank system benchmark has been used to illustrate the approach. Two fault scenarios are discussed and a comparison of the behaviors of a DBN of the complete system with the DBN factors is also included.
Diagnosis as Planning Revisited: An Abridged Report
Authors: Sohrabi Shirin , A. Baier Jorge , A. McIlraith Sheila
Published: March 2024

In the spirit of past contributions to the formal characterization of diagnosis (e.g., (Reiter, 1987; de Kleer et al., 1992)) this paper presents a formal characterization of diagnosis of discrete dynamical systems, appealing to the situation calculus. It then proceeds to establish a correspondence between computing dynamical diagnoses and generating plans. It is this correspondence that we feel may be of particular interest to the DX community. Planning technology provides tailored representations and fast, efficient algorithms for automated plan generation. This paper shows how such technology can be brought to bear on the problem of generating diagnoses. Initial experiments support our claim that planning technology holds great promise for efficient generation of diagnoses.
Integrated Robust Fault Detection, Diagnosis and Reconfigurable Control System with Actuator Saturation
Authors: Fan Jinhua , Zhang Youmin , Zheng Zhiqiang
Published: March 2024

An integrated fault detection, diagnosis and reconfigurable control design method is studied in this paper with explicit consideration of control input constraints. The actuator fault to be treated is modeled as a control effectiveness loss, which is diagnosed by an adaptive algorithm. For fault detection, an observer is designed to generate the output residual and a minimum threshold is set by an H∞ index. To design the reconfigurable controller, an auxiliary matrix is introduced and a linear parameter varying (LPV) system is constructed by convex combination. Linear matrix inequality (LMI) conditions are presented to compute the design parameters of controllers and related performance index. The system performances are measured by the ellipsoidal sets regarding the domain of attraction and disturbance rejection respectively. For illustration, the proposed design techniques are applied to the flight control of a flying wing aircraft under large effectiveness loss of actuators.
A new method of bearing fault diagnostics in complex rotating machines using multi-sensor mixtured hidden Markov models
Authors: Chen Z. S. , Yang Y. M. , Hu Z. , X. Ge Z.
Published: March 2024

Vibration signals from complex rotating machines are often non-Gaussian and non-stationary, so it is difficult to accurately detect faults of a bearing inside using a single sensor. This paper introduces a new bearing fault diagnostics scheme in complex rotating machines using multi-sensor mixtured hidden Markov model (MSMHMM) of vibration signals. Vibration signals of each sensor will be considered as the mixture of nonGaussian sources, which can depict non-Gaussian observation sequences well. Then its parameter learning procedure is given in detail based on EM algorithm. In the end the new method was tested with experimental data collected from a helicopter gearbox and the results are very exciting.
Damage Identification in Frame Structures, Using Damage Index, Based on H2-Norm
Authors: Saffari Mahdi , Sedaghati Ramin , Stiharu Ion
Published: March 2024

A simulation method to detect and locate damage in frame structures by defining a damage index is proposed. Structural members are Timoshenko beam type. The method defines a damage index which is the reduction percentage of H2 norm of the structure at certain locations in both healthy and damaged states. Structure modeling is done by finite element method.
Enhanced Multivariate Based Approach for SHM Using Hilbert Transform
Authors: Hajrya Rafik , Mechbal Nazih , Vergé Michel
Published: March 2024

In structural health monitoring, features extraction from measured data plays an important role. In order to enhance information about damage, we propose in this paper, a new damage detection methodology, based on the Hilbert transform and multivariate analysis. Using measurements given by distributed sensors of a smart composite structure, we apply the Hilbert transform to calculate an envelope matrix. This matrix is then treated using multivariate analysis. The subspaces associated to the envelope matrix are used to define a damage index (DI). Furthermore, from perturbation theory of matrices, we propose a bound associated to this DI, by inspecting this bound, decision on the health of the structure is generated. Experimentation on an actual composite smart structure will show the effectiveness of the proposed approach.
Physics based Prognostics of Solder Joints in Avionics
Authors: Banerjee Avisekh , K Koul Ashok , Kumar Amar , Goel Nishith
Published: March 2024

