bluuebunny/crossref_metadata_2025 huggingface dataset:
Distributed Damage Estimation for Prognostics based on Structural Model Decomposition
Authors: Daigle Matthew , Bregon Anibal , Roychoudhury Indranil
Published: March 2024

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components that include how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation- based experiments to demonstrate the approach.
Lithium-ion Battery State of Health Estimation Using Ah-V Characterization
Authors: Le Daniel , Tang Xidong
Published: March 2024

The battery state of health (SOH) is a measure of the battery’s ability to store and deliver electrical energy. Typical SOH methods characterize either the battery power or energy. In this paper, new SOH estimation methods are investigated based on the battery energy represented by the Ampere-hour throughput (Ah). The methods utilize characteristics of the Ah to estimate the battery capacity or the useable energy for state of health estimation. Three new methods are presented and compared. The simulation results indicate the effectiveness of the methods for state of health estimation.
Using the Validated FMEA to Update Trouble Shooting Manuals: a Case Study of APU TSM Revision
Authors: Yang Chunsheng , Létourneau Sylvain , Zaluski Marvin
Published: March 2024

Trouble Shooting Manuals (TSMs) provide useful information and guidelines for machinery maintenance, in particular, for fault isolation given a failure mode. TSMs produced by OEMs are usually updated based on feedback or requests from end users. Performing such update is very demanding as it requires collecting information from maintenance practices and integrating the new findings into the troubleshooting procedures. The process is also not fully reliable as some uncertainty could be introduced when collecting user information. In this report, we propose to update or enhance TSM by using validated FMEA (Failure Mode and Effects Analysis), which is a standard method to characterize product and process problems. The proposed approach includes two steps. First, we validate key FMEA parameters such as Failure Rate and Failure Mode Probability through an automated analysis of historical maintenance and operational data. Then, we update the TSM using information from the validated FMEA. Preliminary results from the application of the proposed approach to update the TSM for a commercial APU suggest that the revised TSM provides more accurate information and reliable procedures for fault isolation.
Time Distribution Mapping: a Generic Transient Signal Monitoring Technique for Prognostic Methods
Authors: Sharp Michael , Hines J Wesley
Published: March 2024

The utilization of steady state monitoring techniques has become an established means of providing diagnostic and prognostic information for systems and equipment. This is mainly driven by both the wealth of available analysis techniques and the comparatively larger amount of data. However, steady state data is not the only, or in some cases, even the best source of information regarding the health and state of a system. Transient data has largely been overlooked as a source of system information due to the additional complexity in analyzing these types of signals. Time Distribution Mapping via the Sharp Transform allows for a fast, intuitive, generic quantification of deviations a transient signal from an established norm. Without regard to the type or source of the signal, referencing to an established Time Distribution Map can implicitly capture shifts mean, standard deviation, skewness, or even gross frequency shifts without need of additional processing. By quantifying and trending these shifts, an accurate measure of system heath can be established and utilized by prognostic algorithms. In fact, for some systems the elevated stress levels during transients can provide better, more clear indications of system health than those derived from steady state monitoring.
An Approach to Prognostic Decision Making in the Aerospace Domain
Authors: Balaban Edward , J. Alonso Juan
Published: March 2024

The field of Prognostic Health Management (PHM) has been undergoing rapid growth in recent years, with development of increasingly sophisticated techniques for diagnosing faults in system components and estimating fault progression trajectories. Research efforts on how to utilize prognostic health information (e.g. for extending the remaining useful life of the system, increasing safety, or maximizing operational effectiveness) are mostly in their early stages, however. The process of using prognostic information to determine a system’s actions or its configuration is beginning to be referred to as Prognostic Decision Making (PDM). In this paper we propose a formulation of the PDM problem with the attributes of the aerospace domain in mind, outline some of the key requirements for PDM methods, and explore techniques that can be used as a foundation of PDM development. The problem of satisfying the performance goals set for specific objective functions is discussed next, followed by ideas for possible solutions. The ideas, termed Dynamic Constraint Redesign (DCR), have roots in the fields of Multidisciplinary Design Optimization and Game Theory. Prototype PDM and DCR algorithms are also described and results of their testing are presented.
Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation
Authors: R. Celaya Jose ́ , Saxena Abhinav , Goebel Kai
Published: March 2024

This article discusses several aspects of uncertainty representation and management for model-based prognostics method- ologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.
Peer-to-peer Collaborative Vehicle Health Management – the Concept and an Initial Study
Authors: Zhang Yilu , Du Xinyu , Salman Mutasim
Published: March 2024

