bluuebunny/crossref_metadata_2025 huggingface dataset:
Computing Energy-Optimal Tests using DNNF Graphs
Authors: Schumann Anika , Sachenbacher Martin
Published: March 2024

The goal of testing is to discriminate between multiple hypotheses about a system – for example, different fault diagnoses of an HVAC system – by applying input patterns and verifying or falsifying the hypotheses from the observed outputs. Definitely discriminating tests (DDTs) are those input patterns that are guaranteed to discriminate between different hypotheses of non-deterministic systems. Finding DDTs is important in practice, but can be very expensive . Even more challenging is the problem of finding a DDT that minimizes the energy consumption of the testing process, i.e., an input pattern that can be enforced with minimal energy consumption and that is a DDT. This paper addresses both problems. We show how we can transform a given problem into a Boolean structure in decomposable negation normal form (DNNF), and extract from it a Boolean formula whose models correspond to DDTs. This allows us to harness recent advances in both knowledge compilation and satisfiability for efficient and scalable DDT computation in practice. Furthermore, we show how we can generate a DNNF structure compactly encoding all DDTs of the problem and use it to obtain an energy-optimal DDT in time linear in the size of the structure.
Efficient Drive-Based Analysis of Fault Detection Measures in Safety-Related Pneumatic Systems
Authors: Barner Andreas , Bredau Jan , Schiller Frank
Published: March 2024

The focus of this research is on safety-related open-loop controlled pneumatic systems. The top events of those fault trees (FT) would represent functional failures of the system at the highest level of the design. By monitoring the top event of FT by means of signal-based diagnostic methods, each possible failure within the system becomes potentially detectable. This property is deployed explicitly in the proposed approach regarding pneumatic systems. Thus, the system under control is encapsulated, and comprehensive fault detection up to a Diagnostic Coverage of greater than 99% is achievable with tremendously less effort compared to conventional solutions. In this way, a layered system model including a safety-layer similar to current safety-related solutions to fail-safe communication and data processing has been established.
Using distinguishing tests to reduce the number of fault candidates
Authors: Nica Mihai , Nica Simona , Wotawa Franz
Published: March 2024

Tools for automated fault localization usually generate too many bug candidates depending on the underlying technique. Hence, further information is required in order to further restrict the bug candidates. Approaches that rely on specific knowledge of the program to be debugged like variable values at specific position in the source code, are not easily accessible for users especially in case of software maintenance. In order to avoid this problem we suggest to integrate testing for restricting the number of bug candidates. In particular, we suggest to compute possible corrections of the program and from this, distinguishing test cases. A distinguishing test case is a test that reveals different output values for two given program variants, given the same input values. Besides the formal definitions, and algorithms we present the first empirical results of our approach. The use of mutations and distinguishing test cases substantially reduces the number of bug candidates.
Deﬁning and exploring properties in diagnostic systems
Authors: Belard Nuno , Pencolé Yannick , Combacau Michel
Published: March 2024

Every model-based diagnostic approach relies on a representation of a real-world system, in this paper called believed system. The believed system is used along with the observations about the real-world system to generate a diagnostic problem to be solved. In this paper it is ﬁrstly argued that believed systems can differ from real-world systems in many different manners. As so, properties of believed systems, diagnostic problems and diagnostic results are introduced. Then, a series of relations between these properties are proved. The importance of such relations, sometimes seen as intuitive, is that they are necessary to formally prove the accordance between the real-world system and the believed system; to formally prove that a believed system and a diagnostic problem will produce high-quality diagnostic results; or even to ease diagnostic algorithms, since for systems and problems with certain properties, different model-based diagnostic approaches produce the same diagnostic results. In order to introduce the referred properties and reasoning about them a framework of diagnosis based on the difference between the believed and the real systems is proposed.
Diagnosis with Incomplete Models: Diagnosing Hidden Interaction Faults
Authors: Kuhn Lukas , De Kleer Johan
Published: March 2024

This paper extends model-based diagnosis (MBD) (de Kleer and Williams, 1987; Reiter, 1987) to systems with hidden interaction faults. An interaction fault is present if an interaction among a set of components leads to an observable failure, even though each individual component individually meets the specifications. A naive approach to address interaction faults is to simply account for all possible interaction faults in the system model. However, the naive approach presumes that all possible faults, both component and interaction faults, are known and addressed in the model. This assumption is violated by most real world systems, such as shorts in circuits (Davis, 1984) or unmodeled connections (de Kleer, 2007). That leads to incomplete system models, hence possibly hidden interaction faults. The problem of hidden interactions has been known for a long time (Davis, 1984), but until now no general solution has been proposed. Instead of pushing for complete models (Preist and Welham, 1990) or relying on additional structural information (Davis, 1984; Bottcher, 1995; de Kleer, 2007) we approach the challenge differently. We allow system models to be incomplete and introduce a general, domain independent extension to model-based diagnosis to account for resulting hidden interaction faults. This extends model-based diagnosis to systems with incomplete models, in particular to models with incomplete structural information. In the paper, we demonstrate the proposed diagnosis framework on a logic circuit with a hidden interaction fault.
Overcoming Non-Discernibility through Mode-Sequence Analytic Redundancy Relations in Hybrid Diagnosis and Estimation
Authors: Hofbaur Michael , Travé-Massuy`es Louise , Rienm¨uller Theresa , Bayoudh Mehdi
Published: March 2024