Applicability of a physics based prognostics approach for solder joints using microstructural damage models is investigated. A modified deformation mechanism map for the solder alloys is introduced where grain boundary sliding (GBS) plays a dominant role during creep deformation. The high homologous temperature of solder as well as the combined thermal-vibration cycling experienced during typical operating missions necessitates the use of a combined creep-fatigue failure approach. In this work, a PCB consisting of a heat generating chip with Ball-Grid Array (BGA) solder joints is considered for avionics application. A prognostics based Life Cycle Management approach was used to perform the mission analysis, FEA, thermal-mechanical stress analysis and damage accumulation analysis. The remaining useful life (RUL) is predicted for different rupture strains. The uniqueness of this approach lies in the use of microstructure based damage models and consideration of both material and mission variability to predict the RUL under actual usage. The life critical nodes were observed near the junction of the solder joints with the substrate due to high disparities in their coefficients of thermal expansion. In addition, the probabilistic analysis was also performed by randomly varying the grain size and fitting a two-parameter Weibull distribution to the failure data. The model calibration and the results show some practical trends that need to be verified through future experimentation. The simulation results demonstrate the viability of using a physics-based approach for the prognosis of solder joint failures in avionics.
Commercialization of Prognostics Systems Leveraging Commercial Off- The-Shelf Instrumentation, Analysis, and Data Base Technologies
Authors: Johnson Preston
Published: March 2024

There are many facets of a prognostics and health management system. Facets include data collection systems that monitor machine parameters; signal processing facilities that sort, analyze and extract features from collected data; pattern matching algorithms that work to identify machine degradation modes; database systems that organize, trend, compare and report information; communications that synchronize prognostic system information with business functions including plant operations; and finally visualization features that allow interested personnel the ability to view data, reports, and information from within the intranet or across the internet.A prognostic system includes all of these facets, with details of each varying to match specific needs of specific machinery. To profitably commercialize a prognostic system, a generic yet flexible framework is adopted which allows customization of individual facets. Customization of one facet does not materially impact another. This paper describes the framework, and provides case studies of successful implementation.
Cost Comparison of Maintenance Policies
Authors: Minh Duc Le , Cher Ming Tan
Published: March 2024

Maintenance is crucial to all repairable engineering systems as they will degrade and fail. The cost of maintenance for a manufacturing plant can occupy up to 30% of the total operating cost. If maintenance is not scheduled properly, unexpected equipment failure can induce significant cost due to reduced productivity and sub-standard products produced, both of which may result in customer penalty. Various maintenance policies have been proposed in the past. Among the various policies, age-dependent and periodic maintenances are the common policies employed in industries. Recently, predictive maintenance or condition based maintenance policies are also proposed owing to the advancement in the sensor technology. In this work, we compare the age-dependent and periodic maintenance policies as well as the predictive maintenance policies from the perspective of cost using Markov multi-state maintenance modeling and Monte Carlo simulation. To be realistic, imperfect maintenance is included, and both the sequential and continuous inspections are considered and compared.
Experiments with Neural Networks as Prognostics Engines for Patient Physiological System Health Management
Authors: K.Ghavami Peter , C. Kapur Kailash
Published: March 2024

Prognostics and prediction of patients‟ short term physiological health status is of critical importance in medicine because it affords medical interventions that prevent escalating medical complications. Accurate prediction of the patients‟ health status offers many benefits including faster recovery, lower medical costs and better clinical outcomes. This study proposes a prognostics engine to predict patient physiological status. The prognostics engine builds models from historical clinical data using neural network as its computational kernel. This study compared accuracy of various neural network models. Given the diversity of clinical data and disease conditions, no single model is ideal for all medical cases. Certain algorithms are more accurate than others depending on the type, amount and diversity of possible outcomes. Thus multiple neural network algorithms are necessary to build a generalizable prognostics engine. The study proposes using an oracle, an overseer program to select the most accurate predictive model that is most suited for a particular medical prediction among several neural network options.
Identification of Correlated Damage Parameters under Noise and Bias Using Bayesian Inference
Authors: An Dawn , Choi Joo-Ho , H. Kim Nam
Published: March 2024

This paper present a statistical model parameter identification using Bayesian inference when parameters are correlated and observed data have noise and bias. The method is explained using the Paris model that describes crack growth in a plate under mode I loading. It is assumed the observed data are obtained through structural health monitoring systems, which may have random noise and deterministic bias. It was found that strong correlation exists (a) between two model parameters of the Paris model, and (b) between initially measured crack size and bias. As the level of noise increases, the Bayesian inference was not able to identify the correlated parameters. However, the remaining useful life was predicted accurately because the identification errors in correlated parameters were compensated by each other.
Failure Prognostics of a Hydraulic Pump Using Kalman Filter
Authors: Paulo P. Gomes João , P. Leão Bruno , O. L. Vianna Wlamir , K. H. Galvão Roberto , Yoneyama Takashi
Published: March 2024