Advanced vehicle diagnostics and prognostics (D&P) technology enhances ownership experience and reduces corporate warranty cost. D&P performance optimization requires significant algorithm tuning and a large amount of test data collection, which is resource consuming.
In this paper, we propose a novel D&P framework called Collaborative Vehicle Health Management (CVHM) to automatically optimize the D&P algorithms on a host vehicle, using the field data collected from peer vehicles encountered on the road. The carefully designed system architecture and learning algorithms enhance D&P performance without costly human intervention. The experimental results on battery remaining useful life prediction show the effectiveness of the proposed framework. This proposed framework has been implemented in a small test fleet as a proof-of-concept prototype.
Assessment of Remaining Useful Life of Power Plant Steam Generators – a Standardized Industrial Application
Authors: Kunze Ulrich , Raab Stefan
Published: March 2024

The Web based condition monitoring and diagnostic system “Boiler Fatigue Monitoring” enables on-line assessment of cumulative boiler creep and low cycle fatigue according to the European standard EN 12952-3/4 issued in 2001.The application is employed as autonomous module as well as fully integrated into the Siemens process control system SPPA-T3000 and is becoming more and more a standard part of the power plant instrumentation and control (I&C).The Fatigue Monitoring System (FMS) is a standard industrial application for both, new built power plants and retrofits of existing units of any kind. The system is not limited to Siemens I&C systems, it is possible to integrate FMS also into power plants with I&C systems of other suppliers. FMS is also capable for calculating the remaining lifetime for boilers designed according to the American standard ASME VIII-2.
Fatigue Crack Growth Prognostics by Particle Filtering and Ensemble Neural Networks
Authors: Baraldi Piero , Compare Michele , Sauco Sergio , Zio Enrico
Published: March 2024

Particle Filtering (PF) is a model-driven approach widely used in prognostics, which requires models of both the degradation process and the measurement acquisition system. In many practical cases, analytical models are not available, but a dataset containing a number of pairs component state - corresponding measurement may be available.In this work, a data-driven approach based on a bagged ensemble of Artificial Neural Networks (ANNs) is adopted to build an empirical measurement model of a Particle Filter for the prediction of the Residual Useful Life (RUL) of a structure whose degradation process is described by a stochastic fatigue crack growth model of literature. The work focuses on the investigation of the capability of theproposed approach to cope with the uncertainty affecting the RUL prediction.
Prediction of Fatigue Crack Growth in Airframe Structures
Authors: Finda Jindřich , Vechart Andrew , Hédl Radek
Published: March 2024

The paper describes the general proposal, function and performance of a prognostics system for fatigue crack growth in airframe structures. Prognostic capabilities are important to a superior Structural Health Monitoring System (SHM). An aim of the prognosis is estimation / prediction of a system / subsystem / structure remaining life, i.e. the time at which damage (crack, corrosion, wear, delamination, disbonding, etc.) will result in a failure of the considered element.
A Comparison of Methods for Linear Cell-to-Cell Mapping and Application Example for Fault Detection and Isolation
Authors: Mohon Sara , Pisu Pierluigi
Published: March 2024

In this paper, the Generalized Cell Mapping (GCM) method for a linear system is compared with a new stochastic method for novel cell-to-cell mapping. The authors presented the new stochastic method in a previous paper last year. The two methods are compared in an application example of a vehicle alternator. The alternator may experience three faults including belt slippage, a brokendiode, or incorrect controller reference voltage. Fault detection and isolation (FDI) is performed using the two cell-to-cell mapping methods. The results show that the new stochastic method is more computationally intensive but yields better isolation results than the GCM method
Operational Metrics to Assess Performances of a Prognosis Function. Application to Lubricant of a Turbofan Engine Over- Consumption Prognosis
Authors: HMAD Ouadie , MASSE Jean-Rémi , GRALL-MAËS Edith , BEAUSEROY Pierre , MATHEVET Agnès
Published: March 2024