Estimating the mode of operation of a hybrid system means to account for its interleaved continuous and discrete dynamics from a partial set of observations. The quality of the estimation obviously depends on the level of diagnosability of the system, which has been shown to depend on the discrete and the continuous observations as a whole. The case of two modes showing very similar, if not the same, continuous dynamic input/output behavior is critical when the discrete events do not allow discrimination. In this case, these modes, so called non-discernible modes, are accepted to be not discriminable. This paper uses the concept of Mode-Sequence Analytic Redundancy Relation which is shown to achieve discriminability of such non-discernible modes in many cases. A focusing test based on modesequence ARRs is then proposed to improve hybrid state estimation.
Decentralized Fault Detection and Isolation of Manufacturing Systems
Authors: Mouchaweh M. Sayed
Published: March 2024

This paper presents a Boolean discrete event model based approach for Fault Detection and Isolation (FDI) of manufacturing systems. This approach considers a system as a set of independent components composed of discrete actuators and their associated discrete sensors. Each component model is only aware of its local desired fault free behavior. The occurrence of a fault entailing the violation of the desired behavior is detected and the potential responsible candidates are isolated using event sequences, time delays between correlated events and state conditions, characterized by sensor readings and control signals. The proposed approach is applied to a flexible manufacturing system.
An Event-based Approach to Distributed Diagnosis of Continuous Systems
Authors: Daigle Matthew , Roychoudhury Indranil , Biswas Gautam , Koutsoukos Xenofon
Published: March 2024

Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diag-nosers each generate globally correct diagnosis results locally, without a centralized coordinator, by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.
Modeling wave propagation in Sandwich Composite Plates for Structural Health Monitoring
Authors: N. Smelyanskiy V. , Hafiychuk V. , G. Luchinsky D. , Tyson R. , Miller J. , Banks C.
Published: March 2024

Wave propagation is investigated in sandwich composite panels using analytical approach for layered materials, Mindlin plate theory and finite element modeling in the context of developing an on-board structural health monitoring system. It is shown that theoretical results are in agreement with the results of numerical simulations and with experimental results.
Prognostics of Power MOSFETs under Thermal Stress Accelerated Aging using Data-Driven and Model-Based Methodologies
Authors: R. Celaya José , Saxena Abhinav , Saha Sankalita , F. Goebel Kai
Published: March 2024

An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die- attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
A Mobile Robot Testbed for Prognostics-Enabled Autonomous Decision Making
Authors: Balaban Edward , Narasimhan Sriram , Daigle Matthew , Celaya José , Roychoudhury Indranil , Saha Bhaskar , Saha Sankalita , Goebel Kai
Published: March 2024

The ability to utilize prognostic system health information in operational decision making, especially when fused with information about future operational, environmental, and mission requirements, is becoming desirable for both manned and unmanned aerospace vehicles. A vehicle capable of evaluating its own health state and making (or assisting the crew in making) decisions with respect to its system health evolution over time will be able to go further and accomplish more mission objectives than a vehicle fully dependent on human control. This paper describes the development of a hardware testbed for integration and testing of prognostics-enabled decision-making technologies. Although the testbed is based on a planetary rover platform (K11), the algorithms being developed on it are expected to be applicable to a variety of aerospace vehicle types, from unmanned aerial vehicles and deep space probes to manned aircraft and spacecraft. A variety of injectable fault modes is being investigated for electrical, mechanical, and power subsystems of the testbed. A software simulator of the K11 has been developed, for both nominal and off-nominal operating modes, which allows prototyping and validation of algorithms prior to their deployment on hardware. The simulator can also aid in the decision-making process. The testbed is designed to have interfaces that allow reasoning software to be integrated and tested quickly, making it possible to evaluate and compare algorithms of various types and from different sources. Currently, algorithms developed (or being developed) at NASA Ames - a diagnostic system, a prognostic system, a decision-making module, a planner, and an executive - are being used to complete the software architecture and validate design of the testbed.
Comparison of Fault Detection Techniques for an Ocean Turbine
Authors: Mjit Mustapha , J. Beaujean Pierre-Philippe , J. Vendittis David
Published: March 2024

