bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Bayesian Networks for Prognosis
Authors: Bartram Gregory , Mahadevan Sankaran
Published: March 2024

In this paper, a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN) is proposed. Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of formats from various sources and give a probabilistic representation of a system. Further, DBNs provide a platform naturally suited for seamless integration of diagnosis, uncertainty quantification, and prediction. In the proposed methodology, a DBN is used for online diagnosis via particle filtering, providing a current estimate of the joint distribution over the system variables. From this state estimate, future states of the system are predicted using the DBN and sequential Monte Carlo sampling. Prediction in this manner provides the necessary information to estimate the distribution of remaining use life (RUL). The DBN-based recursive prediction procedure may be used to estimate the system state between available measurements, when filtering is not possible. The prognosis procedure, which is system specific, is validated using a suite of offline hierarchical metrics. The prognosis methodology is demonstrated on a hydraulic actuator subject to a progressive seal wear that results in internal leakage between the chambers of the actuator.
A Method for Anomaly Detection for Non-stationary Vibration Signatures
Authors: Klein Renata
Published: March 2024

Vibration signatures contain information regarding the health status of the machine components. One approach to assess the health of the components is to search systematically for a list of specific failure patterns, based on the physical specifications of the known components (e.g. the physical specifications of the bearings, the gearwheels or the shafts). It is possible to do so, since the manifestation of the possible failures in the vibration signature is known a priory. The problem is that such a list is not comprehensive, and may not cover all possible failures. The manifestation of some failure modes in the vibration signature may be less investigated or even unknown. In addition, when more than one component is malfunctioning, unexpected patterns may be generated. Anomaly detection tackles the more general problem: How can one determine that the vibration signatures indicate abnormal functioning when the specifics of the abnormal functioning or its manifestation in the vibration signatures are not known a priori? In essence, anomaly detection completes the diagnostics of the predefined failure modes. In many complex machines (e.g. turbofan engines), the task of anomaly detection is further complicated by the fact that changes in operating conditions influence the vibration sources and change the frequency and amplitude characteristics of the signals, making them non-stationary. Because of that, joint time-frequency representations of the signals are desired. This is different from other vibration based diagnostic techniques, which are designated for stationary signals, and often focus on either the time domain or the frequency domain.

For the purpose of this article, we will refer as TFR (time- frequency representation) to all 3D representations which employ on one axis either time, or cycles, or RPM, and on the other axis either frequency, or order. The proposed method suggests a solution for anomaly detection by analysis of various TFRs of the vibration signals (primarily

the RPM-order domain).In the first stage, TFRs of healthy machines are used to create a baseline. The TFRs can be obtained using various methods (Wigner-Ville, wavelets, STFT, etc). In the next stage, the distance TFR between the inspected recording and the baseline is computed. In the third stage, the distance TFR is analyzed and the exceptional regions in the TFR are found and characterized. A basic classification of the anomaly type is suggested. The different stages of analysis: creating baselines, computing the distance TFR, identifying the exception regions, are illustrated with actual data.
Efficient Generation of Minimal Dynamic Bayesian Networks for Hybrid Systems Fault Diagnosis using Hybrid Possible Conflicts
Authors: Pulido Belarmino , Moya Noemi , J. Alonso-Gonza ́lez Carlos , Bregon Anibal
Published: March 2024

Hybrid systems diagnosis requires different sets of equations for each operation mode in order to estimate the continuous system behaviour. In this work we rely upon Hybrid Possible Conflicts (HPCs), which are an extension of Possible Conflicts (PCs) for hybrid systems, that introduce the information about potential system modes as control specifications that activate/deactivate different sets of equations. We also introduce the concept of Hybrid Minimal Evaluation Models (H-MEMs) to represent the set of globally consistent causal assignments in an HPC for any potential mode.

H-MEMs can be explored for a specific operation mode, and its computational model automatically generated. In this work, the selected computational models are minimal Dynamic Bayesian Networks (DBNs). Since DBNs can be directly generated from PCs, and can be used for fault detection and isolation, we propose to efficiently generate Mini- mal DBNs models on-line using the H-MEM structure. By introducing fault parameters in the DBN model, we can also perform fault identification, providing an unifying framework for fault diagnosis, under single fault assumption. We test the approach in a simulation four-tank system.
Uncertainty in Impact Identification Applied to a Commercial Wind Turbine Blade
Authors: M Bond Raymond , E Adams Douglas
Published: March 2024