In the aeronautical field, one of the major concerns is the availability of systems. To ensure availability, Prognosis and Health Management algorithms are developed. The aim of these algorithms is twofold. The first one is to detect and locate degradation premise of “no go” condition occurrence. The second one is to predict the health state of the system at a given time horizon. Before introducing PHM algorithms in operation, it is necessary to assess their performances. This is accomplished thank to a “maturation” phase. This phase consists in defining the performance metrics from an operational relevance point of view, in estimating this performance indicator and finally in proposing improvements to meet the airline companies requirements. We consider that the maturation of the detection function has already been completed and that we are interested in the maturation of the prognosis function. This paper deals with the performance assessment of a prognosis function using two operational metrics. A performance estimation procedure is developed. It is applied to the prognosis of turbofan engine lubricant over-consumption. The considered prognosis function is the probability to cross “no go” condition threshold at a given time horizon. This prediction is made thanks to an indicator of the health state of the system. Then it is compared with a threshold in order to trigger an alarm and give rise to a removal if necessary. Within this framework, we have defined two operational metrics for assessing the performance of this prognosis function. These metrics are the “ratio of justified removals” (P(Alarm|Crossing)) and the “ratio of not justified removals” (P(No-crossing|Alarm)). These metrics require the availability of observed lubricant over-consumption to compare the prediction results to the observed values. In the absence of lubricant over-consumption values in operation, a way is to simulate values. This communication describes the procedure to estimate the performance of the prognosis function and presents the obtained results. The performances estimations trigger improvements. It appears that we have to enhance the precision of the considered health indicator before continuing to assess the performance of the considered prognosis function.
Aligning PHM, SHM and CBM by understanding the physical system failure behaviour
Authors: Tinga Tiedo , Loendersloot Richard
Published: March 2024

In this work the three disciplines of condition based maintenance (CBM), structural health monitoring (SHM) and prognostics and health management (PHM) are described. Then the characteristics of the disciplines are compared, which leads to a clear insight in the commonalities, but also in the difference in objectives and scope of the three disciplines. The disciplines are then demonstrated using three different case studies on bearing vibration monitoring, composite panel structural health monitoring and helicopter landing gear prognostics, respectively. After a discussion on the benefits of understanding the system physical (failure) behaviour, an integrated approach is proposed in which the three disciplines are aligned. This approach starts from defining an appropriate monitoring strategy (SHM and CM) and eventually ends in supporting the decision making (PHM) that leads to an optimal maintenance process throughout the life cycle of the asset.
Derivation of Fuzzy Diagnosis Rules for Multifunctional Fuel Cell Systems
Authors: Modest Christian , Thielecke Frank
Published: March 2024

This paper presents a model-based approach for the derivation of fuzzy diagnosis rules. These are used to classify data of faulty system behavior in order to identify root causes. The data is gained from an extended simulation model of a multifunctional fuel cell system for aircraft use. Faulty behavior is implemented into each component and a bottom up simulation is carried out. The data gained is classified according to root causes. This means that each data vector is assigned to a class representing one type of simulated fault. The classified data is then fed into an evolutionary optimization procedure. There it is weighted and separated into training and validation data. Inside the optimization procedure, the structure of the fuzzy diagnosis rule is represented by a chromosome that has a discrete and a real valued part. The discrete part describes the selection of a signal and the real valued part states parameters of the membership function for each signal. Based on training data, a genetic algorithm optimizes both parts and a set of optimal binary and real valued parameters is gained. By that, one fuzzy diagnosis rule at a time is identified that bestfits a set of fitness functions. On basis of this rule, weights of the training data are updated afterwards. This is done in order to guide the genetic algorithm in the next run to data vectors that are not covered effectively yet. Each run of the algorithm gives a new fuzzy diagnosis rule. The performance of the set of all rules that are gained so far is evaluated by use of validation data. Subsequently, a new run is started. This process continues until a stop criterion is reached. A set of optimal fuzzy diagnosis rules is gained in the end.
Multi-objective optimization of OEE (Overall Equipment Effectiveness) regarding production speed and energy consumption
Authors: Horenbeek Adriaan Van , Pintelon Liliane , Bey-Temsamani Abdellatif , Bartic Andrei
Published: March 2024

Using condition monitoring to track machine health and trigger maintenance actions is a proven best practice. By monitoring machinery health, costly failures are avoided and downtime due to outages is reduced. This results in an improved OEE (Overall Equipment Effectiveness). Many papers discuss the implementation of condition monitoring to prevent failures and optimize maintenance interventions. However, much less attention is paid to the use of condition monitoring information in order to optimize production capacity of a machine or a plant. This optimization is often translated in production plants by maximizing the production capacity (speed) and minimizing machine’s downtime. As energy consumption is becoming more and more an important decision criterion in modern manufacturing plants, the former optimization needs to take this parameter into account. As such a trade-off has to be made between the gain in capacity and the cost of the additional energy consumed. Therefore, in this paper we will develop a multi-objective optimization of OEE to allow multiple-criteria decision making. More precisely, the goal of this paper is to establish the link between condition monitoring information and production capacity optimization by continuously adjusting production parameters (i.e. production speed) taking into account the machine’s condition and the energy consumption.
Diagnosability-Based Sensor Placement through Structural Model Decomposition
Authors: Daigle Matthew , Roychoudhury Indranil , Bregon Anibal
Published: March 2024

Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system.
Statistical Approach to Diagnostic Rules for Various Malfunctions of Journal Bearing System Using Fisher Discriminant Analysis
Authors: Jeon Byungchul , Jung Joonha , Youn Byeng D. , Kim Yeonwhan , Bae Yong-Chae
Published: March 2024

This research is focused on developing an efficient fault diagnosis procedure for a journal bearing system. Vibration data of journal bearing rotor simulator under four conditions (i.e. a normal condition and three anomaly conditions including unbalance, rubbing and misalignment) was used to develop the algorithm. In order to improve diagnostic performance, cycle based time-domain features and frequency-domain features were extracted after resampling process being applied to the raw vibration data. Then, the optimal feature selection was accomplished by mixture of random combination performance test and Fisher Discrimin- ant Ratio (FDR) criteria. After selecting optimal features, Fisher Discriminant Analysis (FDA) algorithm classified each abnormal conditions mentioned above. To end with, the result of classification is evaluated and verified
Anomaly Detection Using Self-Organizing Maps-Based K-Nearest Neighbor Algorithm
Authors: Tian Jing , Azarian Michael H. , Pecht Michael
Published: March 2024

Self-organizing maps have been used extensively for condition-based maintenance, where quantization errors of test data referring to the self-organizing maps of healthy training data have been used as features. Researchers have used minimum quantization error as a health indicator, which is sensitive to noise in the training data. Some other researchers have used the average of the quantization errors as a health indicator, where the best matching units of the trained self-organizing maps are required to be convex. These requirements are not always satisfied. This paper introduces a method that improves self-organizing maps for anomaly detection by addressing these issues. Noise dominated best matching units extracted from the map trained by the healthy training data are removed, and the rest are used as healthy references. For a given test data observation, the k-nearest neighbor algorithm is applied to identify neighbors of the observation that occur in the references. Then the Euclidean distance between the test data observation and the centroid of the neighbors is calculated as a health indicator. Compared with the minimum quantization error, the health indicator extracted by this method is less sensitive to noise, and compared with the average of quantization errors, it does not put limitations on the convexity or distribution of the best matching units. The result was validated using data from experiments on cooling fan bearings.
Diagnostics and Prognostics of Wire-Bonded Power Semi-Conductor Modules subject to DC Power Cycling with Physically-Inspired Models and Particle Filter
Authors: Degrenne Nicolas , Mollov Stefan
Published: March 2024

This paper presents an algorithm for determining the State of Health (SoH) and the Remaining Useful Life (RUL) of wire-bonded power semi-conductor modules. It is hybrid in the sense that it combines the estimations of physically-inspired models and the on-line data acquisition. A key aspect is the analysis, modeling, estimation, correction and exploitation of the on-state voltage Von evolution. In this paper, the algorithm is demonstrated with a particle filter and with power cycling experimental tests performed until complete wire-bond failure.
Physics Based Methodology for the Estimation of Bearings' RUL: Physics-Based Models, Diagnostic Methods and Experiments
Authors: Gazizulin Dmitri , Klein Renata , Bortman Jacob
Published: March 2024

Rolling element bearing (REB) is one of the basic mechanical components in a rotating machinery. REBs’ remaining useful life (RUL) estimation allows not only to assess time for maintenance actions but also can prevent a critical failure of the mechanical system. Usually, REB damage occurs in two stages, damage initiation and damage propagation. In the current work, it is assumed that spall is generated on the surface of the raceway during the initiation stage. The spall generation process is modeled based on continuum damage mechanics with the representation of material grain structure and implemented using a Finite Element (FE) software. The results of the model are in a good agreement with published theoretical and experimental data. However, after the first spall formation, the bearing might be fully operational for millions of cycles. For estimation of the bearing RUL it is important to understand the damage propagation process. The material behavior at the trailing edge of the spall during the rolling element (RE) impact is analyzed. The analysis is carried out by using a hybrid modeling approach. This approach integrates non-linear dynamic modeling and FE simulations. The paper also includes a discussion on the ongoing research and the methodology for the development of the prognostic method. Implementation of the proposed methodology has the potential to provide a complete estimation of the bearing’s RUL: from first spall formation to the un-operational bearing.
Vibration-based Rotorcraft Gearbox Monitoring Under Varying Operating Conditions
Authors: Carcel Cristobal Ruiz , Starr Andrew , Ottewill James R. , Jaramillo Victor H.
Published: March 2024