Hydraulic systems are an important power source in modern aircraft. Most aircraft employ hydraulic power for flight control systems and landing gears actuation. Pumps are a critical component in hydraulic system and monitoring the health of such components may provide economic and operational benefits to aircraft operators. This work describes the use of Kalman Filter techniques for the estimation of remaining useful life of aircraft hydraulic pumps. An empirical model of degradation evolution is employed for this purpose. Low sampling rate measurements of the hydraulic pressure of the aircraft hydraulic systems are the only measurements employed. In order to illustrate and validate the method, two time series of actual run to failure data are analyzed. Results provide evidence that the method can be successfully employed for actual aircraft hydraulic pump failure prognosis.
Fleet Wide Asset Monitoring: Sensory Data to Signal Processing to Prognostics
Authors: Johnson Preston
Published: March 2024

Next generation fleet wide asset monitoring solutions are incorporating machine failure prediction and prognostics technologies. These technologies build on signal processing of vibration time waveforms, process parameters, and operating conditions of the machine. For prognostics algorithms to work well, the signal processing algorithms need to be applied correctly and the results need to be reliable. This paper provides a survey of signal processing techniques as applied to specific machine component with a focus on the output and use with prognostics technologies. With properly organized outputs, prognostics algorithms transform the fleet condition and health management challenge into a deployable fleet health management solution. To arrive at the deployable fleet management solution, a systematic approach in the design of the prognostics system is preferable. This approach includes data and model driven failure patterns, sensory data connectivity from deployed assets, prognostics analytical applications, and advisory generation outputs which guide the asset owners and maintainers.
Initial Condition Monitoring Experience on a Wind Turbine
Authors: Bechhoefer Eric , Wadham-Gagnon Mathew , Boucher Bruno
Published: March 2024

The initial installation of a condition monitoring system (CMS) on a utility scale wind turbine produced a number of unexpected results. The CMS was installed on the TechnoCentre éolien Repower MM92. The installation allowed testing of a MEMS (microelecctromechanical system) based sensor technology and allowed and in-depth analysis of vibration data and revolutions per minute (RPM) data. A large 3/revolution effect, due to tower shadow and wind shear, required the development of an enhanced time synchronous average algorithm. The ability to easily measured changes in main rotor RPM, as a result of tower shadow and wind shear phenomenology, may also facilitate the detection of icing or blade pitch error.
Integrated Vehicle Health Management and Unmanned Aviation
Authors: Heaton Andrew , Fan Ip-Shing , Lawson Craig , McFeat Jim
Published: March 2024

Over the past decade the use of unmanned aerial systems (UAS) has increased in military, intelligence, and surveillance operations for dull, dirty and dangerous (DDD) missions. They have primarily been used at a time of war, and been pushed into service by programmes designed increase the capabilities of military organisations, with little thought of supportability or interaction with other air users. The increased use of UAS in war time and the ensuing media coverage has naturally led proposed use of UAS in a civilian context, being used for a wide range of non-militaryDDD missions: from land usage and crop monitoring; to the monitoring of nuclear power plants; to inspection of power lines.But when considering the use of UAS this civilian context brings up important issues, such as: How can UAS be integrated into civil unsegregated airspace, and how will they react to other air traffic (manned and unmanned)? How can UAS be shown to be safe to get general public, especial with an increased level of autonomy? What technologies are needed to ensure the safe use of UAS? Is using a UAS more economical than using the manned equivalent? The list goes on.Steps have already been taken to answer these issues, in the United Kingdom the Civil Aviation Authority (CAA) have produced guidance (CAP 722) for manufactures and operators of UAS, to allow them to build and operate UAS whilst developing a framework to fully integrate them into the civil airspace. In addition the CAA is working closely with industry-led consortium ASTRAEA (Autonomous Systems Technology Related Airborne Evaluation & Assessment) to help solve the issues of using UAS in civil airspace, and with the United States Federal Aviation Administration (FAA) having been set a 2015 deadline for full integration, hastening the need for solutions to be found. The poster will take a holistic systems engineering view of the current situation in unmanned aviation and where Integrated Vehicle Health Management (IVHM) might be used (in whole or in part) to solve some of the issues mentioned above.It will present reasons why you may wish to include IVHM in a UAS (e.g. cost, size, safety), the potential benefits (e.g. increased availability, reduced maintenance costs) and pitfalls (e.g. false positives) of implementing an IVHM on a UAS. It will also map how the IVHM (system of interest) interacts with the rest of the UAS (wider system of interest), how it relates to the issues the aviation industry have with UAS (environment) and the general public (wider environment).
Simulation Framework and Certification Guidance for Condition Monitoring and Prognostic Health Management
Authors: Buderath Matthias , Adhikari Partha Pratim
Published: March 2024