This work evaluates the uncertainty of impact force and location estimates using an entropy-based impact identification algorithm applied to a commercial wind turbine blade. The effects of sensor placement, measurement directions and distance between impacts and sensor locations are studied. Results show that impacts to a 35m long wind turbine blade can be accurately located using a single tri-axial accelerometer regardless of sensor location. Uncertainties in impact force estimates are consistent across sensor locations. When omitting acceleration information in the spanwise direction, the bias and variance of force estimates is consistent, but when a single chan- nel of acceleration data is used, both increase somewhat. Impact force identification error was found to be uncorrelated with the distance between the impact and sensor location. The entropy of the estimated force time history, an indicator of the impulsivity of the estimate, was found to be a good indicator of the quality of force estimate. The bias and variance of impact force estimation error was found to be directly correlated with the entropy of the impact force estimate. When considering validation test data from all possible sensor configurations, the entropy of the recreated force estimates was a better indicator of the force magnitude prediction interval than was the specific sensor configuration. By classifying impact force estimates based upon entropy values, impact force prediction intervals were more precisely determined than when all validation impact data were considered at once.
Physics-Based Prognostics for LCF Crack Nucleation Life of IMI 685 Aero-engine Compressor Disc
Authors: Banerjee Avisekh , Zhao Jun , K Koul Ashok , Kumar Amar , Srivastava Alka , Goel Nita
Published: March 2024

A life cycle management-expert system (LCM-ES) framework is employed in this work for physics-based prognostics of a compressor disc. The modeling approach involves the integration of both global behavior and localized response of component at the microstructural level. This paper presents the results of a low cycle fatigue (LCF) case study for a near alpha titanium alloy (IMI 685) high pressure compressor disc using a microstructure based damage model and finite element analysis results. Both deterministic and probabilistic crack nucleation lives are determined at the two critical locations. The lognormal distributions of α-grain structure of IMI685 and hard alpha (HA) inclusions is considered in the probabilistic analysis, while the deterministic life is predicted based on their extreme values that would represent the worst life. In the LCF modeling, the plastic strain estimation assumes an empirical coefficient that has a strong dependence on the alpha grains and defect size. The proposed life prediction model is capable of capturing the effect of the grain size and hard alpha particle density variation on the LCF crack nucleation life. The worst case deterministic life corresponds well with 0.1% probability of failure and lie around 3542 and 4710 cycles respectively for the primary fracture critical location in the disc.
Prognostics Health Management for Advanced Small Modular Reactor Passive Components
Authors: M. Meyer Ryan , Ramuhalli Pradeep , B. Coble Jamie , R. Mitchell Mark , W. Wootan David , H. Hirt Evelyn , J.Berglin Eric , J. Bond Leonard , H. Henager Chuck
Published: March 2024

In the United States, sustainable nuclear power to promote energy security is a key national energy priority. Advanced small modular reactors (AdvSMR), which are based on modularization of advanced reactor concepts using non- light-water reactor (LWR) coolants such as liquid metal, helium, or molten salt, may provide a longer-term alternative to more conventional LWR-based concepts. The economics of AdvSMRs will be impacted by the reduced economy-of-scale savings when compared to traditional LWRs and the controllable day-to-day costs of AdvSMRs are expected to be dominated by operations and maintenance costs. Therefore, achieving the full benefits of AdvSMR deployment requires a new paradigm for plant design and management. In this context, prognostic health management of passive components in AdvSMRs can play a key role in enabling the economic deployment of AdvSMRs. This paper discusses features of AdvSMR systems that are likely to influence PHM implementation for passive components and discusses some requirements based on those features. Further, a brief overview of the state-of- the-art in PHM relevant to AdvSMR passive components is provided followed by an illustration of prognostics for passive AdvSMR components.
Detection of CH-53 swashplate bearing deformation - from a 3D dynamic model to diagnostics
Authors: Battat Mor , Kogan Gideon , Kushnirsky Alex , Klein Renata , Bortman Jacob
Published: March 2024

The purpose of this paper is to present the research approach for the development of an algorithm for detection of a failure of the CH-53 swashplate bearing external spacer. The failure causes a lack of support of the swashplate bearing, thus creating a deformation of the outer ring. This study integrates the results of a new 3D dynamic model, developed for assessment of the defect pattern, and results from experiments. The research approach is planned in hierarchical phases. The experimental phases include a small scale specimen, full scale test rig, helicopter blades test facility and finally a CH-53 helicopter. The unique approach gradually simulates the real work environment of the swashplate bearing. The first two experimental phases and their results are presented. The first experimental phase is conducted on a small scale specimen and the second phase on a full scale test rig. Model results indicate that the lack of support has a defect pattern in both the radial and axial directions. These results are validated with the small scale specimen. In the future phases, the algorithm will be validated with data from the helicopter blades test facility and CH-53 helicopter.
Multivariate Fault Detection using Vector Autoregressive Moving Average and Orthogonal Transformation in Residual Space
Authors: Serdio Francisco , Lughofer Edwin , Pichler Kurt , Buchegger Thomas , Pichler Markus , Efendic Hajrudin
Published: March 2024