Modern rotorcrafts rely on Health and Usage Monitoring Systems (HUMS) to enhance their availability, reliability and safety. The complexity of the transmission system of new helicopter designs such as the Airbus Racer provides additional challenges for HUMS. The work presented here demonstrates a vibration-based monitoring approach for the lateral gearboxes of the racer. This approach relies on the statistical analysis of different condition indicators (CIs) extracted from vibration signals under different operating regimes to define a baseline for these CIs during normal operation. This permits the normalization of CIs from newly acquired signals with respect to the expected baseline value, facilitating the detection of anomalies in the signal characteristics for a variety of operating regimes. The monitoring capabilities of the proposed approach are tested using experimental data where the vibration response to different mechanical malfunctions was artificially seeded in to the acquired signals.
Predicting SMPS's remaining life using ripple ratio of electrolyte capacitor
Authors: Jeong Ui Hyo , Yoon Yang Gi , Hyung Jae Phil
Published: March 2024

Our objective is predicting electrolyte capacitor's lifetime at operating situation using ripple ratio. Most electrical products have SMPS(Switched mode power supply) and SMPS's life is mostly dependent on electrolyte capacitor's life. Our research can help predicting SMPS's lifetime and establishing maintenance plan of SMPS.
Non-invasive Sensing for Aerospace Fuel Systems
Authors: Mills Andy , Blakey Simon , Souris Spiridon , Kadirkamanathan Visakan
Published: March 2024

An aerospace fuel system contains valves required to operate in inhospitable environments where conventional position sensing cannot achieve the required reliability.  This work demonstrates the use of non-invasive, high temperature sensors to monitor valve position.
Novel fuel systems, as required for lean burn combustion, can carry a high risk failure mode.  The rapid detection of these failure modes, such as valve sticking or impending sticking would reduce this risk. However, sensing valve state is challenging due to hot environmental temperatures, which result in a low reliability for conventional position sensing.  Conventional sensing also requires moving parts that can be prone to induce leaks and other failure modes to the valve. 
Acoustic emission (AE) sensing technology is evaluated to provide a non-invasive, remote and high temperature tolerant solution.  The fluidic turbulence created by flow through a narrow valve opening can be sensed to determine the valve state.  A pilot scale aviation fuel thermal stability rig has been utilised to place passive AE sensors to verify the detection capability of valve state.  Data collected from experiments exhibited an acoustic response allowed open and closed states to be distinguished. The sensitivity to different sensor locations and an analysis of the ability to characterise opening time response as well as open / closed state is performed - this is expected to provide diagnostics of incipient fault conditions.
Automated Predictive Monitoring and Diagnosis in the Energy and Natural Resources Sector
Authors: Power Yvonne
Published: March 2024

This paper explores strategies for automated, predictive monitoring and diagnosis (M&D) using advanced intelligent systems technologies within the Energy and Natural Resources (ENR) sector to address safety implications and costs associated with downtime and emergency breakdown repairs.  Automated, predictive M&D lends itself towards application within centralized M&D centers to allow monitoring across an entire fleet, to improve efficiency, reduce duplication of functions and allow for consistent, best practice operation and higher quality repairs across distributed assets.  However, centralization is often accompanied by a reduction in the number of on-site personnel and loss of critical knowledge for the operation and maintenance of assets.  Therefore, the selection of appropriate data, sensors, algorithms, a suitable platform, analytical tools, visualizations and ERP/CMMS integration form the basis for automated and predictive M&D of asset performance (intelligent Asset Performance Management (iAPM)) making it possible to detect and diagnose issues across geographically dispersed assets so that results are available in daily operational workflow for executive, analytical and operational personnel before they impact production, operations and safety.
Understanding malfunctions of smart card validators in trams by development of decision tree models
Authors: Ten Have Marielle , Beekmans Bas , Maas Carolien
Published: March 2024

Passengers can pay with an electronic smart card in every tram operational in Amsterdam. Malfunction of smart card validators leads to reduced customer satisfaction, loss of revenues and unplanned costs. Malfunction of a smart card validator is relatively rare: it fails about once every two years, which is recorded by the maintenance shop inaSAP database. The validators generate transactions and events. The events (about 30 a day per validator) are stored in the OV Chipcard database. During this project, weinvestigated whether it was possible to understand malfunctions by analyzingtheeventdatageneratedinthe24hourspreceding those registered malfunctions. Analysis was done on over six million events generated within a four-month period (January - April 2017) by more than 1,700 validators mounted in 200 trams. The selected decision tree models showed that about 50% of registered malfunctions were related to specific events that occur in relatively high frequencies. These events signified loss of communication and/or the inability to receive GPS location information. The use of decisiontreemodelsmadeitpossiblenotonlyto predict the malfunctions but also to get a better understanding of the root cause of the malfunctions. These insights can be used asinputforimprovingthereliabilityof the smart card validators.
Viable Opportunity for Fielding an Aircraft Structural Health Monitoring System
Authors: Bos Marinus J.
Published: March 2024