The Southeast National Marine Renewable Energy Center at Florida Atlantic University, which supersedes the Center for Ocean Energy Technology (Driscoll et al., 2008), is conducting research and development to support the implementation of ocean current and ocean thermal energy technologies for a low environmental-impact extraction of energy. Fault detection capability is needed for these offshore ocean turbines (and other systems) because access to these machines for maintenance is difficult and costly. Techniques that offer reliable and early (incipient) detection allow for preventive maintenance to prevent the development of secondary faults that may be generated by the primary faults. Several methods for processing and displaying vibration data are compared and evaluated relative to synergistic detection utilizing data from a prototype (dynamometer) of an ocean current turbine. The results may generically apply to other machines, such as wind turbines.
Deriving Bayesian Classifiers from Flight Data to Enhance Aircraft Diagnosis Models
Authors: L.C. Mack Daniel , Biswas Gautam , D. Koutsoukos Xenofon , Mylaraswamy Dinkar , Hadden George
Published: March 2024

Online fault diagnosis is critical for detecting the onset and hence the mitigation of adverse events that arise in complex systems, such as aircraft and industrial processes. A typical fault diagnosis system consists of: (1) a reference model that provides a mathematical representation for various diagnostic monitors that provide partial evidence towards active failure modes, and (2) a reasoning algorithm that combines set-covering and probabilistic computation to establish fault candidates and their rankings. However, for complex systems reference models are typically incomplete, and simplify- ing assumptions are made to make the reasoning algorithms tractable. Incompleteness in the reference models can take several forms, such as absence of discriminating evidence, and errors and incompleteness in the mapping between evidence and failure modes. Inaccuracies in the reasoning algorithm arise from the use of simplified noise models and independence assumptions about the evidence and the faults. Recently, data mining approaches have been proposed to help mitigate some of the problems with the reference models and reasoning schemes. This paper describes a Tree Augmented Na ̈ıve Bayesian Classifier (TAN) that forms the basis for systematically extending aircraft diagnosis reference models using flight data from systems operating with and without faults. The performance of the TAN models is investigated by comparing them against an expert supplied reference model. The results demonstrate that the generated TAN structures can be used by human experts to identify improvements to the reference model, by adding (1) new causal links that relate evidence to faults, and different pieces of evidence, and (2) updated thresholds and new monitors that facilitate the derivation of more precise evidence from the sensor data. A case study shows that this improves overall reasoner performance.
Feature Selection and Categorization to Design Reliable Fault Detection Systems
Authors: Senoussi H. , Chebel-Morello B. , Denaï M. , Zerhouni N.
Published: March 2024

In this work, we will develop a fault detection system which is identified as a classification task. The classes are the nominal or malfunctioning state. To develop a decision system it is important to select among the data collected by the supervision system, only those carrying relevant information related to the decision task. There are two objectives presented in this paper, the first one is to use data mining techniques to improve fault detection tasks. For this purpose, feature selection algorithms are applied before a classifier to select which measures are needed for a fault detection system. The second objective is to use STRASS (STrong Relevant Algorithm of Subset Selection), which gives a useful feature categorization: strong relevant features, weak relevant and/or redundant ones. This feature categorization permits to design reliable fault detection system. The algorithm is tested on real benchmarks in medical diagnosis and fault detection. Our results indicate that a small number of measures can accomplish and perform the classification task and shown our algorithm ability to detect the correlated features. Furthermore, the proposed feature selection and categorization permits to design reliable and efficient fault detection system.
Health Monitoring of an Auxiliary Power Unit Using a Classification Tree
Authors: O. L. Vianna Wlamir , P. P. Gomes João , K. H. Galvão Roberto , Yoneyama Takashi , P. Matsuura Jackson
Published: March 2024

The objective of this work is to present a method to monitor the health of Auxiliary Power Units (APU) using a Dynamic Computational Model, Gas Path Analysis and Classification and Regression Trees (CART). The main data used to train the CART consists of measurements of the exhaust gas temperature, the bleed pressure and the fuel flow. The proposed method was tested using actual APU data collected from a prototype aircraft. The method succeeded in classifying several relevant fault conditions. The few misclassification errors were found to be due to the insufficiency of the information content of the measurement data.
Integrating Probabilistic Reasoning and Statistical Quality Control Techniques for Fault Diagnosis in Hybrid Domains
Authors: Ricks Brian , Harrison Craig , J. Mengshoel Ole
Published: March 2024