The most prominent challenges to the successful qualification of Integrated System Health Monitoring (ISHM) systems are appropriate technology development processes and Verification & Validation (V&V) methods towards certification. This paper outlines a survey of recent ISHM programs in diverse industrial sectors across the globe, offers guideline towards ISHM development at each Technology Readiness Level (TRL), and sets forth a V&V process and certification roadmap. This paper provides insight into Cassidian’s ISHM Simulation framework and emphasizes the relevance of this framework to an effective V&V solution of ISHM.
Autonomous Prognostics and Health Management (APHM)
Authors: Stecki Jacek , Cross Joshua , Stecki Chris , Lucas Andrew
Published: March 2024

The objective of this paper is to show how PHM con-cepts can be included in the design of an autonomous Un-manned Air Vehicle (UAV) and in doing so, provide effec-tive diagnostic/prognostic capabilities during system opera-tion. The authors propose a PHM Cycle that is divided into two parts, covering the design of the Autonomous PHM system and the operation of the PHM system in real-time application. The paper presents steps in design of Autono-mous Prognostics and Health Management (APHM) devel-oped using the above approach, to provide contingency management integrated with autonomous decision-making for power management on a UAV. APHM was developed using commercial software tools such as the JACK® auton-omous software platform to provide real-time intelligent decision making and MADe® - Maintenance Aware Design environment to identify risks due to equipment failures and to select appropriate sensor coverage. The PHM Cycle methodology is demonstrated in application to autonomous, real-time, engine health and power management on an Un-manned Air Vehicle (UAV).
Application of Microwave Sensing to Blade Health Monitoring
Authors: Kwapisz David , Hafner Michaël , Rajamani Ravi
Published: March 2024

This paper discusses the application of microwave sensing to turbine airfoil health monitoring. The proposed microwave system operates at 6- and 24-GHz and is applicable to both blade tip-clearance and blade tip-timing measurements. One of the main advantages of microwave systems, compared to other technology such as capacitive or eddy current, is that it can be installed for long term operations in the harsh environment of the first turbine stages. The monitoring of blade tip-timing and tip-clearance pattern is useful for detecting abnormal blade behavior due to structural damage. Such a sensing system can also be used in actively maintaining optimal blade-to-casing clearance, thereby enhancing turbine efficiency. This paper presents blade tip-clearance pattern monitoring based on microwave measurements. First, a laboratory study shows the ability of the system to consistently measure tip clearance pattern. Then tip clearance pattern measurements from a real engine test are presented. While this paper presents results from system testing on tip clearance, it is expected that this study will be carried forward in the next phase to demonstrate tip-timing measurement and further, to show how such as system can form the basis for a more comprehensive health management system.
Study on Condition Based Maintenance Using On-Line Monitoring and Prognostics Suitable to a Research Reactor
Authors: Bae Sanghoon , Cha Hanju , Suh Youngsuk
Published: March 2024

The purpose of this paper is to look into a more effective way for how condition based maintenance using on-line monitoring and prognostics can be applied to the components/systems in the field of a research reactor, which has been demanded to upgrade or modify the existing MMIS. The requirements of the contemporary diagnostics and prognostics herein are briefly introduced and then an assessment of the actual application to a research reactor is reviewed.
Identification and evaluation of the potentials of Prognostics and Health Management in future civil aircraft
Authors: Torhorst Sebastian , Hölzel Nico B. , Gollnick Volker
Published: March 2024