An important trend in the sustainment of aircraft is the transition from preventative maintenance to Condition Based Maintenance [CBM]. For CBM it is essential that the actual system condition can be measured and that the measured condition can be reliably extrapolated to a convenient moment in the future in order to facilitate the planning process while maintaining flight safety. Much research effort is currently being put in the development of technologies that enable CBM, including Structural Health Monitoring [SHM] systems. Good progress has already been made when it comes to sensors, sensor networks, data acquisition, models and algorithms, data fusion/mining techniques, etc. However, the transition of these technologies into service is very slow. Reasons are that business cases are difficult to define and that certification of SHM systems is very challenging. This paper describes a possibility for fielding an SHM system on legacy aircraft with a minimum amount of certification issues and with a good prospect of a positive return on investment. For appropriate areas in the airframe of an aircraft, the application of SHM will reconcile the fail-safety and slow crack growth damage tolerance approaches that can be used for safeguarding the continuing airworthiness of these areas, combining the benefits of both approaches and removing the drawbacks.
Applications of Artificial Intelligence and Decision-Making Methods in PHM
Authors: Conroy Paddy , Stecki Jacek , Bautista Joshua , Ringeri Alex
Published: March 2024

Developing PHM capability for a system is a multi-staged process. This paper explores genetic algorithms, neural networks, fuzzy logic systems, AHP (Analytical Hierarchy Process), and Boolean logic to synthesize and fuse complex decisions arising in PHM design. Tools for PHM analysis are typically introduced and utilized towards the end of a products design or potentially after design. The methods proposed are tools that can be implemented during conceptual and early stage preliminary design prior to specific hardware design decisions being made. As a result, diagnostic capability can be developed along with the broader system allowing better embedded design of diagnostic instruments into the system and giving PHM a greater role in operation rather than being a secondary consideration of system development.
Cost benefit analysis of applying PHM for Subsea Applications
Authors: Gao Xiaojing
Published: March 2024

The decrease in oil price has been a hot topic over recent years and has directly affected oil companies and original equipment manufacturers (OEMs) of systems used for oil production (subsea assets in particular). Numerous technologies, methodologies, processes and tools are being developed to support lifecycle cost reductions for subsea assets and to maximize the overall profits for the industry.  Prognostic and Health Management (PHM) is a technology that can assess and predict the remaining useful life (RUL) of a system, enabling operations and maintenance strategies to be better planned. One goal of PHM is to lower the cost for a system during the operational period by reducing the downtime cost and risk of unanticipated failures. Traditionally, failure was accepted in this industry sector through the incorporation of functional safety features of critical components. Unfortunately, a fail-safe strategy has significant downtime costs associated with it. However, introduction of new technology (e.g. PHM) requires a business case to demonstrate the potential benefits. At present there is a lack of literature on the topic of PHM cost-benefit-risk analysis for subsea production systems. This paper will provide a background of lifecycle cost and the potential cost savings PHM can deliver in the subsea application will be provided. The paper will also expand on four categories of factors contributing to the cost benefit analysis as well as a case study to illustrate the potential cost savings and the side-effects from PHM integration on subsea equipment.
Enhanced Data Driven Decision Support
Authors: Rijsdijk Chris , Tinga Tiedo
Published: March 2024

As items are increasingly being equipped with sensors, the applicability of data driven decision support may similarly grow. This paper surveys an endeavor to support decisions with sensor recordings that were not primarily installed to support decisions. To become meaningful decision support, these sensor recordings should enable better causal inferences because decisions should cause the future.
Data driven decision support is not evident as normative decision theory is known to suffer from validation issues. This work attempts to alleviate concerns about (i) the assessment of preference, about (ii) causal inferences from non-experimental data and about (iii) the assessment of the uncertainty about the prospective outcome of a decision. This work argues that sensor recordings may appear to be appreciable decision support by presenting some typical cases of human recorded events that were enriched with sensor recordings.
Model-Based Prognosis for Remaining Useful Life Prediction of Composite Components
Authors: Lim Juhee , Lee Jongsoo
Published: March 2024