Bayesian networks, which may be compiled to arithmetic circuits in the interest of speed and predictability, provide a probabilistic method for system fault diagnosis. Currently, there is a limitation in arithmetic circuits in that they can only represent discrete random variables, while important fault types such as drift and offset faults are continuous and induce continuous sensor data. In this paper, we investigate how to handle continuous behavior while using discrete random variables with a small number of states. Central in our approach is the novel integration of a method from statistical quality control, known as cumulative sum (CUSUM), with probabilistic reasoning using static arithmetic circuits compiled from static Bayesian networks. Experimentally, an arithmetic circuit model of the ADAPT Electrical Power System (EPS), a real- world EPS located at the NASA Ames Research Center, is considered. We report on the validation of this approach using PRODIAGNOSE, which had the best performance in three of four industrial track competitions at the International Workshop on Principles of Diagnosis in 2009 and 2010 (DXC-09 and DXC-10). We demonstrate that PRODIAGNOSE, augmented with the CUSUM technique, is successful in diagnosing faults that are small in magnitude (offset faults) or drift linearly from a nominal state (drift faults). In one of these experiments, detection accuracy dramatically improved when CUSUM was used, jumping from 46.15% (CUSUM disabled) to 92.31% (CUSUM enabled).
Model-Based Prognostics Under Non-stationary Operating Conditions
Authors: Gašperin Matej , Boškoski Pavle , Juričić Dani
Published: March 2024

The paper presents a novel approach for prognostics of faults in mechanical drives under non-stationary operating conditions. The feature time series is modeled as an output of a dynamical state-space model, where operating conditions are treated as known model inputs. An algorithm for on-line model estimation is adopted to find the optimal model at the current state of failure. This model is then used to determine the presence of the fault and predict the future behavior and remaining useful life of the system. The approach is validated using the experimental data on a single stage gearbox.
Symbolic Dynamics and Analysis of Time Series Data for Diagnostics of a dc-dc Forward Converter
Authors: M. Bower Gregory , Mayer Jeffrey , Reichard Karl
Published: March 2024

This paper presents a novel approach to diagnosis of dc-dc converters with application to prognosis. The methodology is based on Symbolic Dynamics and Diagnostics. The data derived method builds a statistical baseline of the converter that is used to compare future statistical models of the converter as it degrades. Methods to determine the partitioning and number of partitions for the Symbolic Dynamics algorithm are discussed. In addition, a failure analysis is performed on a dc-dc forward converter to identify components with a high probability of failure. These components are then chosen to be monitored during accelerated testing of the dc-dc forward converter. The methodology is experimentally validated with data recorded from two dc-dc converters under accelerated life testing.
Utilizing Dynamic Fuel Pressure Sensor For Detecting Bearing Spalling and Gear Pump Failure Modes in Cummins Pressure Time (PT) Pumps
Authors: Scott Pflumm J. , C. Banks Jeffrey
Published: March 2024

The objective of this paper is to highlight the results of the fault detection investigation conducted to ascertain the feasibility of exploiting the existing on-board M2/M3 Bradley fuel pressure sensor for the purpose of detecting mechanical bearing spalling and gear pump failure modes of the pressure-time (PT) fuel pump used on the Cummins VTA-903T engine. To investigate this fluid-mechanical cross domain detection approach, a Bradley fuel system test bed was built. Fault tests for four PT pump failure modes were conducted including bearing faults, gear pump fault, idle adjust miscalibration, and air-fuel control fault. The results of the first two fault tests are summarized in this paper. Due to limited number of pumps available for testing (2), these preliminary findings are not statistically substantiated. With this stated, the findings present a method for investigating the presence of a narrowband frequency-domain-based predictive fault detection capability using the existing pressure sensor installed on the Chassis Modernization and Embedded Diagnostics (CMED) variant Bradley. The test stand based seeded fault analysis was not capable of detecting an 0.080 inch outer raceway bearing spall, but there is preliminary evidence to warrant further study that a nominal 0.001 inch foreign object debris accumulation on the gear teeth of the gear pump might be detectable using a simple kurtosis based calculation using a pressure sensor with a 0-500 Hz dynamic bandwidth.
Correction of Data Gathered by Degraded Transducers for Damage Prognosis in Composite Structures
Authors: R. Mulligan K. , Quaegebeur N. , Masson P. , Le ́tourneau S.
Published: March 2024