We propose the use of multivariate orthogonal space transformations and Vector Autoregressive Moving-Average (VARMA) models in combination with data-driven system identification models to improve residual-based approaches to fault detection in rolling mills. Introducing VARMA models allows us to build k-step ahead multi-dimensional prediction models including the time lags that best explain the target. Multivariate orthogonal space transformations pro- vide estimates for the dynamical parameters by rewriting the equation set of the system at hand, decomposing the measured data into process and residuals spaces. Modeling in the process space then produces much more accurate models due to dimensionality (noise) reduction. Since we use an unsupervised scheme that requires a priori neither annotated samples nor fault patterns/models, both model identification and fault detection are based solely on the on-line recorded data streams. Our experimental results demonstrate that our approach yields improved Receiver Operating Characteristic (ROC) curves than methods that do not employ vector autoregressive moving-average models and multivariate orthogonal space transformations.
Performance Metrics in the Perspective of Prognosis Uncertainty
Authors: P. Lea ̃o Bruno , Yoneyama Takashi
Published: March 2024

The subject of uncertainty in failure prognosis, including the importance of estimating and managing it, is a recurring topic in PHM literature. Considering that the prognosis task comprises forecasting, this could not be any different. However, prognosis performance metrics proposed in literature are usually concerned with measuring adherence to requirements, but not the adequate representation of the true uncertainty that arises from various sources in a prognosis problem. This paper presents statistically sound means for evaluating the performance of prognosis methods in the perspective of comparing the true uncertainty to its estimates. This provides a useful yet simple framework for failure prognosis performance evaluation.
Fault Monitoring Techniques for Nuclear Components
Authors: Park Gee-Yong , Taek Kim Jung
Published: March 2024

In this paper, we describe our previous studies for the development of an analysis algorithm and the application of a fault monitoring technique. Various signal processing methods have been implemented in the so-called monitoring tools to monitor and analyze abnormal conditions of components in nuclear power plants (NPPs). One of the analysis methods were devised by us for the efficient analysis of transient signals from NPP process components. This method, the adaptive cone-kernel distribution, is presented in this paper along with the description of the monitoring tool. Then, some application results using the monitoring tool are presented. As another application, the fault monitoring technique applied to the agitator driving system of a thermal chemical reduction reactor is also presented though this technique is not integrated in the monitoring tool yet.
Detection of Wind Turbine Power Performance Abnormalities Using Eigenvalue Analysis
Authors: Alexandros Skrimpas Georgios , Walsted Sweeney Christian , S. Marhadi Kun , Bech Jensen Bogi , Mijatovic Nenad , Holbøll Joachim
Published: March 2024

Condition monitoring of wind turbines is a field of continuous research and development as new turbine configurations enter into the market and new failure modes appear. Systems utilising well established techniques from the energy and industry sector, such as vibration analysis, are commercially available and functioning successfully in fixed speed and variable speed turbines. Power performance analysis is a method specifically applicable to wind turbines for the detection of power generation changes due to external factors, such as icing, internal factors, such as controller malfunction, or deliberate actions, such as power de-rating. In this paper, power performance analysis is performed by sliding a time-power window and calculating the two eigenvalues corresponding to the two dimensional wind speed - power generation distribution. The power is classified into five bins in order to achieve better resolution and thus identify the most probable root cause of the power deviation. An important aspect of the proposed technique is its independence of the power curve provided by the turbine manufacturer. It is shown that by detecting any changes of the two eigenvalues trends in the five power bins, power generation anomalies are consistently identified.
A Time-domain Modeling and Simulation Framework for Comparative Analysis of Prognostics, Reliability and Robustness in System Design
Authors: A. Lambert Nicholas , B. Ferrio Kyle , L. Goodman Douglas
Published: March 2024

Redundancy is an effective, high-level solution to the requirement for reliable safety-critical systems, but it comes at the cost of Size, Weight and Power (SWaP) and reduced capability. A modeling and simulation framework was developed to address the need for robust design alternatives to redundancy. Robustness, in our application, is treated as the insensitivity of the performance with reference to specification. The necessity to characterize both reliability and robustness in the same framework has resulted in a time-domain simulation approach to modeling behaviors associated with unreliability and a lack of robustness. The incorporation of these features offers a novel insight into potential applications of prognostic technology. Further development of this approach has the potential to allow designers to choose how risks associated with failures are mitigated, by redundancy, robustness, or prognosis.