After the stepwise implementation of health management systems in form of diagnostic on-board maintenance sys-tems in the latest generation of aircraft (e.g. AiRTHM (Air-bus Real-Time Health Monitoring) – Airbus, AIMS (Air-plane Information Management System) – Boeing, AHEAD (Aircraft Health Analysis and Diagnosis) – Embraer) and other technical equipment such as jet engines (Engine Con-dition Monitoring – MTU, Performance Based Logistics – GE) or trains (Remote Condition Monitoring – Future Rail-way), the pressure is high for an evolution of this technolo-gy. Integrated Vehicle Health Management (IVHM) repre-sents a set of capabilities that enable sustainable and safe operation of components and subsystems within aerospace platforms. [Rajamani, 2013]. The next step in IVHM is the ability to give prognoses on the Remaining Useful Life (RUL) of a system or component and the structure of the aircraft. This approach is covered in the term “Prognostics and Health Management” (PHM). PHM in this context consists of Integrated Systems Health Management (ISHM) and Structural Health Monitoring (SHM). To put that step into practice in an industrial environment, it is inevitable to weigh up costs vs. benefits in a Cost-Benefit Analysis (CBA). This trade-off is subject of the following investiga-tion. A methodology is presented with which it is possible to evaluate PHM on aircraft level and examples are given to show its applicability. The study shows that, under the as-sumptions made, a PHM system can benefit the design and operation of future civil aircraft. The dimensioning of struc-tures can be modified, maintenance processes adjusted, system reliability, aircraft availability and safety increased. With the help of the results presented herein and further in-depth studies of the aircraft structures/systems of interest, a sufficiently well-founded evaluation of the possible costs and benefits of the implementation of this advanced ap-proach on the PHM technology can be performed.
Sequential Monte Carlo sampling for crack growth prediction providing for several uncertainties
Authors: Corbetta Matteo , Sbarufatti Claudio , Manes Andrea , Giglio Marco
Published: March 2024

The problem of fatigue crack growth monitoring and residual lifetime prediction is faced by means of sequential Monte Carlo methods commonly defined as sequential importance sampling/resampling or particle filtering techniques. The algorithm purpose is the estimation of the fatigue crack evolution in metallic structures, considering uncertainties coming from phenomenological aspects and material properties affecting the process. These multiple uncertainties become a series of unknown parameters within the framework of the dynamic state-space model describing the crack propagation. These parameters, if correctly estimated within the particle filtering algorithm, will cover the uncertainties coming from the real environment, improving the prognostic performances. The standard particle filter formulation needs additional methods to augment the state vector and to correctly estimate the parameters. The prognostic system composed by the sequential Monte Carlo algorithm able to account for different uncertainties is tested through several crack growth simulations. The applicability of the method to real structures and the employment in presence of real environmental conditions (i.e. variable loading conditions) is also discussed at the end of the paper.
Dynamic Weighted PSVR-Based Ensembles for Prognostics of Nuclear Components
Authors: Liu Jie , Vitelli Valeria , Seraoui Redouane , Zio Enrico
Published: March 2024

Combining different physical and / or statistical predictive algorithms for Nuclear Power Plant (NPP) components into an ensemble can improve the robustness and accuracy of the prediction. In this paper, an ensemble approach is proposed for prediction of time series data based on a modified Probabilistic Support Vector Regression (PSVR) algorithm. We propose a modified Radial Basis Function (RBF) as kernel function to tackle time series data and two strategies to build diverse sub-models of the ensemble. A simple but effective strategy is used to combine the results from sub-models built with PSVR, giving the ensemble prediction results. A real case study on a power production component is presented.
A generic ageing model for prognosis - Application to Permanent Magnet Synchronous Machines
Authors: Vinson Garance , Ribot Pauline , Combacau Michel
Published: March 2024

In the context of more electrical aircrafts, Permanent Magnet Synchronous Machines are used in a more and more aggressive environment. It becomes necessary to supervise their health state and to predict their future evolution and remaining useful life in order to anticipate any requested maintenance operation. Model-based prognosis is a solution to this issue. Any prognosis method must rely on knowledge about the system ageing. A review of existing ageing laws is presented. The generic ageing model proposed in (Vinson, Ribot, Prado, & Combacau, 2013) is extended in this paper. It allows representing the ageing of any equipment and the impact of this ageing on its environment. The model includes the possible retroaction of the system health state to itself through stress increase in case of damage. The proposed ageing model is then illustrated with Permanent Magnet Synchronous Machines (PMSM). Two critical faults are characterized and modeled : inter-turns short-circuits and rotor demagnetization. Stator and rotor ageing are well represented by the proposed ageing model. The prognosis method developed in (Vinson et al., 2013) is extended to consider this new generic ageing model. In order to test the prognosis algorithm, ageing data are needed Since no real measurements are available, a virtual prototype of PMSM is developed. It is a realistic model which allows running a fictive but realistic scenario of stator ageing. The scenario comprises apparition and progression of an inter-turns short-circuit and its impact on stator temperature, which value has an impact on the ageing speed. The prognosis method is applied successfully to the PMSM during this scenario and allows estimating the RemainingUseful Life (RUL) of the stator and the machine.
Motor current signature analysis for gearbox health monitoring: Experiment, signal analysis and classification
Authors: Bravo-Imaz Iñaki , García-Arribas Alfredo , Ferreiro Susana , Fernandez Santiago , Arnaiz Aitor
Published: March 2024