Prognostics of composite structures is a key issue in the structural health monitoring of automotive, aerospace, and mechanical systems. In order to predict the remaining useful life (RUL) of composite structures, especially, we focused on the analysis of lower control arm (LCA), which is laminated at various angles with carbon fiber reinforced plastic (CFRP), as a component under severe load conditions. LCA model is shape optimized by applying the CFRP material to the conventional LCA shape, and the damage model according to the fatigue cycle is obtained by integrating the simulation technique and fatigue characteristics of CFRP. Model-based approach includes uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process for predicting the matrix crack evolution and structural stiffness degradation caused by fatigue loads. Then, Bayesian inference is employed to estimate model parameters characterizing the damage behavior using measurement data. As a result, the RUL of CFRP LCA is calculated based on the estimated model parameters associated with uncertainties.
Online Prediction of Remaining Lifetime of IGBT semiconductor Modules
Authors: Salah OULD MAHAMMED
Published: March 2024

This study presents online prediction of remaining useful lifetime of IGBT semiconductor modules using Prognostic and Health Management (PHM) algorithms. This research focuses on the analysis, modeling and exploitation of the degradation in wire-bonded power modules in order to estimate the remaining useful lifetime (RUL) of IGBTpower module.
Automated Feature Validation of Trip Coil Analysis in Condition Monitoring of Circuit Breakers
Authors: Hosseini Michael , Helm Joseph , Stephen Bruce , McArthur Stephen D. J.
Published: March 2024

Datasets of historical performance metrics can offer valuable insight into an asset fleet’s health. This is especially so in the context to establishing normal behavior and thresholds of acceptable performance for diagnostic purposes. However, plant performance can often be obscured by data quality issues which introduce artefacts that do not pertain to asset health. This paper utilises a supervised ensemble machine-learning approach to automate the process of filtering maintenance data based on their predicted validity. The results are then presented both in terms of classification performance, and the impact on the distributions directly. This helps to ensure engineers are basing their diagnostic decisions on valid data. The accuracy of the filtration process, and its effect on the final thresholds will be discussed. To illustrate, this paper uses data of varying quality on circuit breaker trip tests obtained from operational medium-voltage circuit-breakers spanning several decades with the aim of providing decision support for switchgear diag­nostics.
A Bayesian assessment for railway track geometry degradation prognostics
Authors: CHIACHIO-RUANO JUAN , CHIACHIO RUANO Manuel , Prescott Darren , Andrews John
Published: March 2024

Advanced PHM techniques have the potential to substantially reduce railway track maintenance costs while increasing safety and availability. However, there is still a significant lack of knowledge and experience in relation to suitable PHM models and algorithms within the context of railway track geometry degradation. This paper proposes a Bayesian model class methodology for prognostics performance assessment whereby different prognostics algorithms can be rigorously assessed and ranked according to their relative probability to predict the future degradation process. The proposed framework is exemplified and tested for a case study about track degradation prognostics using published data about track settlement, taken from a simulated traffic loading experiment carried out at the Nottingham Railway Test Facility.
Wear monitoring of journal bearings with acoustic emission under different operating conditions
Authors: Bote-Garcia José-Luis , Mokhtari Noushin , Gühmann Clemens
Published: March 2024

It has been shown that Acoustic Emission (AE) can be used to classify different friction states and identify defects in journal bearings. In addition, it has been demonstrated in experimental setups that AE can be used to estimate the wear volume of sliding lubricated metallic contacts. The aim of our work is to monitor the wear during the operation of a journal bearing. This work deals with the development of a prognosis system for a detection of the wear and its volume by means of AE before an actual fault occurs. For this purpose, a journal bearing test bench was developed, where the oil temperature for the lubrication, load and rotational speed can be set. Experiments were carried out under different operating conditions and lasted up to 20 hours each. To establish a correlation between wear and AE, the wear volume is determined by comparing recorded roundness profiles of the inner race before and after each experiment at multiple positions. The method used is validated by measuring the roughness of the inner race surface, thus confirming the position of the wear. The analysis of the recorded AE signals shows an amplitude modulation as well as bursts. The frequency of the amplitude modulation correlates with the rotational speed even at higher velocities. Bursts in the AE signal are directly related to wear. Various features, e.g. root mean square value and properties of the measured bursts, of the AE signal and their relevance for the prediction of the wear are analyzed. A quantitative estimation of the volume proves to be challenging, while a qualitative estimation of occurrence and severity of wear can be reliably detected.
Condition Monitoring of a Paper Feeding Mechanism Using Model-of-Signals as Machine Learning Features
Authors: Barbieri Matteo , Mambelli Francesco , Lucchi Jacopo , Diversi Roberto , Tilli Andrea , Sartini Matteo
Published: March 2024