This paper presents an approach for the correction of data gathered for damage prognosis (DP) in composite structures. The validation setup consists of surface-bonded piezoceramic (PZT) transducers used in a Structural Health Monitoring (SHM) system with simulated bonding layer damage using Teflon masks. The modal damping around PZT mechanical resonance is used as a metric to assess and compensate for the degradation of the adhesive layer of the transducers. Modal damping is derived from electrical admittance curves using a lumped parameter model to monitor the degradation of the transducer adhesive layer. A Pitch-Catch (PC) configuration is then used to discriminate the effect of bonding degradation on actuation and sensing. It is shown that below the first mechanical resonance frequency of the PZT, degradation leads to a decrease in the amplitude of the transmitted and measured signals. Above resonance, in addition to a decrease in signal amplitude of the transmitted and measured signals, a slight linear phase delay is also observed. A Signal Correction Factor (SCF) is proposed to adjust signals based on adhesive degradation evaluated using the measured modal damping. The benefits of the SCF for prognostics feature generation are demonstrated in the frequency domain for the A0mode.
A Reasoning Architecture for Expert Troubleshooting of Complex Processes
Authors: Naveed Abdul , Li Jiaming , Saha Bhaskar , Saxena Abhinav , Vachtsevanos George
Published: March 2024

This paper introduces a novel reasoning methodology, in combination with appropriate models and measurements (data) to perform accurately and expeditiously expert troubleshooting for complex military and industrial processes. This automated troubleshooting tool is designed to support the maintainer/ repairman by identifying and locating faulty system components. The enabling technologies build upon a Model Based Reasoning paradigm and a Dynamic Case Based Reasoning method acting as the intelligent database. A case study employs a helicopter Intermediate Gearbox as the application domain to illustrate the efficacy of the approach.
Spare Parts Inventory Control for Non-Repairable Items Based on Prognostics and Health Monitoring Information
Authors: Ramos Rodrigues Leonardo , Yoneyama Takashi
Published: March 2024

The application of PHM (Prognostics and Health Monitoring) techniques can provide a wide range of benefits to aircraft operators. Since the primary goal of PHM systems is to estimate the health state of components and equipments, as well as forecasting their RUL (Remaining Useful Life), they are often closely associated with the reduction in the number of unscheduled maintenance tasks. Indeed, the avoidance of unscheduled maintenance is a very important factor, but this technology may potentially lead to considerable further savings in other fields. The usage of PHM information by the logistics team for spare parts inventory control is a good example to illustrate that a PHM system can potentially provide benefits for other teams besides the maintenance team. The purpose of this work is to present a comparison between two different inventory control policies for non-repairable parts in terms of average total cost required and service level achieved. The well known [R, Q] (re-order point, economic order quantity) inventory model will be used as a reference. This model will be compared with a model based on information obtained from a PHM system. Discrete event simulation will be used in order to simulate and assess the performance of both models.
Uncertainty of performance requirements for IVHM tools according to business targets
Authors: Esperon-Miguez Manuel , John Philip , Jennions Ian K.
Published: March 2024

Operators and maintainers are faced with the task of selecting which health monitoring tools are to be acquired or developed in order to increase the availability and reduce operational costs of a vehicle. Since these decisions will affect the strength of the business case, choices must be based on a cost benefit analysis. The methodology presented here takes advantage of the historical maintenance data available for legacy platforms to determine the performance requirements for diagnostic and prognostic tools to achieve a certain reduction in maintenance costs and time. Theeffect of these tools on the maintenance process is studied using Event Tree Analysis, from which the equations are derived. However, many of the parameters included in the formulas are not constant and tend to vary randomly around a mean value (e.g.: shipping costs of parts, repair times), introducing uncertainties in the results. As a consequence the equations are modified to take into account the variance of all variables. Additionally, the reliability of the information generated using diagnostic and prognostic tools can be affected by multiple characteristics of the fault, which are never exactly the same, meaning the performance of these tools might not be constant either. To tackle this issue, formulas to determine the acceptable variance in the performance of a health monitoring tool are derived under the assumption that the variables considered follow Gaussian distributions. An example of the application of this methodology using synthetic data is included.
Virtual Framework for Validation and Verification of System Design Requirements to enable Condition Based Maintenance
Authors: Mikat Heiko , Siddiolo Antonino Marco , Buderath Matthias
Published: March 2024

During the last decade Condition Based Maintenance [CBM] became an important area of interest to reduce maintenance and logistic delays related down times and improve system effectiveness. Reliable diagnostic and prognostic capabilities that can identify and predict incipient failures are required to enable such a maintenance concept. For a successful integration of CBM into a system, the challenge beyond the development of suitable algorithms and monitoring concepts is also to validate and verify the appropriate design requirements. To justify additional investments into such a design approach it is also important to understand the benefits of the CBM solution. Throughout this paper we will define a framework that can be used to support the Validation & Verification [V&V] process for a CBM system in a virtual environment. The proposed framework can be tailored to any type of system design. It will be shown that an implementation of failure prediction capabilities can significantly improve the desired system performance outcomes and reduce the risk for resource management; on the other hand an enhanced online monitoring system without prognostics has only a limited potential to ensure the return on investment for developing and integrating such technologies. A case study for a hydraulic pump module will be carried out to illustrate the concept.
A Prognostic Approach Based on Particle Filtering and Optimized Tuning Kernel Smoothing
Authors: Hu Yang , Baraldi Piero , Maio Francesco Di , Zio Enrico
Published: March 2024