By modeling the life of parts, the factors that impact them and the resulting behaviors, the observability and predictability (even controllability in the case of optimized, fault-tolerant, closed-loop control) of faults and failures is identified. Designers can determine which parts of a system would benefit from prognostic health management (PHM) technologies, adaptive / tolerant features to yield robust design, or redundancy based approaches. The complex causality in the models requires a Monte Carlo approach analogous to the simulation of fleets of systems; this, combined with the ability to simulate systems made from new and old parts, can inform strategies for condition-based maintenance (CBM). We present the mathematical modeling concept and the simulation framework which permits comparative assessment of reliability, robustness and prognostics. The multi-hierarchical, systems integration aspects inherent to the concept make this technique highly applicable to real- world dynamic systems. The framework also supports statistical, standards based and physics-of-failure descriptions of stress, aging, fault and failure behaviors in a unified way. There are challenges to be overcome in realizing the benefits of this approach to model-based system design. Issues of model validation, data availability and computational burden are recognized and discussed. As we show, these challenges can be overcome to produce new design tools providing better products and transparent project quality.
Eddy Currents Signatures Classification by Using Time Series: a System Modeling Approach
Authors: Gue ́pie ́ Blaise , Petreczky Mihaly , Lecoeuche Ste ́phane
Published: March 2024

Non destructive testing methods are often used in order to de- tect and classify structural flaws. The detection of structural flaws is useful for maintenance. In this paper we propose to classify flaws in ferromagnetic materials by measuring Eddy currents. Our approach consists of two steps. First, we use a system identification algorithm to find a dynamical system which describes the data. Then, we use the parameters of this dynamical system as a feature vector and we use support vector machines in order to classify the various cracks. We test our method on a well-known benchmark.
Operation Condition Monitoring using Temporal Weighted Dempster-Shafer Theory
Authors: Wang Xiaoyun , Zhao Tingdi
Published: March 2024

System operation is a real time, dynamic decision process, a continuous observation should be implemented to support timely decision. Real time condition monitoring and diagnosis is featured with ongoing event sequence. The more recent observation, the much detailed, accurate information, and the more obsolete observations with much weak correlation to current faults and errors vise versa.

Dempster-Shafer evidence theory is best suitable for the problem of redundant sensors, insufficient data reasoning. However, D-S base applications largely focused on causational relationship between symptoms and effects, and the fusion process of evidences was performed regardless whatever order observed. As an improvement to the frame of discernment of the D-S theory, we purposed a time weighted evidence combination method. Observed events were extracted from multiple time points to form a temporal evidence sequence. Basic probability assignment was altered by temporal weights in accordance with the time proximity between the observed events and current time. The temporal weights value set was in accordance with its occurring time point. Evidences with same timestamps should be allocated with the same temporal weights. An example was discussed to illustrate the temporal weight, D-S rule based assessment framework. In the framework, latest observed evidences stream were combined into the framework to improving fault recognition.
Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton
Authors: Traore Moussa , Chaˆtelet Eric , Soulier Eddie , A. Gabbar Hossam
Published: March 2024

Nowadays, determining faults (or critical situations) in non- stationary environment is a challenging task in complex systems such as Nuclear center, or multi-collaboration such as crisis management. A discrete event system or a fuzzy discrete event system approach with a fuzzy role-base may re- solve the ambiguity in a fault diagnosis problem especially in the case of multiple faults (or multiple critical situations). The main advantage of fuzzy finite state automaton is that their fuzziness allows them to handle imprecise and uncertain data, which is inherent to real-world phenomena, in the form of fuzzy states and transitions. Thus, most of approaches proposed for fault diagnosis of discrete event systems require a complete and accurate model of the system to be diagnosed. However, in non-stationary environment it is hard or impossible to obtain the complete model of the system. The focus of this work is to propose an evolving fuzzy discrete event system whose an activate degree is associated to each active state and to develop a fuzzy learning diagnosis for incomplete model. Our approach use the fuzzy set of output events of the model as input events of the diagnoser and the output of a fuzzy system should be defuzzified in an appropriate way to be usable by the environment.
Validation of Model-Based Prognostics for Pneumatic Valves in a Demonstration Testbed
Authors: S. Kulkarni Chetan , Daigle Matthew , Gorospe George , Goebel Kai
Published: March 2024

Pneumatic-actuated valves play an important role in many applications. When valves are critical to the successful operation of the system, prognostics of these valves becomes extremely important and valuable. In order to facilitate the validation of prognostics algorithms for pneumatic valves, we have constructed a pneumatic valve testbed for use with a cryogenic propellant loading system. The testbed enables the injection of faults with a controllable fault progression pro- file. Specifically, we can introduce controllable pneumatic gas leaks, the most common faults associated with pneumatic valves. We focus on a valve that moves discretely between open and closed position, and is controlled through a solenoid valve. In this paper, we apply a model-based prognostics approach for pneumatic valves on the testbed. We demonstrate the approach using real experimental data obtained from the testbed.
Data-Driven Lead-Acid Battery Prognostics Using Random Survival Forests
Authors: Frisk Erik , Krysander Mattias , Larsson Emil
Published: March 2024