Prognostics and Health Management of machine devices and parts is a hot topic in the Industry 4.0 era. In this fashion, automated procedures to evaluate machinery working conditions are essential to minimize downtime and maintenance costs. In this work, we study how to monitor the decrease in performance of a paper sheet feeder for the packaging industry under heavy-duty cycle operations. The main measurable outcome of such degradation is the increase in backlash among the device moving components. A wide variety of methods and procedures is available to tackle this monitoring problem. In this paper, we analyze the use of a simple yet efficient diagnosis methodology that can exploit machinery controllers (i.e., Programmable Logic Controllers) edge-computing capabilities. Vibration measurements are known in the literature to retain information about the system's mechanics. Model-of Signals, a data-driven approach based on black box system identification, allows to extract that information reliably during machinery working cycle. The refinement of those data using machine learning allows the retrieval of knowledge about the health state of the machine. In this study, the feeder mechanism is run to failure with its parts backlash measured at given time intervals. Accelerometer signals are modelled as AutoRegressive processes whose coefficients are then considered as features to feed to machine learning algorithms, which are employed to perform severity evaluation of the ongoing degradation. Estimation and prediction are both implementable on-board the controller, while the learning task can be carried out remotely, in a cloud computing perspective. The exploitation of AutoRegressive modelling gives a simple and inherent methodology for feature selection, serving as a foundation of the machine learning stage. We make use of a Support Vector Machine algorithm to analyze how obtained models represent the various levels of backlash in the device and develop a suitable predictor of the degradation severity. Finally, the results of the application of the methodology to the case study are shown.
Repairable System Analysis of the Radioactive Ventilation Gas Monitors at CERN from Field Data
Authors: Hurst Saskia , Boukabache Hamza , Perrin Daniel
Published: March 2024

CERN has the legal obligation to protect the public and the people working on its site from any unjustified exposure to ionizing radiation. Therefore, several monitoring systems are operated at CERN to evaluate the radiological impact of CERN’s accelerators and installations by active monitoring. Having highly reliable and available monitoring systems is hence a crucial factor to ensure a safe operation and steady availability of the accelerator. Besides designing reliable systems or implementing a condition-based maintenance strategy, the analysis of field data also helps to achieve these high reliability and availability goals. To decide, whether a system should be replaced or to estimate its situation on its lifetime curve, the analysis of field data is appropriate. This paper will present how failure data from maintenance interventions on the example of the Ventilation Gas Monitors (VGM) are used to estimate the system lifetime, failure rate and optimal point for exchange. A power law process is used to model the parametric growth curve of the number of failures and the Nelson-Aalen estimator is employed to model the non-parametric growth curve of the number of failures for repairable systems. The power law model is extrapolated and enhanced by its failure costs to make estimations about the necessary budget in the future and the optimal time for exchange. Additionally, confidence bounds and goodness-of-fit tests are included to evaluate the precision of the prediction. Taking advantage of open source software, a model with R language is established for all the calculations.

The first chapter of this paper gives an introduction to the topic. The second chapter outlines the functioning, structure and lifetime requirements of the VGM system and presents its collected failure data. The third chapter specifies the mathematical background for repairable system analysis, how the parameters are calculated with maximum likelihood estimation and describes the implementation of the Cramér-von Mises criterion and confidence bounds as goodness-of fit tests. The fourth chapter presents the results for the VGM system. The last chapter contains a conclusion and an outlook.
Rapid Diagnosis of Induction Motor Electrical Faults using Convolutional Autoencoder Feature Extraction
Authors: Bergesen Husebø Arild , Kandukuri Surya Teja , Klausen Andreas , Huynh Van Khang , Robbersmyr Kjell Gunnar
Published: March 2024

Electrical faults such as stator turns fault and broken rotor bars are among the frequently occurring failure modes in induction motors. This article presents a novel deep learning-based approach for the rapid diagnosis of these electrical faults within a short time window of 200 milliseconds. The extended Park's vector, calculated using three-phase supply currents, is chosen as the medium for fault detection. An unsupervised convolutional autoencoder is designed to detect features distinguishing healthy and faulty conditions. The developed features are supplied to a support vector machine to classify the fault conditions. The proposed approach is validated in a laboratory setup consisting of an inverter-fed induction motor operating under time-varying load and speed conditions with an accuracy > 95%.