This paper proposes a novel approach based on a Particle Filtering technique and an Optimized Tuning Kernel Smoothing method for the prediction on the Remaining Useful Life (RUL) of a degrading component. We consider a case in which a model describing the degradation process is available, but the exact values of the model parameters are unknown and observations of historical degradation trajectories in similar components are unavailable. A numerical application concerning the prediction of the RUL of degrading Lithium-ion batteries is considered. The obtained results show that the proposed method can provide a satisfactory RUL prediction as well as the parameters estimation.
Architectures and Key Points for Implementation of E-maintenance Based on Intelligent Sensor Networks
Authors: Katsikas Serafeim , Routzomanis Apostolos , Mermikli Konstantina , Dimas Dimitrios , Koulamas Christos , Katsouros Vasilis , Emmanouilidis Christos
Published: March 2024

During the past few years industrial predictive maintenance has benefited from new developments in hardware and software systems. A key conclusion is that to maximize results, these systems need to be smarter with learning capabilities. Moreover, wireless sensor networks have led to a new revolution in the field of e-maintenance, offering new possibilities in measurement collection, aiming to empower monitoring with more advanced features. In what way can wireless sensor networks be applied to industrial maintenance? How can novelty detection be implemented on these systems? How can such systems scale up to offer distributed intelligence? This paper presents the WelCOM research program’s approach on the aforementioned matters answering many questions that relate to intelligent sensor systems in the field of e-maintenance and proposing flexible architectures for the implementation of these systems.
A Model-based Approach to Detect an Under-Lubricated Condition in a Ball Bearing
Authors: Sreenilayam-Raveendran Ranjith-Kumar , H.Azarian Michael , Pecht Michael
Published: March 2024

Bearings with an insufficient amount of lubricant can lead to early field failures, especially in applications which fail due to lubricant degradation, such as cooling fans used for thermal management of electronics. A reduced amount of lubricant can accelerate the wear process in the bearing, since there is not enough lubricant film thickness to support the operating load on the bearing. Qualification of bearings in cooling fans is carried out by time-truncated tests, where cooling fans have to operate without failure for a pre-determined period of time. Under-lubricated bearings can survive without failure in these tests leading to the usage of these bearings in the field resulting in field returns and warranty claims.A non-linear dynamic model of a ball bearing is developed to simulate the transfer of load from the inner race to the outer race of the bearing as well as the acceleration signal as a function of time. An under-lubricated bearing condition is simulated in this model by changing the load transmitted to the outer race due to the reduced amount of lubricant. The simulated acceleration signal of the under-lubricated bearing condition is compared with that from the normal bearing to develop a fault-characteristic feature. The changes observed in the fault-characteristic feature from the simulation is validated by comparing with that obtained from experiments conducted on bearings with varying amounts of grease, ranging from none to the nominal amount specified by the manufacturer. The vibration level of these bearings was monitored at various operating speeds during the experiment. The changes observed in the fault-characteristic feature from the experiment due to a reduction of the lubricant in the bearing were similar to that observed in the simulations. This study resulted in the development of an experimental methodology and a fault-characteristic feature which can be used as a method for rapid acceptance testing of bearings. The dynamic model developed in this study can be used to determine the fault-characteristic feature for any bearing design.
Robust Passive Fault Tolerant Control Applied to Jet Engine Equipment
Authors: SOUAMI Y. , MECHBAL N. , ECOUTIN S.
Published: March 2024

In order to minimize the occurrence of unexpected costly flight failures modern aircraft engines industry focuses especially on increasing product’s availability. In this work, we propose to monitor the health of a Variable Stator Vane (VSV), subsystem controlling the amount of airflow through the High Pressure Compressor (HPC), allowing optimum compressor performance. This control of airflow prevents the engine from stalling. The proposed methodology is based on an original approach for real time on-board Passive Fault Tolerant Control (PFTC). The objective of the proposed PFTC is to provide acceptable performance and preserve stability when faults occur. The method relies on the design of a specific Robust Virtual Sensor in a Linear Parameter Variable (LPV) polytopic framework. The robustness to model uncertainties is ensured by a Neural Extended Kalman Filter (NEKF) accommodating, in real time, the model prediction. In the proposed methodology, an off-line closed-loop identification scheme is first used to elaborate a multi local linear state space models, after that a multi-model observer based on Linear Matrix Inequalities (LMI) optimization is used to build the virtual sensor. The NEKF is added to circumvent online model accuracy problems. The efficiency and limit of the approach are shown and discussed through simulations on a complete numerical engine test bench.
Unsupervised Kernel Regression Modeling Approach for RUL Prediction
Authors: Khelif Racha , Malinowski Simon , Chebel - Morello Brigitte , Zerhouni Noureddine
Published: March 2024