Problems with starter batteries in heavy-duty trucks can cause costly unplanned stops along the road. Frequent battery changes can increase availability but is expensive and sometimes not necessary since battery degradation is highly dependent on the particular vehicle usage and ambient conditions. The main contribution of this work is a case-study where prognostic information on remaining useful life of lead-acid batteries in individual Scania heavy-duty trucks is computed. A data- driven approach using random survival forests is proposed where the prognostic algorithm has access to fleet management data including 291 variables from 33603 vehicles from 5 different European markets. The data is a mix of numerical values such as temperatures and pressures, together with histograms and categorical data such as battery mount point. Implementation aspects are discussed such as how to include histogram data and how to reduce the computational complexity by reducing the number of variables. Finally, battery lifetime predictions are computed and evaluated on recorded data from Scania’s fleet-management system.
Characterization of Fault Size in Bearings
Authors: Mendelovich Matan , Sanders Yitschak , Kogan Gideon , Battat Mor , Klein Renata , Bortman Jacob
Published: March 2024

Bearings are important components in rotating machines. An initial small damage in the bearing may cause a fast degradation, which may lead to the machine breakdown. The health condition of bearings can be monitored using proven vibro-acoustic methods effective for detecting bearing faults. However, the existing bearing health indicators do not provide a reliable estimation of the fault characteristics, such as fault size and fault location. As a result, the ability to assess the severity of the bearing damage and to make maintenance decisions is limited.The presented study is a part of an ongoing research on bearing prognostics, aimed to improve the understanding of the effects of fault size on the bearing dynamics. The research methodology combines dynamic modeling of the faulty bearing with experimental validation and confirmation of model simulations.

In the presented study, small faults (starting from 0.3 mm), simulating incipient damage are generated at increasing sizes by an electrical discharge machine. The recorded vibration data is then analyzed and compared to the vibration signatures predicted by the model. The experimental and the simulation results add new insights on the manifestation of the size of the fault and possible indicators of the damage severity.
Equipment Health Monitoring with Non-Parametric Statistics for Online Early Detection and Scoring of Degradation
Authors: Mokhtar Maizura , C. Edge Joseph , R. Mills Andrew
Published: March 2024

This paper develops a health monitoring scheme to detect and trend degradation in dynamic systems that are characterised by multiple parameter time-series data. The presented scheme provides early detection of degradation and ability to score its significance in order to inform maintenance planning and consequently reduce disruption. Non-parametric statistics are proposed to provide this early detection and scoring. The non-parametric statistics approximate the data distribution for a sliding time window, with the change in distribution is indicated using the two-sample Kolmogorov-Smirnov test. Trending the changes to the signal distribution is shown to provide diagnostic capabilities, with deviations indicating the precursors to failure. The paper applies the equipment health monitoring scheme to address the growing concerns for future gas turbine fuel metering valve availability. The fuel metering unit within a gas turbine is a complex electro-mechanical system, failures of which can be a major source of airline dis- ruption. The application is performed on data acquired from a series of industrial tests performed on large civil aero-engine fuel metering units subjected to varying levels of contaminant. The data exhibits characteristics of degradation, which are identified and trended by the equipment health monitoring scheme presented in this paper.
A Reference Stack for PHM Architectures
Authors: Crabb Charles
Published: March 2024

This paper suggests a reference model for PHM processes that aids the customer of PHM in developing a business case for adopting PHM in his or her supply chain. Various PHM systems have been envisioned and developed in order to produce a prognosis of system or component behavior by collecting physical data from some section of a system, analyzing it and reporting the results to the entity that benefits from it, notably the supply chain that manages the components and receives the resulting cost benefit from PHM. All these systems have varying configurations that involve the collection of different types of data in different ways, the analysis of varying types of physical behavior and have different types of customers (different supply chain configurations). The customer needs to include the cost and complexity of the PHM system in his or her business model but has no formal standard to determine bounds on the complexity of the PHM system. Just as there are reference stacks for service-oriented architectures, this paper proposes a functional stack for PHM that can become a reference architecture for developing or purchasing a PHM system for an organization. The stack of PHM services ranges from the data acquisition layer through analysis functions to supply chain decision support services.
Distributed Fault Detection and Estimation for Cooperative Adaptive Cruise Control System in a Platoon
Authors: Abdollahi Biron Zoleikha , Pisu Pierluigi
Published: March 2024