Preventing downtimes in machinery operation is becoming fundamental in industrial standards. The most common strategy to avoid costly production stoppages is the preventive maintenance, combining it with reactive maintenance in detected malfunctions. Condition-based maintenance can reduce costs, and help maintaining the quality of the produced goods. Gearboxes, as crucial elements in industrial machinery, are conventionally monitored using accelerometers, which are expensive and can be hard to install in place to provide useful information.Motor current signature analysis overcomes these inconveniences. This analysis technique provides a non-intrusive method, and it is based on readily available signals. Changes in the input voltages are related with variations of the speed and/or load of the electric motor. The health state of the gearbox can be examined through an exhaustive analysis of the input currents.A gear prognosis simulator (GPS) test bench has been used to perform an extensive experimentation campaign. This test bench is particularly convenient due to the flexibility it provides. Different sets of sensors can be placed in different positions, and multiple combinations of speeds and loads can be established. Three damage categories in the gears have been analyzed, high damage, moderate damage and little damage. The test parameters have been selected to simulate the working conditions of electromechanical actuators and machine tools. Constant speed and transient tests have been performed. In the transient tests, fast speed changes are performed to produce acceleration, to investigate the concomitant changes produced in the signal. The analysis has been performed in both the time and the frequency domain, and complementarily, using the wavelet decomposition. The results obtained allow discerning the different type of defects on the gears, thus allowing detecting the different fault conditions and enabling the assessment of the health state of the gearbox.
Lessons Learned in Fleetwide Asset Monitoring of Gas Turbines and Supporting Equipment in Power Generation Applications
Authors: Johnson Preston
Published: March 2024

Condition monitoring remains an important technology for equipment life cycle management. Historically, online condition monitoring systems are installed only on the most critical assets within a power plant, process plant, or manufacturing facility. Less critical equipment, while vital to operation of the plant, are only monitored or tested periodically using manual route based technologies. This historical practice leaves equipment specialists with a small amount of time for analysis of collected sensory data (vibration, temperature, oil, power, etc.) as they spend the vast majority of working hours collecting equipment sensory data. Fortunately, data acquisition technology has evolved, making it possible to transform standard and advanced machinery measurements from manual collections to online collections, increasing time for specialists to analyze, and yielding opportunities for automated diagnostics and prognostics. By taking advantage of automation, the ability of equipment owners and operators to lower life cycle costs and increase reliability of plant equipment is greatly improved.The transition from manual route based measurements to a fleetwide surveillance program touches many elements from sensors to networked data acquisition nodes to servers to historians and predictive technologies. Within power generation plants, installation costs, information technology strategies, and long term vision come together to create higher machine reliability at lower operational cost and new automation in performance monitoring, diagnostics, and advisory generation. With automation, comes increased sensory data from pumps and turbines that require new tools for data management, data mining, and data transformation into actionable information. A case study reviews the open and extensible data architecture of a fleetwide monitoring system deployed, the ongoing efforts, and current benefits delivered to the power generation industry participants.
Definition of parametric methods for fault analysis applied to an electromechanical servomechanism affected by multiple failures
Authors: Maggiore Paolo , Vedova Matteo D. L. Dalla , Pace Lorenzo , Desando Alessio
Published: March 2024

In order to detect incipient failures due to a progressive wear of a primary flight command electromechanical actuator, prognostics could employ several approaches; the choice of the best ones is driven by the efficacy shown in failure detection, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a fault detection algorithm able to identify the precursors of the above mentioned electromechanical actuator (EMA) failure and its degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew such to properly schedule the servomechanism replacement. The research presented in the paper was focused to develop a prognostic technique, able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior; in particular four kinds of failure are considered: friction, backlash, coil short circuit, rotor static eccentricity. To this purpose, an innovative model based fault detection technique has been developedmerging together several information achieved by means of FFT analysis and proper "failure precursors" (calculated by comparing the actual EMA responses with the expected ones). To assess the robustness of the proposed technique, an appropriate simulation test environment was developed. The results showed an adequate robustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures.
Vibration Based Blind Identification of Bearing Failures for Autonomous Wireless Sensor Nodes
Authors: Ramirez Andrea Sanchez , Loendersloot Richard , Tinga Tiedo
Published: March 2024