Recently, Prognostics and Health Management (PHM) has gained attention from the industrial world since it aims at increasing safety and reliability while reducing the maintenance cost by providing a useful prediction about the Remaining Useful Life (RUL) of critical components/system. In this paper, an Instance-Based Learning (IBL) approach is proposed for RUL prediction. Instances correspond to trajectories representing run-to-failure data of a component. These trajectories are modeled using Unsupervised Kernel Regression (UKR). A historical database is used to learn a UKR model for each training unit. These models fuse the run-tofailure data into a single feature that evolves over time and hence allow the construction of a library of instances. Whenunseen sensory data arrive, the learned UKR models are used to construct the test degradation trajectories. RUL is deduced by comparing the test degradation trajectory to the library of instance. Only the most similar train instances are kept for RUL prediction. The proposed approach was tested and compared to approaches that apply linear regression and PCA to model the library of instances. Results highlight the benefit of using UK compared to other approaches.
Fault Prognosis with Stochastic Modelling on Critical Points of Discrete Processes
Authors: Nguyen Thi-Bich-Lien , Djeziri Mohand , Ananou Bouchra , Ouladsine Mustapha , Pinaton Jacques
Published: March 2024

The primary role of a machine tool is produce the good quality parts, but a machine tool goes always through a process of degradation and wear which will affect the accuracy and precision of machining and the quality of products. Therefore, monitoring the degradation of machine tool and quantifying its health is very important. The degradation level of a machine can be qualified by an index which is called health indicator (HI). Based on the HI, fault prognosis can provide the Remaining Useful Life (RUL) of machine which is useful for an effective maintenance policy, thus, that helps to increase efficiency of operations and manufacturing. However, the HI is not usually predetermined in most Discrete Manufacturing Processes (DMP). This paper presents a new method of HI extraction based on the degradation reconstruction. The HI is then modeled with a stochastic process. For the online supervision, the RUL is estimated for each inspection time.
Investigation of an Indicator for On-line Diagnosis of Polymer Electrolyte Membrane (PEM) Fuel Cell Flooding using Model Based Techniques
Authors: Mao Lei , Jackson Lisa , Dunnett Sarah , Vasilyev Andrey
Published: March 2024

The durability and reliability of producing high quality power for long periods of time have the potential to be the leading marketing factors for future hydrogen and fuel cell power sources. In the past few decades, several researchers have been devoted to investigating diagnostic techniques for fuel cell systems. However, in commercial fuel cell applications, on-line diagnosis is urgently required so that fuel cell degradation can be detected at its early stage, and mitigation strategies can be performed to recover fuel cell performance. In this paper, on-line diagnosis of fuel cell flooding is investigated. For this purpose, a generalised fuel cell stack model is developed, and water mass balance equations are used to study water balance inside the fuel cell stack. Moreover, with these equations, the flooding indicator is proposed and its relationship with liquid water inside the stack is evaluated. Results demonstrate that the proposed indicator is sensitive to the liquid water in the stack, thus can be used for flooding diagnosis. Furthermore, the expectation of the proposed indication in the cell flooding case is also presented. The advantage of this method is that parameters in the flooding indicator can be determined with measurements from tests, thus quick diagnosis can be made during the practical fuel cell operation.
Linear Polarization Resistance Sensor Using the Structure as aWorking Electrode
Authors: Brown Douglas W. , Connolly Richard J. , Darr Duane R. , Laskowski Bernard
Published: March 2024

A direct method of measuring corrosion on a structure using a micro-linear polarization resistance (mLPR) sensor is presented. The sensor includes three electrodes, where each electrode is fabricated on a flexible substrate to create a circuit consisting of gold-plated copper. The first two electrodes, or the counter and reference electrodes, are configured in an interdigitated fashion with a separation distance of 8mil. The flex cable contains a porous membrane between the pair of electrodes and the structure. A third electrode, or the working electrode makes electrical contact to the structure through a 1mil thick electrically conductive transfer tape placed between the electrode and structure. The reference and counter electrodes are electrically isolated from the working electrode and physically separated from the surface of the structure by 1mil. The flex cable can be attached to the structure through the use of adhesives or in the case of placement in a butt joint or lap joint configuration, by the joint itself. Corrosion is computed from known physical constants, by measuring the polarization resistance between the electrolytic solution and the structure. A controlled experiment using the ASTM G85 Annex 5 standard verifies the precision and accuracy of sensor measurements by comparing the estimated mass loss with witness coupons.
A combined framework for automated diagnosis and prognosis based on surrogate modelling and a particle filter
Authors: Sbarufatti Claudio , Cadini Francesco , Locatelli Andrea , Giglio Marco
Published: March 2024