Wireless vehicle to vehicle communication in a vehicle platoon is proposed in intelligent transportation systems to increase safety of the transportation system and assist drivers for improved decision making. However, similar to any networked system, cooperative connected vehicles in a platoon are vulnerable to malfunction due to failures in network communication. In addition to the possible data corruption, sensor and actuator faults can have significant effects on the control strategy for cruise control. This paper considers a platoon of connected vehicles equipped with cooperative adaptive cruise control and presents a reconstructive method based on sliding mode observer to estimate and reconstruct the faults in the sensors and actuators of vehicles.
Sliding Wear Particle Mass Distribution Assessment for Wear Mode Diagnosis
Authors: Raadnui Surapol
Published: March 2024

Condition based maintenance has been coming to the fore especially in recent decades and it is at the expense of conventional maintenance strategies. Wear particle tribology-based predictive maintenance is based on continuous monitoring, evaluating its condition and uses knowledge of technical diagnostics and prognostics. The use of sliding wear particle mass distribution as a means for distinguishing different modes of wear and determining its transitory behavior is evaluated in terms of the quantitative analysis of the multi-filtergram slides produced from a series of wear tests from a multiple point contact sliding wear tester. In this particular research, a four ball machine was used throughout. At the end of each test the wear debris generated was collected and then separated using a multi- filtergram maker which resulted in the wear debris being extracted due to their specific size ranges. Each filtergram patch of each specific size range was subsequently weighed to obtain “wear particle mass distribution” which in turn can be used to produce a histogram plot of the particle mass distribution. Various distribution functions have been fitted with the data. The results obtained from different sliding wear modes are presented; they confirm that changes in the mean and the variance of the selected statistical distributions provide clear indications of the type and extent of the sliding wear as it progresses.
D-matrix Based Fault Modeling for Cryogenic Loading Systems
Authors: Kodali Anuradha , Ponizovskaya-Devine Ekaterina , Robinson Peter , Luchinsky Dmitry , Bajwa Anupa
Published: March 2024

The study is motivated by NASA plans to develop technology for an autonomous cryogenic loading operation including online fault diagnostics as a part of Integrated Health Management system. For years, the diagnostic modeling effort is performed in many paradigms. None of these paradigms independently can provide a complete set of efficiency metrics: better diagnostics, lower run-time, etc. D-matrix, a causal 0-1 relationship between faults and tests, is proposed as a single representation between different model-based diagnostic methods for comparison and communication. This framework is suitable to create a common platform for communication via D-matrix for systems engineering process. The knowledge transfer between modeling techniques is done via D-matrix. In addition, D-matrix provides a common paradigm to compare the embedded knowledge and performance of heterogeneous diagnostic systems. D-matrix is generated from physics models to be used with faster run-time performance D-matrix based diagnostic algorithms. Additionally, we will also investigate if the derived D- matrix and thereby the physics model is sufficient and accurate for efficient diagnostics via iDME tool.
Device Health Estimation by Combining Contextual Control Information with Sensor Data and Device Health Prognostics Utilizing Restricted Boltzmann Machine
Authors: Liao Linxia , Honda Tomonori , Pavel Radu
Published: March 2024

The goal of this work is to bridge the gap between business decision-making and real-time factory data. Beyond real- time data collection, we aim to provide analysis capability to obtain insights from the data and converting the learnings into actionable recommendations.

For device health estimation, we focus on analyzing device health conditions and propose a data fusion method that com- bines sensor data with limited diagnostic signals with the de- vice’s operating context. We propose a segmentation algo- rithm that provides a temporal representation of the device’s operation context, which is combined with sensor data to fa- cilitate device health estimation. Sensor data is decomposed into features by time-domain and frequency-domain analy- sis. Principal component analysis is used to project the high- dimensional feature space into a low-dimensional space fol- lowed by a linear discriminant analysis to search the optimal separation among different device health conditions. Our in- dustrial experimental results show that by combining device operating context with sensor data, our proposed segmenta- tion and linear transformation approach can accurately iden- tify various device imbalance conditions even for limited sen- sor data which could not be used to diagnose imbalance on its own.