Despite all the attention received by maintainers, undetected roller bearings failures are still a major source of concern in relation with reliability losses and high maintenance costs. Because of that, bearing condition assessment through vibration monitoring remains an intensive topic of scientific research, focusing on the definition of monitoring strategies that allow early stage damage detection, failure causes identification and remaining life prediction. Next to the developments on signal processing, new opportunities of advanced monitoring platforms are devised as those based on Wireless Sensor Networks (WSNs). The combination of integrated sensing, embedded computing and wireless communication provides interesting elements on the development of a new generation of vibration monitoring systems. The algorithms for bearing assessment remain a crucial point for achieving a balance between efficient monitoring strategies and highly flexible monitoring platforms. Though current trends on signal processing for mechanical vibrations focuses on the development of robust techniques, the constraints of embedded processing in relation to energy and memory consumption hamper their implementation on WSN.The present paper discusses the problem of bearing condition characterization from the basis of extraction of damage features associated with the specific stage of its deterioration process. This, other than data driven methods, allow to find the best compromise between robustness of the bearing assessment algorithm and the applicability of the algorithm on a WSN. Two cases are presented as validation of this approach: an artificial damage on a lab setup and a train bearing, for which the possibilities for detection, diagnostics and prognostics are discussed. The advantages and constraints of the use of autonomous wireless sensor nodes is discussed as final part of the paper.
Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft
Authors: Bole Brian , Daigle Matthew , Gorospe George
Published: March 2024

Predicting whether or not vehicle batteries contain sufficient charge to support operations over the remainder of a given flight plan is critical for electric aircraft. This paper describes an approach for identifying upper and lower uncertainty bounds on predictions that aircraft batteries will continue to meet output power and voltage requirements over the remainder of a flight plan. Battery discharge prediction is considered here in terms of the following components; (i) online battery state of charge estimation; (ii) prediction of future battery power demand as a function of an  aircraft flight plan; (iii) online estimation of additional parasitic battery loads; and finally, (iv) estimation of flight plan safety. Substantial uncertainty is considered to be an irremovable part of the battery discharge prediction problem. However, highconfidence estimates of flight plan safety or lack of safety are shown to be generated from even highly uncertain prognostic predictions.
A General Framework for Uncertainty Propagation Based on Point Estimate Methods
Authors: Schenkendorf René
Published: March 2024

A general framework to approach the challenge of uncertainty propagation in model based prognostics is presented in this work. It is shown how the so-called Point Estimate Methods (PEMs) are ideally suited for this purpose because of the following reasons: 1) A credible propagation and representation of Gaussian (normally distributed) uncertainty can be done with a minimum of computational effort for non-linear applications. 2) Also non-Gaussian uncertainties can be propagated by evaluating suitable transfer functions inherently. 3) Confidence intervals of simulation results can be derived which do not have to be symmetrically distributed around the mean value by applying PEM in conjunction with the Cornish-Fisher expansion. 4) Moreover, the entire probability function of simulation results can be reconstructed efficiently by the proposed framework. The joint evaluation of PEM with the Polynomial Chaos expansion methodology is likely to provide good approximation results. Thus, non-Gaussian probability density functions can be derived as well. 5) The presented framework of uncertainty propagation is derivativefree, i.e. even non-smooth (non-differentiable) propagation problems can be tackled in principle. 6) Although the PEM is sample-based the overall method is deterministic. Computational results are reproducible which might be important to safety critical applications. - Consequently, the proposed approach may play an essential part in contributing to render the prognostics and health management into a more credible process. A given study of a generic uncertainty propagation problem supports this issue illustratively.
A Model-Based Prognostics Framework to Predict Fatigue Damage Evolution and Reliability in Composites
Authors: Chiachío Juan , Chiachío Manuel , Saxena Abhinav , Rus Guillermo , Goebel Kai
Published: March 2024

In this work, a model-based prognostics methodology is proposed to predict the remaining useful life (RUL) of composite materials under fatigue loads. To this end, degradation phenomena such as stiffness reduction and increase in matrix micro-cracks density are predicted by connecting microscale and macro-scale damage models in a Bayesian filtering framework. The proposed Bayesian filtering framework also allows incorporating various uncertainties in the prediction that are generally associated with material defects, sensing and monitoring noise, modeling errors, etc., to name a few. This, however, results in a explosion of search space due to high dimensionality, and hence a high computational complexity not conducive for real-time monitoring and prediction. To reduce the dimensionality of the problem without significantly compromising on prediction performance (precision and accuracy), a model tuning is first carried out by means of a Global Sensitivity Analysis. This allows identifying and subsequently down selecting the parameters for online adaptation that affect prediction performance the most. Resulting RUL estimates are then used to compute a timevariant reliability index for composite materials under fatigue stress. The approach is demonstrated on data collected from run-to-failure tension-tension fatigue experiments measuring the evolution of fatigue damage in CRFP cross-ply laminates. Micro-cracks are considered as the primary internal damage mode that are estimated from measurements obtained by active interrogation using PZT sensors. Results are presented and discussed for the prediction of growth in micro-cracks density and loss of stiffness for a given panel along with thereliability index calculation for the damaged component.
Diagnostics of Mechanical Faults in Power Transformers - Vibration Sensor Network Design under Vibration Uncertainty
Authors: Yoon Joung Taek , Park Kyung Min , Youn Byeng D. , Lee Wook-Ryun
Published: March 2024