Bayesian approaches have proven to be successful in prognostic health monitoring, especially for inverse problem solutions, such as the estimation of damage evolution model parameters from some damage dependent observations. In practice, they stem from the evaluation of the posterior distribution of a vector of parameters of interest conditioned on the observation of some signal features, which relies on the direct calculation of the observation likelihood, i.e. a measure of the probability that the observations are associated to some realizations of the system parameter vector. However, for realistic structures, a numerical simulation might be required for the evaluation of each sample likelihood, which can make the whole procedure for posterior pdf estimation computationally unfeasible.
In this work, this problem is addressed by leveraging on surrogate modelling. Particle filter is used as a general framework for the combined health state estimation and prognosis of a skin panel subject to fatigue crack growth, while observing the strain field pattern acquired at some specific locations. A surrogate model consisting of an artificial neural network, trained on a set of analytical simulations, is used to predict the strain as a function of the crack position and length, thus allowing a fast calculation of the strain observation likelihood. The algorithm is tested with an analytic case study of a crack propagating in an infinite plate, allowing for a simultaneous diagnosis of the crack position and length, as well as a real-time updating of the evolution model parameters and system prognosis.
Experimental Validation of Multi-Sensor Data Fusion Model for Railway Wheel Defect Identification
Authors: Alemi Alireza , Pang Yusong , Lodewijks Gabriel
Published: March 2024

Wheel defects are detrimental for railway train and track components and should be detected and identified as early as possible. Wheel Impact Load Detector (WILD) is a commercial condition monitoring system used for detecting the defective wheels. This system usually measures the rail strain at different points by multiple sensors. WILD converts the measured strains to the force and uses the peak force, dynamic force, and ratio of the peak force to the static force to estimate the condition of the in-service wheels. These methods are useful for detecting the severe defects contributing to the contact force to the extent that exceed a predetermined threshold. Therefore, in the prior research a fusion method has been developed to reconstruct a new informative pattern from the data collected by the multiple sensors. The reconstructed pattern provides a comprehensive description of the wheel condition. This paper validates the fusion method using a set of lab tests to investigate the applicability of the proposed method. For this purpose, a test rig has been built consisting of a circular rail, a rotating arm, and a wheel. Six strain sensors have been installed under the rail in the symmetric locations over the rail circle with 60 degree intervals. The fusion method used to reconstruct a signal from the bending strain signals measured by the multiple sensors. Different wheel defects including the flat and out-of-round wheels have been tested and the results validated the fusion method by providing informative patterns.
US Air Force Research Laboratory Perspective on Structural Health Monitoring in Support of Risk Management
Authors: Lindgren Eric
Published: March 2024

The US Air Force (USAF) and US Department of Defense has a long history of research and development in the exploration of on-board sensors being used for detection of damage in aircraft structures. Initial activities can be traced to the early 1980’s which led to an extensive on-aircraft assessment of acoustic emission based (i.e. “passive) sensor system. In the late 1990’s an effort was launched to revitalize the capability which cumulated in the “Hot Spots” program which explored the use of an ultrasonic guided wave (i.e. “active”) sensor system. Each of these programs encountered challenges that have hindered the use of these technologies on fixed-wing military aircraft. This paper briefly reviews these previous efforts, present current USAF Military Standards that define Structural Health Monitoring (SHM) for fixed wing aircraft, and provide a discussion of current and future concepts for research and development to resolve these challenges and enable eventual adaptation of SHM for fixed-wing applications. This will include a summary of current initiatives within the Materials and Manufacturing Directorate of AFRL and notional thoughts on potential projects for future developments required for this capability to be applied to fixed wing military aircraft.
Pricing full-service maintenance contracts: a data analytics approach
Authors: Deprez Laurens , Antonio Katrien , Boute Robert
Published: March 2024

A full-service maintenance contract covers all futurecosts of both preventive and corrective maintenanceover a predetermined time horizon in exchange fora fixed upfront price. Due to the stochastic natureof the maintenance costs the determination of thecorrect break-even price of such a contract is a keychallenge. We set out a data-driven methodologyto provide insight in the future maintenance costswithin a full-service contract. This methodology in-volves building predictive models for the frequencyof failure and the associated costs taking into accountmachine and customer characteristics. Not only willour approach lead to a break-even price driven bythe analysis of relevant historical data, it also leadsto a classification of the customer base. This classi-fication may in turn enable price discrimination offuture service contracts.