For device health prediction, we propose a restricted Boltz- mann machine based method to automatically generate fea- tures that can be used for remaining useful life prediction, which is performed by a random forest regression algorithm. The proposed method was validated through run-to-failure dataset of a machine tool spindle test-bed.
Lessons Learned in Implementing a Practical Aircraft System Health Management (ASHM) Syste
Authors: Koelle Joshua , Smith Matthew , Sulcs Peter , Kacprzynski Greg , Walthall Onda
Published: March 2024

Aircraft System Health Management (ASHM) is a web application used for Boeing 787® and Airbus A320® and A380® aircraft system monitoring by airlines and field engineers worldwide which also serves all existing Aircraft Condition Monitoring Function (ACMF) reports, Flight Deck Effects (FDE) records and aircraft metadata to UTC engineering teams to aid in efficient aftermarket support. This enables creation, testing and fielding of off-board diagnostics and prognostics modules of varying levels of sophistication, that convert this abundance of existing data into actionable and timely knowledge about a/c fleet health. ASHM encourages and promotes cross functional collaboration allowing those with the most subject matter expertise within the enterprise to access the field data they need to observe operational performance and to create, test and field modules that can actively diagnose and warn field service professionals of problems when and potentially before they arise. A practical case study related to monitoring of the novel Boeing 787® electromechanically driven distributed aircraft environmental systems is presented. This use case motivates a discussion of pragmatic lessons learned in the fielding of diagnostic and prognostics solutions.
Cost-benefit Analysis of Prognostics and Condition-based Maintenance Concepts for Commercial Aircraft Considering Prognostic Errors
Authors: B. Hölzel Nico , Gollnick Volker
Published: March 2024

This paper provides a lifecycle cost-benefit analysis of the use of Prognostics and Health Management (PHM) systems in future or present commercial aircraft. The approach considers individual aircraft component’s failure behavior, prognostic performance levels including prognostic errors, and condition-based maintenance (CBM) concepts. The proposed methodology is based on a discrete-event simulation for aircraft operation and maintenance and uses an optimization algorithm for the planning and scheduling of condition-based maintenance (CBM) tasks. In the study, a 150-seat short-/medium-range aircraft equipped with PHM and subject to a CBM program is analyzed. The simulation results are evaluated from an operational and economic perspective. The analysis results can support the derivation of technical and economic requirements for prognostic systems and CBM planning concepts.
Active Mission Success Estimation through PHM-Informed Probabilistic Modelling
Authors: R. Short Adam , L. Van Bossuyt Douglas
Published: March 2024

Prognostics and Health Management (PHM) techniques have traditionally been used to analyze electrical and mechanical systems, but similar techniques can be adapted for less mechatronically-focused processes such as crewed space missions. By applying failure analysis techniques taken from PHM, the probability of success for missions can be calculated. Extensive work has been conducted to predict space mission failure, but many existing methods do not take full advantage of modern computing power and the potential for real-time calculation of mission failure probabilities. The Active Mission Success Estimation (AMSE) method is developed in this paper to track and calculate the probability of mission failure as the mission progresses, and is intentionally adaptable for shifting mission objectives and parameters. This form of mission modelling takes a broader view of the mission and objectives, and develops statistical probability models of success or failure for multiple possible choice combinations that is used to inform real-time decisions and maximize probability of mission success. A case study of a generalized crewed Mars mission that has turned into a survival scenario is considered where an astronaut has been left behind on the surface and must survive for an extended period of time before undertaking a long-distance journey to a new launch site for rescue and return to Earth. The AMSE method presented here aims to establish real-time probabilistic modeling of decision outcomes during an active mission and can be used to inform mission decisions.
A New Scheme for Monitoring and Diagnosis of Multistage Manufacturing Processes Using Product Quality Measurements
Authors: Davari Ardakani Hossein , Phillips Brian , Bagheri Behrad , Lee Jay
Published: March 2024

The development of robust monitoring systems for assuring the consistency and stability of multistage manufacturing processes necessitates the use of add-on sensors and advanced data collection, storage, and analysis platforms to deal with the high-dimensional data collected from machines and products in multiple stages. In many cases, such an approach may not be feasible due to high implementation costs and the challenges of obtaining the process parameters and analyzing them effectively. This paper proposes an alternative approach for health monitoring and diagnosis of multistage manufacturing processes based on product quality measurements in a sensor-less environment. In the presented work, the available data consists of product quality parameters measured from multiple product types along with the manufacturing route associated with each product. A Gamma distribution is fit to the data for each parameter within a moving time window. Using the distribution fits, a metric is developed to represent the performance of each machine in a stage compared to its peers producing the same product. This metric is then aggregated across all the products produced by the machine to generate the final metric reflecting the overall performance of the machine. This performance metric is first calculated for the machines in the last stage. After flagging the underperforming machines in the last stage, the samples from those machines are removed from the data set and the remaining samples are used to calculate the similar metric for the prior stage. The suggested approach assumes the random distribution of products from one stage to the next to facilitate the implementation of a comparison-based approach. This approach is tested on a data set collected from a manufacturing plant. The results demonstrate the effectiveness of such approach for monitoring and diagnosis of multistage manufacturing processes when the data is not available from within the process.
A New Generic Approach to Convert FMEA in Causal Trees for the Purpose of Hydro-Generator Rotor Failure Mechanisms Identification
Authors: Blancke Olivier , Amyot Normand , Hudon Claude , Lévesque Mélanie , Tahan Antoine
Published: March 2024

At Hydro-Québec (HQ), an integrated diagnostic system (MIDA) is currently used to assess hydro-generators health index. This system gives the global health index but does not propose any understanding of active failure mechanisms. At this point, this work needs to be done by experts after analysis of the diagnostic data in MIDA.