Power transformer is a critical component in energy transmission, and its failure can cause catastrophic social loss. Among many techniques to prevent the transformer failures, ones using vibration signals show good capability of detecting the mechanical faults. For on-site power transformers, numerous vibration sensors are installed to take into account vibration uncertainty which comes from sizable and complex transformers and random operating condition. It, however, brings about the high maintenance cost of sensing system as well as superfluous data obstructing precise diagnostics. This study proposes sensor positioning to detect mechanical faults of power transformers. Thirty six on-site power transformers in nuclear power plants were employed. Their vibration signals are processed based upon the principles of transformer vibration. Vibration characteristics are analyzed in terms of spectrum analysis, vibration contour plot and high vibration locations. Then the sensor network design framework is proposed which adjusts the number of sensors and their locations to measure high vibration signals robustly under vibration uncertainty. It is demonstrated that the designed sensing system evaluates the health status of the power transformers successfully with the significantly reduced number of sensors.
Designing for Human-Centred Decision Support Systems in PHM
Authors: McDonnell Darren , Balfe Nora , Al-Dahidi Sameer , O'Donnell Garret E.
Published: March 2024

Prognostics and health management (PHM) represents a paradigm shift from legacy condition based maintenance (CBM) frameworks by expanding the potentials to accurately and robustly detect and diagnose incipient system faults. The ultimate goal of PHM is reliably predicting system failure times to allow for efficient maintenance scheduling either autonomously or by human decision makers (DM). In many industrial settings today the output from PHM systems constitutes a decision support system (DSS) used to aid DM, as entirely autonomous systems have not seen widespread industrial integration. However, there is relatively little support for engineers designing PHM systems in terms of human factors and how to provide the information in a way that actively supports human decision-making and this gap may result in limited use of PHM system by maintainers. The reliability of the information presented is a critical factor in the user acceptance and trust in a system. As a first step in developing such guidance, this paper reviews the implementation of other DSS and presents a design framework whereby PHM reliability levels are mapped against a suggested level of human input to the decision making process regarding required maintenance. The aim is to provide engineers with a guide to the level to which they should consider human factors and the presentation of information in the design of their PHM system. Fundamental to the suggested paradigm is that the uncertainties within a PHM system can be quantified, and as uncertainty increases, the requirement for DM to access additional information not explicitly tied to the PHM output increases. This information can form both explicit and tacit knowledge of a system or indeed industrial contexts surrounding decision implications, such as acceptable maintenance intervention windows in busy production schedules. As the complexity of a system or component being monitored is likely to affect the uncertainty within the PHM system associated with it, we are considering the overall cumulative uncertainty of a model output as the metric through which the required level of human input can be inferred. Coupled to this is the fact that increased model uncertainty is a causal factor in distrust and potential non-use of the model in industrial applications. It is the authors’ belief therefore that designing for increased human-model interaction concurrent with increasing model uncertainty may lead to a better engagement with PHM decision support capabilities, thereby offering the full advantages that PHM has to offer. The framework presented in this paper is an initial step towards facilitating the design of more usable and useful PHM systems.
Aircraft Preventive Diagnosis Based on Failure Conditions Graphs
Authors: Chérière Vincent
Published: March 2024

Modern aircraft are designed to be fault-tolerant. Current maintenance systems provide diagnosis of existing faults, capabilities to do trend monitoring, but no information about the real-time remaining tolerance margin knowing the existing faults, and regarding next incoming MMEL (Master Minimum Equipment List) items that impact aircraft dispatch capabilities.This paper presents a new concept of aircraft preventive diagnosis based on failure conditions graphs with the associated logical framework. The complete method was successfully applied by Airbus on A380 use cases. The first part of the present paper gives the formal logical definitions for the aircraft preventive diagnosis and remaining margin, distance, risk rate. The second part gives an application example based on the landing gear system of an aircraft and also the lessons learnt from Airbus on A380. Finally, the last section provides a logical integration of preventive diagnosis with prognosis that opens new perspectives.