To relieve the expert from part of this work, a prognostic tool, that uses a Failure Mechanisms and Symptoms Analysis (FMSA), is under development. The approach is based on the understanding of the evolution of degradation processes for each failure mechanism. Failure mechanisms are structured as causal trees and defined as a sequence of physical states starting from a root cause and ending with a failure mode. A physical state corresponds to characteristic degradation condition of a component of the generator. Each physical state being defined by a unique combination of symptoms as measured with diagnostic tools. After consigning all possible mechanisms occurring in both the rotor and the stator, the symptoms logged into a database can be read to automatically identify all active physical state and active failure mechanisms. This approach has been under development in HQ for the stator for a number of years and is now extended to the rotors of hydro-generators.The purpose of this paper is to present the structured method used to build the failure mechanisms from bits and pieces of information (sub-mechanisms) found in the literature and from discussions with experts. This new methodology is based on a two steps process. First, sub-mechanisms were extracted from FMEA in the literature. Then, an algorithm was used to generate a set of causal trees from these sub- mechanisms. The generated results then had to be validated by experts to make sure that automatically generated mechanisms were logical and plausible. The resulting extended failure mechanisms trees built can then be used for the purpose of Root Cause Analysis (RCA), model-based diagnostics and prognosis. This method was developed to be as generic as possible so it could be applied to any complex system.
Characterization of Phase Space Topology Using Density: Application to Fault Diagnostics
Authors: Samadani Mohsen , Kwuimy Cedrick A. Kitio , Nataraj C.
Published: March 2024

Almost all engineered systems are nonlinear and show nonlinear phenomena that can only be predicted by nonlinear models. However, the application of model-based approaches for diagnostics has been constrained mostly to linearized or simplified models. This paper introduces a fundamental approach for characterization of nonlinear response of systems based on the topology of the phase space trajectory. The method uses the density distribution of the system states to quantify this topology and extracts features that can be used for system diagnostics. The proposed method has been employed to diagnose a multi degree of freedom system with various simultaneous defects.
Estimating Remaining Useful Life Using Actuarial Methods
Authors: Bechhoefer Eric , Schlanbusch Rune , Inge Waag Tor
Published: March 2024

In many instances, condition monitoring equipment has not been installed on machinery. Yet, operators still need guidance as to when to perform maintenance that is better than what is offered by the equipment manufacturers. For these systems, running hours, counts, or some other measure of usage may be available. This data, along with failure rate data, can provide an expected time to failure, and the estimated remaining useful life. The failure rate (even small sample size) is used to estimate the shape and scale parameters for the Weibull distribution. Then the conditional expectation of the truncated survival function of the Weibull is used to estimate the time to failure. This is an actuarial technique to solve the conditional survival function problem of: given that the equipment has survived to time x, what is that probability of the equipment surviving to time x + y. The inverse cumulative distribution of the truncated survival function can then be used to estimate the remaining useful life, that is: a time when the conditional likelihood of failure is small, such as 10%. The 90% confidence of the shape and scale parameters is then used to give a bound on the remaining useful life. This method is then tested on a real world bearing dataset.
Advances in Ultrasonic Imaging for Internal Flaws in Structures
Authors: Vu Nguyen Thompson , Sternini Simone , Lanza di Scalea Francesco
Published: March 2024

In the field of non-destructive testing of structures, 3D imag- ing of internal flaws is a critical task. Defect imaging allows the engineer to make informed follow-up decisions based on the morphology of the flaw.

This paper will present advances in ultrasonic tomography for the 3D visualization of internal flaws in solids. In particular, improvements to the conventional tomographic imaging algorithms have been made by utilizing a mode-selective im- age reconstruction scheme that exploits the specific displacement field, respectively, of the longitudinal wave modes and the shear wave modes, both propagating simultaneously in the test volume. The specific mode structure is exploited by an adaptive weight assignment to the ultrasonic tomographic array. Such adaptive weighting forces the imaging array to look at a specific scan direction and better focus the imaging onto the actual flaw (ultrasound reflector).

This study shows that the adaptive weighing based on mode structure improves image contrast and resolution compared to a conventional ultrasonic imaging technique based on delay- and-sum. Results will be shown from simulations and imaging experiments of simulated flaws in an aluminum block.